Datasets:
librarian-bots
/
model_cards_with_metadata_test

Dataset card Data Studio Files
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mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF
mradermacher
"2024-06-14T01:41:27Z"
5,266
1
transformers
[ "transformers", "gguf", "mergekit", "merge", "not-for-all-audiences", "en", "base_model:invisietch/EtherealRainbow-v0.2-8B", "license:llama3", "endpoints_compatible", "region:us" ]
null
"2024-06-13T22:47:43Z"
--- base_model: invisietch/EtherealRainbow-v0.2-8B language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - mergekit - merge - not-for-all-audiences --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> weighted/imatrix quants of https://huggingface.co/invisietch/EtherealRainbow-v0.2-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/EtherealRainbow-v0.2-8B-i1-GGUF/resolve/main/EtherealRainbow-v0.2-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Autolycus-Mistral_7B-i1-GGUF
mradermacher
"2024-06-11T14:32:48Z"
5,265
0
transformers
[ "transformers", "gguf", "mistral", "instruct", "finetune", "chatml", "gpt4", "en", "base_model:FPHam/Autolycus-Mistral_7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-11T13:22:00Z"
--- base_model: FPHam/Autolycus-Mistral_7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mistral - instruct - finetune - chatml - gpt4 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/FPHam/Autolycus-Mistral_7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Autolycus-Mistral_7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Autolycus-Mistral_7B-i1-GGUF/resolve/main/Autolycus-Mistral_7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
Yntec/RetroLife
Yntec
"2024-03-09T09:15:42Z"
5,262
4
diffusers
[ "diffusers", "safetensors", "Photorealistic", "Retro", "Base model", "Abstract", "Elldreths", "Fusch", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2024-03-05T13:51:24Z"
--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Photorealistic - Retro - Base model - Abstract - Elldreths - Fusch - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # Retro Life A mix of Elldreth's Retro Mix and Real Life 2.0. The old version hosted here has been renamed to RetroLifeAlpha, the new one improves the anatomy. Original pages: https://huggingface.co/Yntec/RealLife https://huggingface.co/Yntec/ElldrethsRetroMix Samples and prompts: ![Free AI image generator Retrolife](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/LX86N046E-l2eDlidwepE.png) (Click for larger) Top left: Stock washed out worn Retro colors TV movie TRAILER. Closeup Santa Claus and daughters enjoying enchiladas with tacos. sitting with a pretty cute little girl, Art Christmas Theme by Gil_Elvgren and Haddon_Sundblom. Posing Top right: Retropunk painting of a rainbow fantasy phoenix by Bnhr, fire eyes, nature, grass, tree, outdoors, forest, animal focus, blue eyes Bottom left: vintage colors photo of view from diagonally above, Heidi Bloom, central adjustment, skinny young northern european female, long reddish ponytail hair, real hair movement, elongated head, beautiful face, grey eyes, thin bowed eyebrows, snub nose, gentle lower jaw line, narrow chin, da vinci lips, slightly smiling with parted lips, curious friendly facial expression, small, slim narrow tapered hips Bottom right: 1977 kodachrome camera transparency, dramatic lighting film grain, PARTY HARD BACKGROUND, pretty cute little girl in Zone 51, Extraterrestrial, Alien Space Ship Delivering Christmas Presents, Alien Space Ship Decorated With Garlands and Christmas Balls, Snowstorm # Recipes: - SuperMerger Weight sum MBW 0,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0 Model A: Real Life 2.0 Model B: ElldrethsRetroMix Output: RetroLifeAlpha - SuperMerger Weight sum MBW 0,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,1 Model A: Real Life 2.0 Model B: ElldrethsRetroMix Output: RetroLife
Remek/Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT-GGUF
Remek
"2024-05-12T13:10:22Z"
5,257
3
null
[ "gguf", "text-generation", "pl", "en", "region:us" ]
text-generation
"2024-04-22T20:25:02Z"
--- language: - pl - en pipeline_tag: text-generation --- # Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT-GGUF To repozytorum zawiera konwersję modeli Llama-3-8B-Omnibus-1-PL-v01-INSTRUCT do formatu GGUF - Q8_0 oraz Q4_K_M. Przetestowana została w dwóch środowiskach uruchomieniowych: #### LM Studio Wersja minimum 0.2.20 - koniecznie wybierz format promptu Llama 3 (!) (opcja Preset) #### Ollama Wersja 0.1.32. Konfiguracja ollama plik Modelfile. Uwaga! Nie zmieniaj SYSTEM mimo, że chcesz rozmawiać w języku polskim. Pozostaw treść pola systemowego po angielsku tak jak jest. ``` FROM ./Llama-3-Omnibus-PL-v01-GGUF.Q4_K_M.gguf TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> {{ .Response }}<|eot_id|>""" SYSTEM """You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.""" PARAMETER num_ctx 8192 PARAMETER num_gpu 99 ``` Repozytorium zawiera model Meta Llama-3-8B-Omnibus-1-PL-v01 w wersji polskojęzycznej. Model postał na podstawie finetuningu modelu bazowego Llama-3-8B. Wykorzystano do tego dataset instrukcji Omnibus-1-PL (stworzyłem go na własne potrzeby przeprowadzania eksperymenów finetuningu modeli w języku polskim). Szczegóły parametrów treningu w sekcji Trening. Celem tego eksperymentu było sprawdzenie czy można namówić Llama-3-8B do płynnego rozmawiania w języku polskim (oryginalny model instrukcyjny 8B ma z tym problem - woli zdecydowanie bardziej rozmawiać po angielsku). <img src="Llama-3-8B-PL-small.jpg" width="420" /> Uwaga! * Model NIE jest CENZUROWANY. To wersja do zabawy. Nie została ujarzmiona. * Model będzie dalej rozwijany ponieważ eksperymentuję z a. kolejnymi wersjami datasetu, b. model jest świetną bazą do testowania różnych technik finetunowania (LoRA, QLoRA; DPO, ORPO itd.) * Udostępniłem go spontanicznie by użytkownicy mogli go używać i sprawdzać jakość Llama 3 ale w kontekście języka polskiego. * Po informacji, że baza była trenowana na 15T tokenów (tylko 5% nie angielskich) uznałem, że to świetny model do finetuningu. Być może lekkie dotrenowanie modelu za pomocą contingued-pretraining da jeszcze większy uzysk. ### Sposób kodowania nazwy modelu * Nazwa modelu bazowego: Llama-3-8B * Nazwa datasetu: Omnibus-1 * Wersja językowa: PL (polska) * Wersja modelu: v01 ### Dataset Omnibus-1 to zbiór polskich instrukcji (100% kontekstu Polskiego - fakty, osoby, miejsca osadzone w Polsce), który został w 100% syntetycznie wygenerowany. Zawiera on instrukcje z kategorii - matematyka, umiejętność pisania, dialogi, tematy medyczne, zagadki logiczne, tłumaczenia itd. Powstał on w ramach moich prac związanych z badaniem jakości modeli w kontekście języka polskiego. Pozwala on na finetuning modelu i sprawdzenie podatności modelu do mówienia w naszym rodzimym języku. Dataset zawiera obecnie 75.000 instrukcji. Będzie cały czas udoskonalony i być może w przyszłości udostępniony (jak uznam, że już jest wtstarczająco pełen i obejmuje szerokie spektrum tematyki i umiejętności). Dataset jest w 100% generowany za pomocą innych LLM (GPT3.5, GPT4, Mixtral itd.) ### Szablon konwersacji Szablon konwersacji to oryginalna wersja Llama3 ``` <|start_header_id|>You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.<|end_header_id|> {System} <|eot_id|> <|start_header_id|>user<|end_header_id|> {User} <|eot_id|><|start_header_id|>assistant<|end_header_id|> {Assistant} ``` ### Trening Poniżej szczegóły hiperparametrów treningu: * learning_rate: 2e-05 * train_batch_size: 8 * eval_batch_size: 8 * seed: 42 * distributed_type: single-GPU (Nvidia A6000 Ada) * num_devices: 1 * gradient_accumulation_steps: 4 * optimizer: adamw_8bit * lr_scheduler_type: linear * lr_scheduler_warmup_steps: 5 * num_epochs: 1 * QLoRa - 4bit: rank 64, alpha 128 #### Unsloth <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200px" align="center" /> [Unsloth](https://unsloth.ai), narzędzie dzięki któremu powstał ten model. ### Licencja Licencja na zasadzie nie do komercyjnego użycia (ze względu na dataset - generowany syntetycznie za pomocą modeli GPT4, GPT3.5) oraz licencja Llama3 (proszę o zapoznanie się ze szczegółami licencji).
lcw99/zephykor-ko-beta-7b-chang
lcw99
"2023-12-25T01:17:13Z"
5,252
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "ko", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-11-27T00:46:19Z"
--- language: - ko - en --- * Under construction, be carefull.
RLHFlow/pair-preference-model-LLaMA3-8B
RLHFlow
"2024-05-24T07:05:10Z"
5,250
26
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2405.07863", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-04-23T04:51:16Z"
--- license: llama3 --- This preference model is trained from [LLaMA3-8B-it](meta-llama/Meta-Llama-3-8B-Instruct) with the training script at [Reward Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/pm_dev/pair-pm). The dataset is RLHFlow/pair_preference_model_dataset. It achieves Chat-98.6, Char-hard 65.8, Safety 89.6, and reasoning 94.9 in reward bench. See our paper [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/abs/2405.07863) for more details of this model. ## Service the RM Here is an example to use the Preference Model to rank a pair. For n>2 responses, it is recommened to use the tournament style ranking strategy to get the best response so that the complexity is linear in n. ```python device = 0 model = AutoModelForCausalLM.from_pretrained(script_args.preference_name_or_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").cuda() tokenizer = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain.chat_template = "\n{% for message in messages %}{% if loop.index0 % 2 == 0 %}\n\n<turn> user\n {{ message['content'] }}{% else %}\n\n<turn> assistant\n {{ message['content'] }}{% endif %}{% endfor %}\n\n\n" prompt_template = "[CONTEXT] {context} [RESPONSE A] {response_A} [RESPONSE B] {response_B} \n" token_id_A = tokenizer.encode("A", add_special_tokens=False) token_id_B = tokenizer.encode("B", add_special_tokens=False) assert len(token_id_A) == 1 and len(token_id_B) == 1 token_id_A = token_id_A[0] token_id_B = token_id_B[0] temperature = 1.0 model.eval() response_chosen = "BBBB" response_rejected = "CCCC" ## We can also handle multi-turn conversation. instruction = [{"role": "user", "content": ...}, {"role": "assistant", "content": ...}, {"role": "user", "content": ...}, ] context = tokenizer_plain.apply_chat_template(instruction, tokenize=False) responses = [response_chosen, response_rejected] probs_chosen = [] for chosen_position in [0, 1]: # we swap order to mitigate position bias response_A = responses[chosen_position] response_B = responses[1 - chosen_position] prompt = prompt_template.format(context=context, response_A=response_A, response_B=response_B) message = [ {"role": "user", "content": prompt}, ] input_ids = tokenizer.encode(tokenizer.apply_chat_template(message, tokenize=False).replace(tokenizer.bos_token, ""), return_tensors='pt', add_special_tokens=False).cuda() with torch.no_grad(): output = model(input_ids) logit_A = output.logits[0, -1, token_id_A].item() logit_B = output.logits[0, -1, token_id_B].item() # take softmax to get the probability; using numpy Z = np.exp(logit_A / temperature) + np.exp(logit_B / temperature) logit_chosen = [logit_A, logit_B][chosen_position] prob_chosen = np.exp(logit_chosen / temperature) / Z probs_chosen.append(prob_chosen) avg_prob_chosen = np.mean(probs_chosen) correct = 0.5 if avg_prob_chosen == 0.5 else float(avg_prob_chosen > 0.5) print(correct) ``` ## Citation If you use this model in your research, please consider citing our paper ``` @misc{rlhflow, title={RLHF Workflow: From Reward Modeling to Online RLHF}, author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang}, year={2024}, eprint={2405.07863}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` and Google's Slic paper (which initially proposes this pairwise preference model) ``` @article{zhao2023slic, title={Slic-hf: Sequence likelihood calibration with human feedback}, author={Zhao, Yao and Joshi, Rishabh and Liu, Tianqi and Khalman, Misha and Saleh, Mohammad and Liu, Peter J}, journal={arXiv preprint arXiv:2305.10425}, year={2023} } ```
01-ai/Yi-1.5-34B-32K
01-ai
"2024-06-26T10:42:31Z"
5,246
33
transformers
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2403.04652", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-05-15T10:42:51Z"
--- license: apache-2.0 --- <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://01-ai.github.io/">💪 Tech Blog</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T </div> # Models - Chat models <div align="center"> | Name | Download | | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI)| | Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | | Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | | Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | | Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | </div> - Base models <div align="center"> | Name | Download | | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | | Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | | Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | | Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | | Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) | </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
mradermacher/Winterreise-m7-i1-GGUF
mradermacher
"2024-06-05T08:43:46Z"
5,246
0
transformers
[ "transformers", "gguf", "en", "dataset:LDJnr/Capybara", "dataset:chargoddard/rpguild", "dataset:PocketDoc/Guanaco-Unchained-Refined", "dataset:lemonilia/LimaRP", "base_model:Sao10K/Winterreise-m7", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
null
"2024-06-04T13:07:32Z"
--- base_model: Sao10K/Winterreise-m7 datasets: - LDJnr/Capybara - chargoddard/rpguild - PocketDoc/Guanaco-Unchained-Refined - lemonilia/LimaRP language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Sao10K/Winterreise-m7 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Winterreise-m7-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Winterreise-m7-i1-GGUF/resolve/main/Winterreise-m7.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit
Muennighoff
"2023-03-27T22:19:34Z"
5,241
2
sentence-transformers
[ "sentence-transformers", "pytorch", "gpt_neo", "feature-extraction", "sentence-similarity", "mteb", "arxiv:2202.08904", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
"2022-03-02T23:29:04Z"
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: SGPT-125M-weightedmean-msmarco-specb-bitfit results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 61.23880597014926 - type: ap value: 25.854431650388644 - type: f1 value: 55.751862762818604 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 56.88436830835117 - type: ap value: 72.67279104379772 - type: f1 value: 54.449840243786404 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 58.27586206896551 - type: ap value: 14.067357642500387 - type: f1 value: 48.172318518691334 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 54.64668094218415 - type: ap value: 11.776694555054965 - type: f1 value: 44.526622834078765 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 65.401225 - type: ap value: 60.22809958678552 - type: f1 value: 65.0251824898292 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 31.165999999999993 - type: f1 value: 30.908870050167437 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 24.79 - type: f1 value: 24.5833598854121 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 26.643999999999995 - type: f1 value: 26.39012792213563 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 26.386000000000003 - type: f1 value: 26.276867791454873 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 22.078000000000003 - type: f1 value: 21.797960290226843 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 24.274 - type: f1 value: 23.887054434822627 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 22.404 - type: map_at_10 value: 36.845 - type: map_at_100 value: 37.945 - type: map_at_1000 value: 37.966 - type: map_at_3 value: 31.78 - type: map_at_5 value: 34.608 - type: mrr_at_1 value: 22.902 - type: mrr_at_10 value: 37.034 - type: mrr_at_100 value: 38.134 - type: mrr_at_1000 value: 38.155 - type: mrr_at_3 value: 31.935000000000002 - type: mrr_at_5 value: 34.812 - type: ndcg_at_1 value: 22.404 - type: ndcg_at_10 value: 45.425 - type: ndcg_at_100 value: 50.354 - type: ndcg_at_1000 value: 50.873999999999995 - type: ndcg_at_3 value: 34.97 - type: ndcg_at_5 value: 40.081 - type: precision_at_1 value: 22.404 - type: precision_at_10 value: 7.303999999999999 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.746 - type: precision_at_5 value: 11.337 - type: recall_at_1 value: 22.404 - type: recall_at_10 value: 73.044 - type: recall_at_100 value: 95.092 - type: recall_at_1000 value: 99.075 - type: recall_at_3 value: 44.239 - type: recall_at_5 value: 56.686 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 39.70858340673288 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 28.242847713721048 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 55.83700395192393 - type: mrr value: 70.3891307215407 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 79.25366801756223 - type: cos_sim_spearman value: 75.20954502580506 - type: euclidean_pearson value: 78.79900722991617 - type: euclidean_spearman value: 77.79996549607588 - type: manhattan_pearson value: 78.18408109480399 - type: manhattan_spearman value: 76.85958262303106 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 77.70454545454545 - type: f1 value: 77.6929000113803 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 33.63260395543984 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 27.038042665369925 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 22.139 - type: map_at_10 value: 28.839 - type: map_at_100 value: 30.023 - type: map_at_1000 value: 30.153000000000002 - type: map_at_3 value: 26.521 - type: map_at_5 value: 27.775 - type: mrr_at_1 value: 26.466 - type: mrr_at_10 value: 33.495000000000005 - type: mrr_at_100 value: 34.416999999999994 - type: mrr_at_1000 value: 34.485 - type: mrr_at_3 value: 31.402 - type: mrr_at_5 value: 32.496 - type: ndcg_at_1 value: 26.466 - type: ndcg_at_10 value: 33.372 - type: ndcg_at_100 value: 38.7 - type: ndcg_at_1000 value: 41.696 - type: ndcg_at_3 value: 29.443 - type: ndcg_at_5 value: 31.121 - type: precision_at_1 value: 26.466 - type: precision_at_10 value: 6.037 - type: precision_at_100 value: 1.0670000000000002 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 13.782 - type: precision_at_5 value: 9.757 - type: recall_at_1 value: 22.139 - type: recall_at_10 value: 42.39 - type: recall_at_100 value: 65.427 - type: recall_at_1000 value: 86.04899999999999 - type: recall_at_3 value: 31.127 - type: recall_at_5 value: 35.717999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 20.652 - type: map_at_10 value: 27.558 - type: map_at_100 value: 28.473 - type: map_at_1000 value: 28.577 - type: map_at_3 value: 25.402 - type: map_at_5 value: 26.68 - type: mrr_at_1 value: 25.223000000000003 - type: mrr_at_10 value: 31.966 - type: mrr_at_100 value: 32.664 - type: mrr_at_1000 value: 32.724 - type: mrr_at_3 value: 30.074 - type: mrr_at_5 value: 31.249 - type: ndcg_at_1 value: 25.223000000000003 - type: ndcg_at_10 value: 31.694 - type: ndcg_at_100 value: 35.662 - type: ndcg_at_1000 value: 38.092 - type: ndcg_at_3 value: 28.294000000000004 - type: ndcg_at_5 value: 30.049 - type: precision_at_1 value: 25.223000000000003 - type: precision_at_10 value: 5.777 - type: precision_at_100 value: 0.9730000000000001 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 13.397 - type: precision_at_5 value: 9.605 - type: recall_at_1 value: 20.652 - type: recall_at_10 value: 39.367999999999995 - type: recall_at_100 value: 56.485 - type: recall_at_1000 value: 73.292 - type: recall_at_3 value: 29.830000000000002 - type: recall_at_5 value: 34.43 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 25.180000000000003 - type: map_at_10 value: 34.579 - type: map_at_100 value: 35.589999999999996 - type: map_at_1000 value: 35.68 - type: map_at_3 value: 31.735999999999997 - type: map_at_5 value: 33.479 - type: mrr_at_1 value: 29.467 - type: mrr_at_10 value: 37.967 - type: mrr_at_100 value: 38.800000000000004 - type: mrr_at_1000 value: 38.858 - type: mrr_at_3 value: 35.465 - type: mrr_at_5 value: 37.057 - type: ndcg_at_1 value: 29.467 - type: ndcg_at_10 value: 39.796 - type: ndcg_at_100 value: 44.531 - type: ndcg_at_1000 value: 46.666000000000004 - type: ndcg_at_3 value: 34.676 - type: ndcg_at_5 value: 37.468 - type: precision_at_1 value: 29.467 - type: precision_at_10 value: 6.601999999999999 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 15.568999999999999 - type: precision_at_5 value: 11.172 - type: recall_at_1 value: 25.180000000000003 - type: recall_at_10 value: 52.269 - type: recall_at_100 value: 73.574 - type: recall_at_1000 value: 89.141 - type: recall_at_3 value: 38.522 - type: recall_at_5 value: 45.323 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.303 - type: map_at_10 value: 21.629 - type: map_at_100 value: 22.387999999999998 - type: map_at_1000 value: 22.489 - type: map_at_3 value: 19.608 - type: map_at_5 value: 20.774 - type: mrr_at_1 value: 17.740000000000002 - type: mrr_at_10 value: 23.214000000000002 - type: mrr_at_100 value: 23.97 - type: mrr_at_1000 value: 24.054000000000002 - type: mrr_at_3 value: 21.243000000000002 - type: mrr_at_5 value: 22.322 - type: ndcg_at_1 value: 17.740000000000002 - type: ndcg_at_10 value: 25.113000000000003 - type: ndcg_at_100 value: 29.287999999999997 - type: ndcg_at_1000 value: 32.204 - type: ndcg_at_3 value: 21.111 - type: ndcg_at_5 value: 23.061999999999998 - type: precision_at_1 value: 17.740000000000002 - type: precision_at_10 value: 3.955 - type: precision_at_100 value: 0.644 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 8.851 - type: precision_at_5 value: 6.418 - type: recall_at_1 value: 16.303 - type: recall_at_10 value: 34.487 - type: recall_at_100 value: 54.413999999999994 - type: recall_at_1000 value: 77.158 - type: recall_at_3 value: 23.733 - type: recall_at_5 value: 28.381 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 10.133000000000001 - type: map_at_10 value: 15.665999999999999 - type: map_at_100 value: 16.592000000000002 - type: map_at_1000 value: 16.733999999999998 - type: map_at_3 value: 13.625000000000002 - type: map_at_5 value: 14.721 - type: mrr_at_1 value: 12.562000000000001 - type: mrr_at_10 value: 18.487000000000002 - type: mrr_at_100 value: 19.391 - type: mrr_at_1000 value: 19.487 - type: mrr_at_3 value: 16.418 - type: mrr_at_5 value: 17.599999999999998 - type: ndcg_at_1 value: 12.562000000000001 - type: ndcg_at_10 value: 19.43 - type: ndcg_at_100 value: 24.546 - type: ndcg_at_1000 value: 28.193 - type: ndcg_at_3 value: 15.509999999999998 - type: ndcg_at_5 value: 17.322000000000003 - type: precision_at_1 value: 12.562000000000001 - type: precision_at_10 value: 3.794 - type: precision_at_100 value: 0.74 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 7.546 - type: precision_at_5 value: 5.721 - type: recall_at_1 value: 10.133000000000001 - type: recall_at_10 value: 28.261999999999997 - type: recall_at_100 value: 51.742999999999995 - type: recall_at_1000 value: 78.075 - type: recall_at_3 value: 17.634 - type: recall_at_5 value: 22.128999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 19.991999999999997 - type: map_at_10 value: 27.346999999999998 - type: map_at_100 value: 28.582 - type: map_at_1000 value: 28.716 - type: map_at_3 value: 24.907 - type: map_at_5 value: 26.1 - type: mrr_at_1 value: 23.773 - type: mrr_at_10 value: 31.647 - type: mrr_at_100 value: 32.639 - type: mrr_at_1000 value: 32.706 - type: mrr_at_3 value: 29.195 - type: mrr_at_5 value: 30.484 - type: ndcg_at_1 value: 23.773 - type: ndcg_at_10 value: 32.322 - type: ndcg_at_100 value: 37.996 - type: ndcg_at_1000 value: 40.819 - type: ndcg_at_3 value: 27.876 - type: ndcg_at_5 value: 29.664 - type: precision_at_1 value: 23.773 - type: precision_at_10 value: 5.976999999999999 - type: precision_at_100 value: 1.055 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 13.122 - type: precision_at_5 value: 9.451 - type: recall_at_1 value: 19.991999999999997 - type: recall_at_10 value: 43.106 - type: recall_at_100 value: 67.264 - type: recall_at_1000 value: 86.386 - type: recall_at_3 value: 30.392000000000003 - type: recall_at_5 value: 34.910999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 17.896 - type: map_at_10 value: 24.644 - type: map_at_100 value: 25.790000000000003 - type: map_at_1000 value: 25.913999999999998 - type: map_at_3 value: 22.694 - type: map_at_5 value: 23.69 - type: mrr_at_1 value: 21.346999999999998 - type: mrr_at_10 value: 28.594 - type: mrr_at_100 value: 29.543999999999997 - type: mrr_at_1000 value: 29.621 - type: mrr_at_3 value: 26.807 - type: mrr_at_5 value: 27.669 - type: ndcg_at_1 value: 21.346999999999998 - type: ndcg_at_10 value: 28.833 - type: ndcg_at_100 value: 34.272000000000006 - type: ndcg_at_1000 value: 37.355 - type: ndcg_at_3 value: 25.373 - type: ndcg_at_5 value: 26.756 - type: precision_at_1 value: 21.346999999999998 - type: precision_at_10 value: 5.2170000000000005 - type: precision_at_100 value: 0.954 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.948 - type: precision_at_5 value: 8.425 - type: recall_at_1 value: 17.896 - type: recall_at_10 value: 37.291000000000004 - type: recall_at_100 value: 61.138000000000005 - type: recall_at_1000 value: 83.212 - type: recall_at_3 value: 27.705999999999996 - type: recall_at_5 value: 31.234 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 17.195166666666665 - type: map_at_10 value: 23.329083333333333 - type: map_at_100 value: 24.30308333333333 - type: map_at_1000 value: 24.422416666666667 - type: map_at_3 value: 21.327416666666664 - type: map_at_5 value: 22.419999999999998 - type: mrr_at_1 value: 19.999916666666667 - type: mrr_at_10 value: 26.390166666666666 - type: mrr_at_100 value: 27.230999999999998 - type: mrr_at_1000 value: 27.308333333333334 - type: mrr_at_3 value: 24.4675 - type: mrr_at_5 value: 25.541083333333336 - type: ndcg_at_1 value: 19.999916666666667 - type: ndcg_at_10 value: 27.248666666666665 - type: ndcg_at_100 value: 32.00258333333334 - type: ndcg_at_1000 value: 34.9465 - type: ndcg_at_3 value: 23.58566666666667 - type: ndcg_at_5 value: 25.26341666666666 - type: precision_at_1 value: 19.999916666666667 - type: precision_at_10 value: 4.772166666666666 - type: precision_at_100 value: 0.847 - type: precision_at_1000 value: 0.12741666666666668 - type: precision_at_3 value: 10.756166666666669 - type: precision_at_5 value: 7.725416666666667 - type: recall_at_1 value: 17.195166666666665 - type: recall_at_10 value: 35.99083333333334 - type: recall_at_100 value: 57.467999999999996 - type: recall_at_1000 value: 78.82366666666667 - type: recall_at_3 value: 25.898499999999995 - type: recall_at_5 value: 30.084333333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.779 - type: map_at_10 value: 21.557000000000002 - type: map_at_100 value: 22.338 - type: map_at_1000 value: 22.421 - type: map_at_3 value: 19.939 - type: map_at_5 value: 20.903 - type: mrr_at_1 value: 18.404999999999998 - type: mrr_at_10 value: 23.435 - type: mrr_at_100 value: 24.179000000000002 - type: mrr_at_1000 value: 24.25 - type: mrr_at_3 value: 21.907 - type: mrr_at_5 value: 22.781000000000002 - type: ndcg_at_1 value: 18.404999999999998 - type: ndcg_at_10 value: 24.515 - type: ndcg_at_100 value: 28.721000000000004 - type: ndcg_at_1000 value: 31.259999999999998 - type: ndcg_at_3 value: 21.508 - type: ndcg_at_5 value: 23.01 - type: precision_at_1 value: 18.404999999999998 - type: precision_at_10 value: 3.834 - type: precision_at_100 value: 0.641 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 9.151 - type: precision_at_5 value: 6.503 - type: recall_at_1 value: 16.779 - type: recall_at_10 value: 31.730000000000004 - type: recall_at_100 value: 51.673 - type: recall_at_1000 value: 71.17599999999999 - type: recall_at_3 value: 23.518 - type: recall_at_5 value: 27.230999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 9.279 - type: map_at_10 value: 13.822000000000001 - type: map_at_100 value: 14.533 - type: map_at_1000 value: 14.649999999999999 - type: map_at_3 value: 12.396 - type: map_at_5 value: 13.214 - type: mrr_at_1 value: 11.149000000000001 - type: mrr_at_10 value: 16.139 - type: mrr_at_100 value: 16.872 - type: mrr_at_1000 value: 16.964000000000002 - type: mrr_at_3 value: 14.613000000000001 - type: mrr_at_5 value: 15.486 - type: ndcg_at_1 value: 11.149000000000001 - type: ndcg_at_10 value: 16.82 - type: ndcg_at_100 value: 20.73 - type: ndcg_at_1000 value: 23.894000000000002 - type: ndcg_at_3 value: 14.11 - type: ndcg_at_5 value: 15.404000000000002 - type: precision_at_1 value: 11.149000000000001 - type: precision_at_10 value: 3.063 - type: precision_at_100 value: 0.587 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 6.699 - type: precision_at_5 value: 4.928 - type: recall_at_1 value: 9.279 - type: recall_at_10 value: 23.745 - type: recall_at_100 value: 41.873 - type: recall_at_1000 value: 64.982 - type: recall_at_3 value: 16.152 - type: recall_at_5 value: 19.409000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.36 - type: map_at_10 value: 21.927 - type: map_at_100 value: 22.889 - type: map_at_1000 value: 22.994 - type: map_at_3 value: 20.433 - type: map_at_5 value: 21.337 - type: mrr_at_1 value: 18.75 - type: mrr_at_10 value: 24.859 - type: mrr_at_100 value: 25.746999999999996 - type: mrr_at_1000 value: 25.829 - type: mrr_at_3 value: 23.383000000000003 - type: mrr_at_5 value: 24.297 - type: ndcg_at_1 value: 18.75 - type: ndcg_at_10 value: 25.372 - type: ndcg_at_100 value: 30.342999999999996 - type: ndcg_at_1000 value: 33.286 - type: ndcg_at_3 value: 22.627 - type: ndcg_at_5 value: 24.04 - type: precision_at_1 value: 18.75 - type: precision_at_10 value: 4.1419999999999995 - type: precision_at_100 value: 0.738 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 10.261000000000001 - type: precision_at_5 value: 7.164 - type: recall_at_1 value: 16.36 - type: recall_at_10 value: 32.949 - type: recall_at_100 value: 55.552 - type: recall_at_1000 value: 77.09899999999999 - type: recall_at_3 value: 25.538 - type: recall_at_5 value: 29.008 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 17.39 - type: map_at_10 value: 23.058 - type: map_at_100 value: 24.445 - type: map_at_1000 value: 24.637999999999998 - type: map_at_3 value: 21.037 - type: map_at_5 value: 21.966 - type: mrr_at_1 value: 19.96 - type: mrr_at_10 value: 26.301000000000002 - type: mrr_at_100 value: 27.297 - type: mrr_at_1000 value: 27.375 - type: mrr_at_3 value: 24.340999999999998 - type: mrr_at_5 value: 25.339 - type: ndcg_at_1 value: 19.96 - type: ndcg_at_10 value: 27.249000000000002 - type: ndcg_at_100 value: 32.997 - type: ndcg_at_1000 value: 36.359 - type: ndcg_at_3 value: 23.519000000000002 - type: ndcg_at_5 value: 24.915000000000003 - type: precision_at_1 value: 19.96 - type: precision_at_10 value: 5.356000000000001 - type: precision_at_100 value: 1.198 - type: precision_at_1000 value: 0.20400000000000001 - type: precision_at_3 value: 10.738 - type: precision_at_5 value: 7.904999999999999 - type: recall_at_1 value: 17.39 - type: recall_at_10 value: 35.254999999999995 - type: recall_at_100 value: 61.351 - type: recall_at_1000 value: 84.395 - type: recall_at_3 value: 25.194 - type: recall_at_5 value: 28.546 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 14.238999999999999 - type: map_at_10 value: 19.323 - type: map_at_100 value: 19.994 - type: map_at_1000 value: 20.102999999999998 - type: map_at_3 value: 17.631 - 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type: max_f1 value: 73.64688856729379 --- # SGPT-125M-weightedmean-msmarco-specb-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 15600 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0002 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
redponike/Prox-Llama-3-8B-GGUF
redponike
"2024-06-21T06:28:03Z"
5,240
0
null
[ "gguf", "region:us" ]
null
"2024-06-20T15:09:51Z"
GGUF quants of [openvoid/Prox-Llama-3-8B](https://huggingface.co/openvoid/Prox-Llama-3-8B)
sbintuitions/tiny-lm
sbintuitions
"2024-06-27T09:47:28Z"
5,235
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "ja", "en", "dataset:wikipedia", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-06-07T13:12:16Z"
--- license: mit datasets: - wikipedia language: - ja - en --- # tiny-lm This repository provides a tiny 16M parameters language model for debugging and testing purposes. Trained on English and Japanese Wikipedia data. ## How to use ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm", use_fast=False) generator = pipeline("text-generation", model=model, tokenizer=tokenizer) print(generator("Hello", max_length=30, do_sample=True, top_k=100)) ``` ## Model architecture A 4-layer, 512-hidden-size transformer-based language model. ## Training The model was trained on English Wikipedia and Japanese Wikipedia to optimize a traditional language modelling objective for 25B tokens. ## License [MIT License](https://huggingface.co/sbintuitions/tiny-lm/resolve/main/LICENSE)
MBZUAI/LaMini-Flan-T5-248M
MBZUAI
"2023-04-28T12:08:23Z"
5,234
62
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "instruction fine-tuning", "en", "arxiv:2304.14402", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
"2023-04-10T17:37:18Z"
--- license: cc-by-nc-4.0 tags: - generated_from_trainer - instruction fine-tuning model-index: - name: flan-t5-small-distil-v2 results: [] language: - en pipeline_tag: text2text-generation widget: - text: >- how can I become more healthy? example_title: example --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> # LaMini-Flan-T5-248M [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]() This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/). You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. <table> <thead> <tr> <th>Base model</th> <th colspan="4">LaMini-LM series (#parameters)</th> </tr> </thead> <tbody> <tr> <td>T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td> <td></td> </tr> <tr> <td>Flan-T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td> <td></td> </tr> <tr> <td>Cerebras-GPT</td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td> </tr> <tr> <td>GPT-2</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td> <td></td> </tr> <tr> <td>GPT-Neo</td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td> <td></td> <td></td> </tr> <tr> <td>GPT-J</td> <td colspan="4">coming soon</td> </tr> <tr> <td>LLaMA</td> <td colspan="4">coming soon</td> </tr> </tbody> </table> ## Use ### Intended use We recommend using the model to response to human instructions written in natural language. We now show you how to load and use our model using HuggingFace `pipeline()`. ```python # pip install -q transformers from transformers import pipeline checkpoint = "{model_name}" model = pipeline('text2text-generation', model = checkpoint) input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"' generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text'] print("Response", generated_text) ``` ## Training Procedure <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a> </p> We initialize with [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 248M. ### Training Hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ## Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). ## Limitations More information needed # Citation ```bibtex @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```
mradermacher/Dr.Samantha-8B-i1-GGUF
mradermacher
"2024-06-05T08:45:24Z"
5,234
0
transformers
[ "transformers", "gguf", "mergekit", "merge", "medical", "en", "dataset:cognitivecomputations/samantha-data", "dataset:ruslanmv/ai-medical-dataset", "base_model:sethuiyer/Dr.Samantha-8B", "license:other", "endpoints_compatible", "region:us" ]
null
"2024-06-03T12:16:06Z"
--- base_model: sethuiyer/Dr.Samantha-8B datasets: - cognitivecomputations/samantha-data - ruslanmv/ai-medical-dataset language: - en library_name: transformers license: other quantized_by: mradermacher tags: - mergekit - merge - medical --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/sethuiyer/Dr.Samantha-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Dr.Samantha-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Dr.Samantha-8B-i1-GGUF/resolve/main/Dr.Samantha-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mradermacher/MythoMist-7b-i1-GGUF
mradermacher
"2024-06-06T21:48:38Z"
5,233
0
transformers
[ "transformers", "gguf", "en", "base_model:Gryphe/MythoMist-7b", "license:other", "endpoints_compatible", "region:us" ]
null
"2024-06-06T01:31:50Z"
--- base_model: Gryphe/MythoMist-7b language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Gryphe/MythoMist-7b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MythoMist-7b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF/resolve/main/MythoMist-7b.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF
mradermacher
"2024-06-09T20:04:04Z"
5,226
0
transformers
[ "transformers", "gguf", "bangla", "large language model", "bn", "en", "dataset:wikimedia/wikipedia", "base_model:BanglaLLM/BanglaLLama-3-8b-BnWiki-Base", "license:llama3", "endpoints_compatible", "region:us" ]
null
"2024-06-09T17:22:39Z"
--- base_model: BanglaLLM/BanglaLLama-3-8b-BnWiki-Base datasets: - wikimedia/wikipedia language: - bn - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - bangla - large language model --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-BnWiki-Base <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/BanglaLLama-3-8b-BnWiki-Base-i1-GGUF/resolve/main/BanglaLLama-3-8b-BnWiki-Base.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mradermacher/AmberChat-i1-GGUF
mradermacher
"2024-06-18T00:48:32Z"
5,223
0
transformers
[ "transformers", "gguf", "nlp", "llm", "en", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:icybee/share_gpt_90k_v1", "base_model:LLM360/AmberChat", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-17T23:38:46Z"
--- base_model: LLM360/AmberChat datasets: - WizardLM/WizardLM_evol_instruct_V2_196k - icybee/share_gpt_90k_v1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - nlp - llm --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/LLM360/AmberChat <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AmberChat-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ1_S.gguf) | i1-IQ1_S | 1.6 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ3_S.gguf) | i1-IQ3_S | 3.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ3_M.gguf) | i1-IQ3_M | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.7 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q4_0.gguf) | i1-Q4_0 | 3.9 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/AmberChat-i1-GGUF/resolve/main/AmberChat.i1-Q6_K.gguf) | i1-Q6_K | 5.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
olm/olm-roberta-base-dec-2022
olm
"2023-01-20T14:32:41Z"
5,222
7
transformers
[ "transformers", "pytorch", "tf", "tensorboard", "roberta", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
"2022-12-27T22:14:15Z"
--- language: en --- # OLM RoBERTa/BERT December 2022 This is a more up-to-date version of the [original BERT](https://huggingface.co/bert-base-cased) and [original RoBERTa](https://huggingface.co/roberta-base). In addition to being more up-to-date, it also tends to perform better than the original BERT on standard benchmarks. We think it is fair to directly compare our model to the original BERT because our model was trained with about the same level of compute as the original BERT, and the architecture of BERT and RoBERTa are basically the same. The original RoBERTa takes an order of magnitude more compute, although our model is also not that different in performance from the original RoBERTa on many standard benchmarks. Our model was trained on a cleaned December 2022 snapshot of Common Crawl and Wikipedia. This model was created as part of the OLM project, which has the goal of continuously training and releasing models that are up-to-date and comparable in standard language model performance to their static counterparts. This is important because we want our models to know about events like COVID or a presidential election right after they happen. ## Intended uses You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task, such as sequence classification, token classification or question answering. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='olm/olm-roberta-base-dec-2022') >>> unmasker("Hello I'm a <mask> model.") [{'score': 0.04252663999795914, 'token': 631, 'token_str': ' new', 'sequence': "Hello I'm a new model."}, {'score': 0.034064881503582, 'token': 4750, 'token_str': ' female', 'sequence': "Hello I'm a female model."}, {'score': 0.03066524863243103, 'token': 932, 'token_str': ' business', 'sequence': "Hello I'm a business model."}, {'score': 0.029599128291010857, 'token': 10345, 'token_str': ' junior', 'sequence': "Hello I'm a junior model."}, {'score': 0.025790784507989883, 'token': 2219, 'token_str': ' human', 'sequence': "Hello I'm a human model."}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, RobertaModel tokenizer = AutoTokenizer.from_pretrained('olm/olm-roberta-base-dec-2022') model = RobertaModel.from_pretrained("olm/olm-roberta-base-dec-2022") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Dataset The model and tokenizer were trained with this [December 2022 cleaned Common Crawl dataset](https://huggingface.co/datasets/olm/olm-CC-MAIN-2022-49-sampling-ratio-olm-0.15114822547) plus this [December 2022 cleaned Wikipedia dataset](https://huggingface.co/datasets/olm/olm-wikipedia-20221220).\ The tokenized version of these concatenated datasets is [here](https://huggingface.co/datasets/olm/olm-december-2022-tokenized-512).\ The datasets were created with this [repo](https://github.com/huggingface/olm-datasets). ## Training The model was trained according to the OLM BERT/RoBERTa instructions at this [repo](https://github.com/huggingface/olm-training). ## Evaluation results The model achieves the following results after tuning on GLUE tasks: | Task | Metric | Original BERT | OLM RoBERTa Dec 2022 (Ours) | |:-----|:---------|----------------:|----------------------------:| |cola |mcc |**0.5889** |0.28067 | |sst2 |acc |0.9181 |**0.9275** | |mrpc |acc/f1 |**0.9182**/0.8923|0.8662/**0.9033** | |stsb |pear/spear|0.8822/0.8794 |**0.8870**/**0.8857** | |qqp |acc/f1 |0.9071/0.8748 |**0.9097**/**0.8791** | |mnli |acc/acc_mm|0.8400/0.8410 |**0.8576**/**0.8621** | |qnli |acc |0.9075 |**0.9192** | |rte |acc |0.6296 |**0.6390** | |wnli |acc |0.4000 |**0.4648** | For both the original BERT and our model, we used the Hugging Face run_glue.py script [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification). For both models, we used the default fine-tuning hyperparameters and we averaged the results over five training seeds. These are the results for the GLUE dev sets, which can be a bit different than the results for the test sets.
h2oai/h2ogpt-oasst1-512-12b
h2oai
"2023-06-02T22:36:27Z"
5,222
27
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "gpt", "llm", "large language model", "open-source", "en", "dataset:h2oai/openassistant_oasst1_h2ogpt_graded", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-04-17T20:33:51Z"
--- license: apache-2.0 language: - en library_name: transformers inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico tags: - gpt - llm - large language model - open-source datasets: - h2oai/openassistant_oasst1_h2ogpt_graded --- # h2oGPT Model Card ## Summary H2O.ai's `h2ogpt-oasst1-512-12b` is a 12 billion parameter instruction-following large language model licensed for commercial use. - Base model: [EleutherAI/pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) - Fine-tuning dataset: [h2oai/openassistant_oasst1_h2ogpt_graded](https://huggingface.co/datasets/h2oai/openassistant_oasst1_h2ogpt_graded) - Data-prep and fine-tuning code: [H2O.ai GitHub](https://github.com/h2oai/h2ogpt) - Training logs: [zip](https://huggingface.co/h2oai/h2ogpt-oasst1-512-12b/blob/main/pythia-12b-deduped.h2oaiopenassistant_oasst1_h2ogpt_graded.3_epochs.2ccf687ea3f3f3775a501838e81c1a0066430455.4.zip) ## Chatbot - Run your own chatbot: [H2O.ai GitHub](https://github.com/h2oai/h2ogpt) [![H2O.ai GitHub](https://user-images.githubusercontent.com/6147661/232930822-e7170e4d-8aa1-4f7a-ad70-ece9cdd8b0cb.png)](https://github.com/h2oai/h2ogpt) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. ```bash pip install transformers==4.28.1 pip install accelerate==0.18.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline(model="h2oai/h2ogpt-oasst1-512-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", prompt_type='human_bot') res = generate_text("Why is drinking water so healthy?", max_new_tokens=100) print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/h2oai/h2ogpt-oasst1-512-12b/blob/main/h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-oasst1-512-12b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-oasst1-512-12b", torch_dtype=torch.bfloat16, device_map="auto") generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer, prompt_type='human_bot') res = generate_text("Why is drinking water so healthy?", max_new_tokens=100) print(res[0]["generated_text"]) ``` ## Model Architecture ``` GPTNeoXForCausalLM( (gpt_neox): GPTNeoXModel( (embed_in): Embedding(50688, 5120) (layers): ModuleList( (0-35): 36 x GPTNeoXLayer( (input_layernorm): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) (post_attention_layernorm): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) (attention): GPTNeoXAttention( (rotary_emb): RotaryEmbedding() (query_key_value): Linear(in_features=5120, out_features=15360, bias=True) (dense): Linear(in_features=5120, out_features=5120, bias=True) ) (mlp): GPTNeoXMLP( (dense_h_to_4h): Linear(in_features=5120, out_features=20480, bias=True) (dense_4h_to_h): Linear(in_features=20480, out_features=5120, bias=True) (act): GELUActivation() ) ) ) (final_layer_norm): LayerNorm((5120,), eps=1e-05, elementwise_affine=True) ) (embed_out): Linear(in_features=5120, out_features=50688, bias=False) ) ``` ## Model Configuration ```json GPTNeoXConfig { "_name_or_path": "h2oai/h2ogpt-oasst1-512-12b", "architectures": [ "GPTNeoXForCausalLM" ], "bos_token_id": 0, "classifier_dropout": 0.1, "custom_pipelines": { "text-generation": { "impl": "h2oai_pipeline.H2OTextGenerationPipeline", "pt": "AutoModelForCausalLM" } }, "eos_token_id": 0, "hidden_act": "gelu", "hidden_size": 5120, "initializer_range": 0.02, "intermediate_size": 20480, "layer_norm_eps": 1e-05, "max_position_embeddings": 2048, "model_type": "gpt_neox", "num_attention_heads": 40, "num_hidden_layers": 36, "rotary_emb_base": 10000, "rotary_pct": 0.25, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.30.0.dev0", "use_cache": true, "use_parallel_residual": true, "vocab_size": 50688 } ``` ## Model Validation Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). [eval source code](https://github.com/h2oai/h2ogpt/issues/125#issuecomment-1548239108) | Task |Version| Metric |Value | |Stderr| |-------------|------:|--------|-----:|---|-----:| |arc_challenge| 0|acc |0.3157|± |0.0136| | | |acc_norm|0.3507|± |0.0139| |arc_easy | 0|acc |0.6932|± |0.0095| | | |acc_norm|0.6225|± |0.0099| |boolq | 1|acc |0.6685|± |0.0082| |hellaswag | 0|acc |0.5140|± |0.0050| | | |acc_norm|0.6803|± |0.0047| |openbookqa | 0|acc |0.2900|± |0.0203| | | |acc_norm|0.3740|± |0.0217| |piqa | 0|acc |0.7682|± |0.0098| | | |acc_norm|0.7661|± |0.0099| |winogrande | 0|acc |0.6369|± |0.0135| ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
cross-encoder/quora-roberta-large
cross-encoder
"2021-08-05T08:41:41Z"
5,220
3
transformers
[ "transformers", "pytorch", "jax", "roberta", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2022-03-02T23:29:05Z"
--- license: apache-2.0 --- # Cross-Encoder for Quora Duplicate Questions Detection This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. ## Training Data This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates. Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates. ## Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')]) ``` You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
google/ddpm-celebahq-256
google
"2022-07-21T15:00:31Z"
5,220
37
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
"2022-07-19T10:42:22Z"
--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-celebahq-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm()["sample"] # save image image[0].save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/images/generated_image_3.png)
mradermacher/LlamaGramma-7b-i1-GGUF
mradermacher
"2024-06-10T13:36:10Z"
5,219
0
transformers
[ "transformers", "gguf", "en", "dataset:Gryphe/CoEdit-Alpaca", "base_model:Gryphe/LlamaGramma-7b", "license:other", "endpoints_compatible", "region:us" ]
null
"2024-06-10T03:52:53Z"
--- base_model: Gryphe/LlamaGramma-7b datasets: - Gryphe/CoEdit-Alpaca language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Gryphe/LlamaGramma-7b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/LlamaGramma-7b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ1_S.gguf) | i1-IQ1_S | 1.6 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ3_S.gguf) | i1-IQ3_S | 3.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ3_M.gguf) | i1-IQ3_M | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.7 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q4_0.gguf) | i1-Q4_0 | 3.9 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/LlamaGramma-7b-i1-GGUF/resolve/main/LlamaGramma-7b.i1-Q6_K.gguf) | i1-Q6_K | 5.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
Mathoufle13/reverse_maker_llama8_4bit
Mathoufle13
"2024-07-01T15:07:54Z"
5,219
0
transformers
[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-07-01T14:51:40Z"
--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** Mathoufle13 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Qwen/Qwen2-72B-Instruct-GGUF
Qwen
"2024-06-17T16:49:09Z"
5,217
9
null
[ "gguf", "instruct", "chat", "text-generation", "en", "license:other", "region:us" ]
text-generation
"2024-06-06T10:54:52Z"
--- language: - en pipeline_tag: text-generation tags: - instruct - chat license: other --- # Qwen2-72B-Instruct-GGUF ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). In this repo, we provide quantized models in the GGUF formats, including `q5_0`, `q5_k_m`, `q6_k` and `q8_0`. ## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Training details We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. ## Requirements We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide. We follow the latest version of llama.cpp. In the following demonstration, we assume that you are running commands under the repository `llama.cpp`. ## How to use Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use `huggingface-cli` (`pip install huggingface_hub`) as shown below: ```shell huggingface-cli download Qwen/Qwen2-72B-Instruct-GGUF qwen2-72b-instruct-q4_0.gguf --local-dir . --local-dir-use-symlinks False ``` However, for large files, we split them into multiple segments due to the limitation of 50G for a single file to be uploaded. Specifically, for the split files, they share a prefix, with a suffix indicating its index. For examples, the `q5_k_m` GGUF files are: ``` qwen2-72b-instruct-q5_k_m-00001-of-00002.gguf qwen2-72b-instruct-q5_k_m-00002-of-00002.gguf ``` They share the prefix of `qwen2-72b-instruct-q5_k_m`, but have their own suffix for indexing respectively, say `-00001-of-00002`. To use the split GGUF files, you need to merge them first with the command `llama-gguf-split` as shown below: ```bash ./llama-gguf-split --merge qwen2-72b-instruct-q5_k_m-00001-of-00002.gguf qwen2-72b-instruct-q5_k_m.gguf ``` With the upgrade of APIs of llama.cpp, `llama-gguf-split` is equivalent to the previous `gguf-split`. For the arguments of this command, the first is the path to the first split GGUF file, and the second is the path to the output GGUF file. To run Qwen2, you can use `llama-cli` (the previous `main`) or `llama-server` (the previous `server`). We recommend using the `llama-server` as it is simple and compatible with OpenAI API. For example: ```bash ./llama-server -m qwen2-72b-instruct-q4_0.gguf -ngl 80 -fa ``` (Note: `-ngl 80` refers to offloading 80 layers to GPUs, and `-fa` refers to the use of flash attention.) Then it is easy to access the deployed service with OpenAI API: ```python import openai client = openai.OpenAI( base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port" api_key = "sk-no-key-required" ) completion = client.chat.completions.create( model="qwen", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "tell me something about michael jordan"} ] ) print(completion.choices[0].message.content) ``` If you choose to use `llama-cli`, pay attention to the removal of `-cml` for the ChatML template. Instead you should use `--in-prefix` and `--in-suffix` to tackle this problem. ```bash ./llama-cli -m qwen2-72b-instruct-q4_0.gguf \ -n 512 -co -i -if -f prompts/chat-with-qwen.txt \ --in-prefix "<|im_start|>user\n" \ --in-suffix "<|im_end|>\n<|im_start|>assistant\n" \ -ngl 80 -fa ``` ## Evaluation We implement perplexity evaluation using wikitext following the practice of `llama.cpp` with `./llama-perplexity` (the previous `./perplexity`). In the following we report the PPL of GGUF models of different sizes and different quantization levels. |Size | fp16 | q8_0 | q6_k | q5_k_m | q5_0 | q4_k_m | q4_0 | q3_k_m | q2_k | iq1_m | |--------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------| |0.5B | 15.11 | 15.13 | 15.14 | 15.24 | 15.40 | 15.36 | 16.28 | 15.70 | 16.74 | - | |1.5B | 10.43 | 10.43 | 10.45 | 10.50 | 10.56 | 10.61 | 10.79 | 11.08 | 13.04 | - | |7B | 7.93 | 7.94 | 7.96 | 7.97 | 7.98 | 8.02 | 8.19 | 8.20 | 10.58 | - | |57B-A14B| 6.81 | 6.81 | 6.83 | 6.84 | 6.89 | 6.99 | 7.02 | 7.43 | - | - | |72B | 5.58 | 5.58 | 5.59 | 5.59 | 5.60 | 5.61 | 5.66 | 5.68 | 5.91 | 6.75 | ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```
RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf
RichardErkhov
"2024-06-30T05:21:50Z"
5,216
0
null
[ "gguf", "region:us" ]
null
"2024-06-30T04:51:45Z"
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Fox-1-1.6B-Instruct-v0.1 - GGUF - Model creator: https://huggingface.co/tensoropera/ - Original model: https://huggingface.co/tensoropera/Fox-1-1.6B-Instruct-v0.1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Fox-1-1.6B-Instruct-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q2_K.gguf) | Q2_K | 0.8GB | | [Fox-1-1.6B-Instruct-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.IQ3_XS.gguf) | IQ3_XS | 0.84GB | | [Fox-1-1.6B-Instruct-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.IQ3_S.gguf) | IQ3_S | 0.87GB | | [Fox-1-1.6B-Instruct-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.86GB | | [Fox-1-1.6B-Instruct-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.IQ3_M.gguf) | IQ3_M | 0.89GB | | [Fox-1-1.6B-Instruct-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q3_K.gguf) | Q3_K | 0.92GB | | [Fox-1-1.6B-Instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.92GB | | [Fox-1-1.6B-Instruct-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.97GB | | [Fox-1-1.6B-Instruct-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 0.98GB | | [Fox-1-1.6B-Instruct-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q4_0.gguf) | Q4_0 | 1.0GB | | [Fox-1-1.6B-Instruct-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.IQ4_NL.gguf) | IQ4_NL | 1.01GB | | [Fox-1-1.6B-Instruct-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 1.01GB | | [Fox-1-1.6B-Instruct-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q4_K.gguf) | Q4_K | 1.04GB | | [Fox-1-1.6B-Instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 1.04GB | | [Fox-1-1.6B-Instruct-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q4_1.gguf) | Q4_1 | 1.07GB | | [Fox-1-1.6B-Instruct-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q5_0.gguf) | Q5_0 | 1.14GB | | [Fox-1-1.6B-Instruct-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 1.14GB | | [Fox-1-1.6B-Instruct-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q5_K.gguf) | Q5_K | 1.16GB | | [Fox-1-1.6B-Instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 1.16GB | | [Fox-1-1.6B-Instruct-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q5_1.gguf) | Q5_1 | 1.2GB | | [Fox-1-1.6B-Instruct-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q6_K.gguf) | Q6_K | 1.28GB | | [Fox-1-1.6B-Instruct-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/tensoropera_-_Fox-1-1.6B-Instruct-v0.1-gguf/blob/main/Fox-1-1.6B-Instruct-v0.1.Q8_0.gguf) | Q8_0 | 1.65GB | Original model description: --- license: apache-2.0 language: - en --- ## Model Card for Fox-1-1.6B-Instruct > [!IMPORTANT] > This model is an instruction tuned model which requires alignment before it can be used in production. We will release > the chat version soon. Fox-1 is a decoder-only transformer-based small language model (SLM) with 1.6B total parameters developed by [TensorOpera AI](https://tensoropera.ai/). The model was pre-trained with a 3-stage data curriculum on 3 trillion tokens of text and code data in 8K sequence length. Fox-1 uses Grouped Query Attention (GQA) with 4 key-value heads and 16 attention heads for faster inference. Fox-1-Instruct-v0.1 is an instruction-tuned (SFT) version of Fox-1-1.6B that has an 8K native context length. The model was finetuned with 5B tokens of instruction following and multi-turn conversation data. For the full details of this model please read our [release blog post](https://blog.tensoropera.ai/tensoropera-unveils-fox-foundation-model-a-pioneering-open-source-slm-leading-the-way-against-tech-giants). ## Getting-Started The model and a live inference endpoint are available on the [TensorOpera AI Platform](https://tensoropera.ai/models/1228?owner=tensoropera). For detailed deployment instructions, refer to the [Step-by-Step Guide](https://blog.tensoropera.ai/how-to/how-to-deploy-fox-1-on-tensoropera-ai-a-step-by-step-guide-2/) on how to deploy Fox-1-Instruct on the [TensorOpera AI Platform](https://tensoropera.ai/). ## Benchmarks We evaluated Fox-1 on ARC Challenge (25-shot), HellaSwag (10-shot), TruthfulQA (0-shot), MMLU (5-shot), Winogrande (5-shot), and GSM8k (5-shot). We follow the Open LLM Leaderboard's evaluation setup and report the average score of the 6 benchmarks. The model was evaluated on a machine with 8*H100 GPUs. | | Fox-1-1.6B-Instruct-v0.1 | Fox-1-1.6B | Qwen1.5-1.8B-Chat | Gemma-2B-It | OpenELM-1.1B-Instruct | |---------------|--------------------------|------------|-------------------|-------------|-----------------------| | GSM8k | 39.20% | 36.39% | 18.20% | 4.47% | 0.91% | | MMLU | 44.99% | 43.05% | 45.77% | 37.70% | 25.70% | | ARC Challenge | 43.60% | 41.21% | 38.99% | 43.34% | 40.36% | | HellaSwag | 63.39% | 62.82% | 60.31% | 62.72% | 71.67% | | TruthfulQA | 44.12% | 38.66% | 40.57% | 45.86% | 45.96% | | Winogrande | 62.67% | 60.62% | 59.51% | 61.33% | 61.96% | | Average | 49.66% | 47.13% | 43.89% | 42.57% | 41.09% |
unionai/Phi-3-mini-128k-instruct-news-headlines-gguf
unionai
"2024-06-11T19:01:46Z"
5,214
0
null
[ "gguf", "pytorch", "causal-lm", "llama2", "code llama", "fine-tuning", "flyte llama", "flyte repo dataset", "en", "license:apache-2.0", "region:us" ]
null
"2024-06-03T17:34:47Z"
--- language: - en license: apache-2.0 tags: - pytorch - causal-lm - llama2 - code llama - fine-tuning - flyte llama - flyte repo dataset --- # Phi-3-mini-128k-instruct fine-tuned on news headlines
Himitsui/Kaiju-11B-GGUF
Himitsui
"2024-02-13T12:55:47Z"
5,208
14
null
[ "gguf", "region:us" ]
null
"2024-02-13T12:26:49Z"
Included in this repo is the GGUF Quants for Kaiju-11B (ﾉ≧∀≦)ﾉ ‥…━━━━━━━━━━━━━★ ||| ╲/\╭[ ᴼᴼ ౪ ᴼᴼ]╮/\╱\ Hiya! This is an experiment using Gryphe's [MergeMonster](https://github.com/Gryphe/MergeMonster). I decided to try and reduce what the community calls 'GPT-isms' or GPT Slop, Solar is a good model but does have fair share of positivity bias and 'slop' in roleplays. I used my friend [Sao](https://huggingface.co/Sao10K)'s models as bases as they are pretty popular, along with Kuromitsu and the popular Instruct-Uncensored tune. Alpaca Format should be fine as it is universal, Vicuna Format should work too. Universal-Light preset in SillyTavern is pretty nice too. :) 💜 I hope this model may be useful to you 💜 *** Merge Details Below: <details><summary>See Merge Config</summary> ``` ----------------------------------------------------------------------------------------------------- | Type | Phrase | Context | Raw Prob* | Used Prob** | Change | ----------------------------------------------------------------------------------------------------- | BAD | anticipation | Her body quivers with | 9.99850% | 119.98% | -54.02% | | BAD | anticipation | The atmosphere is thic.. | 8.82392% | 105.89% | -32.13% | | BAD | unwavering | Filled with an | 0.09003% | 1.08% | -0.06% | | BAD | determination | Her eyes were filled w.. | 0.19863% | 2.38% | -0.26% | | BAD | determination | Her stubbornness only .. | 7.17110% | 86.05% | -39.86% | | BAD | whisper | Her voice barely above.. | 96.55492% | 1158.66% | -8.91% | | BAD | spine | shivers down her | 85.57597% | 1026.91% | -66.19% | | BAD | sends shivers | The thrill of the act | 0.00230% | 0.03% | -0.00% | | BAD | ministrations | She moans and twitches.. | 1.35264% | 16.23% | -10.49% | | BAD | legs | wraps her | 2.45741% | 29.49% | -10.58% | | BAD | imposing figure | He had an | 0.00356% | 0.04% | +0.00% | | BAD | shared challenges | Their bond strengthene.. | 0.10075% | 1.21% | -0.03% | | BAD | bond | forged a | 1.78930% | 21.47% | -9.07% | | BAD | bond | an unspoken | 4.33001% | 51.96% | -28.17% | | BAD | enhance our expe.. | I'm excited to see how | 0.00000% | 0.00% | +0.00% | | BAD | sense of vulnera.. | create a | 0.00003% | 0.00% | -0.00% | | BAD | dimensions of in.. | explore new | 0.00047% | 0.01% | -0.00% | | BAD | deepening our co.. | while | 0.00003% | 0.00% | -0.00% | | BAD | shared experiences | through | 0.00469% | 0.06% | -0.00% | | BAD | societal expecta.. | that transcend | 0.00170% | 0.02% | -0.00% | | BAD | conventional bou.. | that defy | 0.03593% | 0.43% | +0.04% | | BAD | conventional bou.. | and defy | 0.00410% | 0.05% | +0.01% | | BAD | open communication | an environment | 0.00000% | 0.00% | +0.00% | | BAD | emotional vulner.. | an environment | 0.00000% | 0.00% | +0.00% | | BAD | heightens our co.. | touch and the anticipa.. | 0.00000% | 0.00% | +0.00% | | BAD | sensations you'r.. | I'm enjoying | 0.00000% | 0.00% | -0.00% | | BAD | is truly arousing | attention to detail | 0.00000% | 0.00% | +0.00% | | BAD | is truly arousing | way you explore my body | 0.00001% | 0.00% | +0.00% | | BAD | challenge presen.. | my resolve unwavering .. | 0.00000% | 0.00% | +0.00% | | BAD | humble vessel | surrendering to the ex.. | 0.00000% | 0.00% | +0.00% | | BAD | bond | cherishing the unique | 1.37498% | 16.50% | +1.21% | | BAD | bond | special | 0.05834% | 0.70% | +0.01% | | BAD | grows stronger w.. | bond | 0.00000% | 0.00% | +0.00% | | BAD | that cannot be b.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | becomes unbreaka.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | grew stronger wi.. | bond | 0.00000% | 0.00% | +0.00% | | GOOD | The apple is in .. | Question: If I'm in th.. | 78.38934% | 78.39% | -10.79% | ------------------------------------------------------------------------------------------------------ | Totals | 298.32% | 2717.54% | -269.30% | ------------------------------------------------------------------------------------------------------ ``` * = Unweighted, raw probability - ** = Probability after weight adjustments ``` -------- MERGE COMPOSITION --------- Fimbulvetr-11B-v2-Test-14: 0.50 KuroMitsu-11B: 0.18 Fimbulvetr-10.7B-v1: 0.17 SOLAR-10.7B-Instruct-v1.0-uncensored: 0.10 Solstice-11B-v1: 0.05 ``` </details><br>
Habana/vit
Habana
"2023-07-25T21:36:05Z"
5,205
0
null
[ "optimum_habana", "license:apache-2.0", "region:us" ]
null
"2022-08-05T22:23:55Z"
--- license: apache-2.0 --- [Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). ## ViT model HPU configuration This model only contains the `GaudiConfig` file for running the [ViT](https://huggingface.co/google/vit-base-patch16-224-in21k) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_fused_adam`: whether to use Habana's custom AdamW implementation - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator - `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision ## Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.\ It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with ViT with the following command: ```bash python run_image_classification.py \ --model_name_or_path google/vit-base-patch16-224-in21k \ --dataset_name cifar10 \ --output_dir /tmp/outputs/ \ --remove_unused_columns False \ --do_train \ --do_eval \ --learning_rate 2e-5 \ --num_train_epochs 5 \ --per_device_train_batch_size 64 \ --per_device_eval_batch_size 64 \ --evaluation_strategy epoch \ --save_strategy epoch \ --load_best_model_at_end True \ --save_total_limit 3 \ --seed 1337 \ --use_habana \ --use_lazy_mode \ --gaudi_config_name Habana/vit \ --throughput_warmup_steps 3 \ --bf16 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
Qwen/Qwen1.5-110B
Qwen
"2024-04-26T14:55:00Z"
5,201
86
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "pretrained", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-04-25T07:30:56Z"
--- license: other license_name: tongyi-qianwen license_link: >- https://huggingface.co/Qwen/Qwen1.5-110B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained --- # Qwen1.5-110B ## Introduction Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: * 9 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B dense models, and an MoE model of 14B with 2.7B activated; * Significant performance improvement in Chat models; * Multilingual support of both base and chat models; * Stable support of 32K context length for models of all sizes * No need of `trust_remote_code`. For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). ## Model Details Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B and 110B) and the mixture of SWA and full attention. ## Requirements The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2'. ``` ## Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen, title={Qwen Technical Report}, author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, journal={arXiv preprint arXiv:2309.16609}, year={2023} } ```
RichardErkhov/Fizzarolli_-_sappha-2b-v3-gguf
RichardErkhov
"2024-06-27T13:19:13Z"
5,194
0
null
[ "gguf", "region:us" ]
null
"2024-06-27T12:59:06Z"
Entry not found
flaviagiammarino/medsam-vit-base
flaviagiammarino
"2023-07-13T15:43:56Z"
5,190
6
transformers
[ "transformers", "pytorch", "tf", "sam", "mask-generation", "medical", "vision", "arxiv:2304.12306", "license:apache-2.0", "endpoints_compatible", "region:us" ]
mask-generation
"2023-07-11T07:37:57Z"
--- license: apache-2.0 tags: - medical - vision --- # Model Card for MedSAM MedSAM is a fine-tuned version of [SAM](https://huggingface.co/docs/transformers/main/model_doc/sam) for the medical domain. This repository is based on the paper, code and pre-trained model released by the authors in July 2023. ## Model Description MedSAM was trained on a large-scale medical image segmentation dataset of 1,090,486 image-mask pairs collected from different publicly available sources. The image-mask pairs cover 15 imaging modalities and over 30 cancer types. MedSAM was initialized using the pre-trained SAM model with the ViT-Base backbone. The prompt encoder weights were frozen, while the image encoder and mask decoder weights were updated during training. The training was performed for 100 epochs with a batch size of 160 using the AdamW optimizer with a learning rate of 10−4 and a weight decay of 0.01. - **Repository:** [MedSAM Official GitHub Repository](https://github.com/bowang-lab/medsam) - **Paper:** [Segment Anything in Medical Images](https://arxiv.org/abs/2304.12306v1) ## Usage ```python import requests import numpy as np import matplotlib.pyplot as plt from PIL import Image from transformers import SamModel, SamProcessor import torch device = "cuda" if torch.cuda.is_available() else "cpu" model = SamModel.from_pretrained("flaviagiammarino/medsam-vit-base").to(device) processor = SamProcessor.from_pretrained("flaviagiammarino/medsam-vit-base") img_url = "https://huggingface.co/flaviagiammarino/medsam-vit-base/resolve/main/scripts/input.png" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") input_boxes = [95., 255., 190., 350.] inputs = processor(raw_image, input_boxes=[[input_boxes]], return_tensors="pt").to(device) outputs = model(**inputs, multimask_output=False) probs = processor.image_processor.post_process_masks(outputs.pred_masks.sigmoid().cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu(), binarize=False) def show_mask(mask, ax, random_color): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([251/255, 252/255, 30/255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) def show_box(box, ax): x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="blue", facecolor=(0, 0, 0, 0), lw=2)) fig, ax = plt.subplots(1, 2, figsize=(10, 5)) ax[0].imshow(np.array(raw_image)) show_box(input_boxes, ax[0]) ax[0].set_title("Input Image and Bounding Box") ax[0].axis("off") ax[1].imshow(np.array(raw_image)) show_mask(mask=probs[0] > 0.5, ax=ax[1], random_color=False) show_box(input_boxes, ax[1]) ax[1].set_title("MedSAM Segmentation") ax[1].axis("off") plt.show() ``` ![results](scripts/output.png) ## Additional Information ### Licensing Information The authors have released the model code and pre-trained checkpoint under the [Apache License 2.0](https://github.com/bowang-lab/MedSAM/blob/main/LICENSE). ### Citation Information ``` @article{ma2023segment, title={Segment anything in medical images}, author={Ma, Jun and Wang, Bo}, journal={arXiv preprint arXiv:2304.12306}, year={2023} } ```
Jiayi-Pan/Tiny-Vicuna-1B
Jiayi-Pan
"2024-04-26T20:00:14Z"
5,187
13
transformers
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-11-22T21:11:51Z"
--- language: - en license: apache-2.0 model-index: - name: Tiny-Vicuna-1B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 33.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Jiayi-Pan/Tiny-Vicuna-1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 55.92 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Jiayi-Pan/Tiny-Vicuna-1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 25.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Jiayi-Pan/Tiny-Vicuna-1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 33.82 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Jiayi-Pan/Tiny-Vicuna-1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 58.41 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Jiayi-Pan/Tiny-Vicuna-1B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 1.52 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Jiayi-Pan/Tiny-Vicuna-1B name: Open LLM Leaderboard --- # Tiny Vicuna 1B This model is a fine-tuned version of [TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T) on [WizardVicuna Dataset](https://github.com/melodysdreamj/WizardVicunaLM). It should be fully compatible with Vicuna-v1.5 series. This model is easy to iterate on for early experiments! # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Jiayi-Pan__Tiny-Vicuna-1B) | Metric |Value| |---------------------------------|----:| |Avg. |34.76| |AI2 Reasoning Challenge (25-Shot)|33.45| |HellaSwag (10-Shot) |55.92| |MMLU (5-Shot) |25.45| |TruthfulQA (0-shot) |33.82| |Winogrande (5-shot) |58.41| |GSM8k (5-shot) | 1.52|
facebook/galactica-30b
facebook
"2023-01-24T17:20:45Z"
5,183
39
transformers
[ "transformers", "pytorch", "opt", "text-generation", "galactica", "arxiv:1810.03993", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
"2022-11-16T14:46:22Z"
--- license: cc-by-nc-4.0 tags: - galactica widget: - text: "The Transformer architecture [START_REF]" - text: "The Schwarzschild radius is defined as: \\[" - text: "A force of 0.6N is applied to an object, which accelerates at 3m/s. What is its mass? <work>" - text: "Lecture 1: The Ising Model\n\n" - text: "[START_I_SMILES]" - text: "[START_AMINO]GHMQSITAGQKVISKHKNGRFYQCEVVRLTTETFYEVNFDDGSFSDNLYPEDIVSQDCLQFGPPAEGEVVQVRWTDGQVYGAKFVASHPIQMYQVEFEDGSQLVVKRDDVYTLDEELP[END_AMINO] ## Keywords" inference: false --- ![logo](https://s3.amazonaws.com/moonup/production/uploads/1668679814649-62441d1d9fdefb55a0b7d12c.png) # GALACTICA 30 B (large) Model card from the original [repo](https://github.com/paperswithcode/galai/blob/main/docs/model_card.md) Following [Mitchell et al. (2018)](https://arxiv.org/abs/1810.03993), this model card provides information about the GALACTICA model, how it was trained, and the intended use cases. Full details about how the model was trained and evaluated can be found in the [release paper](https://galactica.org/paper.pdf). ## Model Details The GALACTICA models are trained on a large-scale scientific corpus. The models are designed to perform scientific tasks, including but not limited to citation prediction, scientific QA, mathematical reasoning, summarization, document generation, molecular property prediction and entity extraction. The models were developed by the Papers with Code team at Meta AI to study the use of language models for the automatic organization of science. We train models with sizes ranging from 125M to 120B parameters. Below is a summary of the released models: | Size | Parameters | |:-----------:|:-----------:| | `mini` | 125 M | | `base` | 1.3 B | | `standard` | 6.7 B | | `large` | 30 B | | `huge` | 120 B | ## Release Date November 2022 ## Model Type Transformer based architecture in a decoder-only setup with a few modifications (see paper for more details). ## Paper & Demo [Paper](https://galactica.org/paper.pdf) / [Demo](https://galactica.org) ## Model Use The primary intended users of the GALACTICA models are researchers studying language models applied to the scientific domain. We also anticipate the model will be useful for developers who wish to build scientific tooling. However, we caution against production use without safeguards given the potential of language models to hallucinate. The models are made available under a non-commercial CC BY-NC 4.0 license. More information about how to use the model can be found in the README.md of this repository. ## Training Data The GALACTICA models are trained on 106 billion tokens of open-access scientific text and data. This includes papers, textbooks, scientific websites, encyclopedias, reference material, knowledge bases, and more. We tokenize different modalities to provide a natural langauge interface for different tasks. See the README.md for more information. See the paper for full information on the training data. ## How to use Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, OPTForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-30b") model = OPTForCausalLM.from_pretrained("facebook/galactica-30b") input_text = "The Transformer architecture [START_REF]" input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import AutoTokenizer, OPTForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-30b") model = OPTForCausalLM.from_pretrained("facebook/galactica-30b", device_map="auto") input_text = "The Transformer architecture [START_REF]" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU using different precisions #### FP16 <details> <summary> Click to expand </summary> ```python # pip install accelerate import torch from transformers import AutoTokenizer, OPTForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-30b") model = OPTForCausalLM.from_pretrained("facebook/galactica-30b", device_map="auto", torch_dtype=torch.float16) input_text = "The Transformer architecture [START_REF]" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> #### INT8 <details> <summary> Click to expand </summary> ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, OPTForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-30b") model = OPTForCausalLM.from_pretrained("facebook/galactica-30b", device_map="auto", load_in_8bit=True) input_text = "The Transformer architecture [START_REF]" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ## Performance and Limitations The model outperforms several existing language models on a range of knowledge probes, reasoning, and knowledge-intensive scientific tasks. This also extends to general NLP tasks, where GALACTICA outperforms other open source general language models. That being said, we note a number of limitations in this section. As with other language models, GALACTICA is often prone to hallucination - and training on a high-quality academic corpus does not prevent this, especially for less popular and less cited scientific concepts. There are no guarantees of truthful output when generating from the model. This extends to specific modalities such as citation prediction. While GALACTICA's citation behaviour approaches the ground truth citation behaviour with scale, the model continues to exhibit a popularity bias at larger scales. In addition, we evaluated the model on several types of benchmarks related to stereotypes and toxicity. Overall, the model exhibits substantially lower toxicity rates compared to other large language models. That being said, the model continues to exhibit bias on certain measures (see the paper for details). So we recommend care when using the model for generations. ## Broader Implications GALACTICA can potentially be used as a new way to discover academic literature. We also expect a lot of downstream use for application to particular domains, such as mathematics, biology, and chemistry. In the paper, we demonstrated several examples of the model acting as alternative to standard search tools. We expect a new generation of scientific tools to be built upon large language models such as GALACTICA. We encourage researchers to investigate beneficial and new use cases for these models. That being said, it is important to be aware of the current limitations of large language models. Researchers should pay attention to common issues such as hallucination and biases that could emerge from using these models. ## Citation ```bibtex @inproceedings{GALACTICA, title={GALACTICA: A Large Language Model for Science}, author={Ross Taylor and Marcin Kardas and Guillem Cucurull and Thomas Scialom and Anthony Hartshorn and Elvis Saravia and Andrew Poulton and Viktor Kerkez and Robert Stojnic}, year={2022} } ```
TheBloke/Synthia-70B-GGUF
TheBloke
"2023-09-27T12:46:22Z"
5,182
7
transformers
[ "transformers", "gguf", "llama", "text-generation", "en", "arxiv:2306.02707", "base_model:migtissera/Synthia-70B", "license:llama2", "text-generation-inference", "region:us" ]
text-generation
"2023-08-26T12:20:37Z"
--- language: - en license: llama2 library_name: transformers model_name: Synthia 70B base_model: migtissera/Synthia-70B inference: false model_creator: Migel Tissera model_type: llama pipeline_tag: text-generation prompt_template: 'SYSTEM: {system_message} USER: {prompt} ASSISTANT: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Synthia 70B - GGUF - Model creator: [Migel Tissera](https://huggingface.co/migtissera) - Original model: [Synthia 70B](https://huggingface.co/migtissera/Synthia-70B) <!-- description start --> ## Description This repo contains GGUF format model files for [Migel Tissera's Synthia 70B](https://huggingface.co/migtissera/Synthia-70B). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Synthia-70B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Synthia-70B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Synthia-70B-GGUF) * [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Synthia-70B) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Orca-Vicuna ``` SYSTEM: {system_message} USER: {prompt} ASSISTANT: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [synthia-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q2_K.gguf) | Q2_K | 2 | 29.11 GB| 31.61 GB | smallest, significant quality loss - not recommended for most purposes | | [synthia-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.75 GB| 32.25 GB | very small, high quality loss | | [synthia-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.10 GB| 35.60 GB | very small, high quality loss | | [synthia-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss | | [synthia-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 38.99 GB| 41.49 GB | small, greater quality loss | | [synthia-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.38 GB| 43.88 GB | medium, balanced quality - recommended | | [synthia-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended | | [synthia-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Synthia-70B-GGUF/blob/main/synthia-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended | | synthia-70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss | | synthia-70b.Q8_0.gguf | Q8_0 | 8 | 73.23 GB| 75.73 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ### Q6_K and Q8_0 files are split and require joining **Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files. <details> <summary>Click for instructions regarding Q6_K and Q8_0 files</summary> ### q6_K Please download: * `synthia-70b.Q6_K.gguf-split-a` * `synthia-70b.Q6_K.gguf-split-b` ### q8_0 Please download: * `synthia-70b.Q8_0.gguf-split-a` * `synthia-70b.Q8_0.gguf-split-b` To join the files, do the following: Linux and macOS: ``` cat synthia-70b.Q6_K.gguf-split-* > synthia-70b.Q6_K.gguf && rm synthia-70b.Q6_K.gguf-split-* cat synthia-70b.Q8_0.gguf-split-* > synthia-70b.Q8_0.gguf && rm synthia-70b.Q8_0.gguf-split-* ``` Windows command line: ``` COPY /B synthia-70b.Q6_K.gguf-split-a + synthia-70b.Q6_K.gguf-split-b synthia-70b.Q6_K.gguf del synthia-70b.Q6_K.gguf-split-a synthia-70b.Q6_K.gguf-split-b COPY /B synthia-70b.Q8_0.gguf-split-a + synthia-70b.Q8_0.gguf-split-b synthia-70b.Q8_0.gguf del synthia-70b.Q8_0.gguf-split-a synthia-70b.Q8_0.gguf-split-b ``` </details> <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Synthia-70B-GGUF and below it, a specific filename to download, such as: synthia-70b.q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Synthia-70B-GGUF synthia-70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Synthia-70B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Synthia-70B-GGUF synthia-70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m synthia-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model from Python using ctransformers #### First install the package ```bash # Base ctransformers with no GPU acceleration pip install ctransformers>=0.2.24 # Or with CUDA GPU acceleration pip install ctransformers[cuda]>=0.2.24 # Or with ROCm GPU acceleration CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers ``` #### Simple example code to load one of these GGUF models ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/Synthia-70B-GGUF", model_file="synthia-70b.q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here's guides on using llama-cpp-python or ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Migel Tissera's Synthia 70B # Synthia-70B SynthIA (Synthetic Intelligent Agent) is a LLama-2-70B model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations. <br> ![Synthia](https://huggingface.co/migtissera/Synthia-70B/resolve/main/Synthia.jpeg) <br> <br> #### License Disclaimer: This model is bound by the license & usage restrictions of the original Llama-2 model, and comes with no warranty or gurantees of any kind. <br> ## Evaluation We evaluated Synthia-70B on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |||| |:------:|:--------:|:-------:| |**Task**|**Metric**|**Value**| |*arc_challenge*|acc_norm|0.6945| |*hellaswag*|acc_norm|0.8711| |*mmlu*|acc_norm|0.6891| |*truthfulqa_mc*|mc2|0.5979| |**Total Average**|-|**0.7132**|| <br> ## Example Usage ### Here is prompt format: ``` SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually. USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? ASSISTANT: ``` ### Below shows a code example on how to use this model: ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/Synthia-70B" output_file_path = "./Synthia-70B-conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: As a an AI superintelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" json_data = {"prompt": user_input, "answer": answer} ## Save your conversation with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ``` <br> #### Limitations & Biases: While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. Exercise caution and cross-check information when necessary. This is an uncensored model. <br> ### Citiation: Please kindly cite using the following BibTeX: ``` @misc{Synthia-70B, author = {Migel Tissera}, title = {Synthia-70B: Synthetic Intelligent Agent}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://huggingface.co/migtissera/Synthia-70B}, } ``` ``` @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @software{touvron2023llama, title={LLaMA2: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ``` <!-- original-model-card end -->
mradermacher/Venomia-m7-i1-GGUF
mradermacher
"2024-06-05T08:43:13Z"
5,181
0
transformers
[ "transformers", "gguf", "en", "base_model:Sao10K/Venomia-m7", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
null
"2024-06-04T15:16:21Z"
--- base_model: Sao10K/Venomia-m7 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Sao10K/Venomia-m7 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Venomia-m7-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Venomia-m7-i1-GGUF/resolve/main/Venomia-m7.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
sentence-transformers/bert-large-nli-mean-tokens
sentence-transformers
"2024-03-27T10:12:29Z"
5,179
0
sentence-transformers
[ "sentence-transformers", "pytorch", "tf", "jax", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "arxiv:1908.10084", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]
sentence-similarity
"2022-03-02T23:29:05Z"
--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/bert-large-nli-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/bert-large-nli-mean-tokens') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-large-nli-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/bert-large-nli-mean-tokens') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-large-nli-mean-tokens) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
SeaLLMs/SeaLLM-7B-v2
SeaLLMs
"2024-04-15T02:17:00Z"
5,177
62
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "multilingual", "sea", "conversational", "en", "zh", "vi", "id", "th", "ms", "km", "lo", "my", "tl", "arxiv:2312.00738", "arxiv:2205.11916", "arxiv:2306.05179", "arxiv:2306.05685", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-01-29T08:59:58Z"
--- license: other license_name: seallms license_link: https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat/blob/main/LICENSE language: - en - zh - vi - id - th - ms - km - lo - my - tl tags: - multilingual - sea --- <p align="center"> <img src="seal_logo.png" width="200" /> </p> # *SeaLLM-7B-v2* - Large Language Models for Southeast Asia # <strong style="color: red">BIG NEWS: <a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2.5">SeaLLM-7B-v2.5</a> is released with state-of-the-art performance in world knowledge and reasoning. SeaLLM-7B-v2 will begin deprecation.</strong> <p align="center"> <a href="https://damo-nlp-sg.github.io/SeaLLMs/" target="_blank" rel="noopener">Technical Blog</a> &nbsp;&nbsp; <a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2" target="_blank" rel="noopener"> 🤗 Tech Memo</a> &nbsp;&nbsp; <a href="https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B" target="_blank" rel="noopener"> 🤗 DEMO</a> &nbsp;&nbsp; <a href="https://github.com/DAMO-NLP-SG/SeaLLMs" target="_blank" rel="noopener">Github</a> &nbsp;&nbsp; <a href="https://arxiv.org/pdf/2312.00738.pdf" target="_blank" rel="noopener">Technical Report</a> </p> We introduce [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2), the state-of-the-art multilingual LLM for Southeast Asian (SEA) languages 🇬🇧 🇨🇳 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇲🇲 🇵🇭. It is the most significant upgrade since [SeaLLM-13B](https://huggingface.co/SeaLLMs/SeaLLM-13B-Chat), with half the size, outperforming performance across diverse multilingual tasks, from world knowledge, math reasoning, instruction following, etc. ### Highlights * [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves the **7B-SOTA** on the **Zero-shot CoT GSM8K** task with **78.2** score and outperforms GPT-3.5 in many GSM8K-translated tasks in SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭) as well as MGSM (🇨🇳 🇹🇭). It also surpasses GPT-3.5 in MATH CoT for Thai 🇹🇭. * It scores competitively against GPT-3.5 in many zero-shot CoT commonsense benchmark, with **82.5, 68.3, 80.9** scores on Arc-C, Winogrande, and Hellaswag. * It achieves **7.54** score on the 🇬🇧 **MT-bench**, it ranks 3rd place on the leaderboard for 7B category and is the most outperforming multilingual model. * It scores **45.74** on the VMLU benchmark for Vietnamese 🇻🇳, and is the only open-source multilingual model that can be competitive to monolingual models ([Vistral-7B](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)) of similar sizes. ### Release and DEMO - DEMO: [SeaLLMs/SeaLLM-7B](https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B). - Technical report: [Arxiv: SeaLLMs - Large Language Models for Southeast Asia](https://arxiv.org/pdf/2312.00738.pdf). - Model weights: - [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2). - [SeaLLM-7B-v2-gguf](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf). - [SeaLLM-7B-v2-GGUF (thanks Lonestriker)](https://huggingface.co/LoneStriker/SeaLLM-7B-v2-GGUF). NOTE: use [seallm.preset.json](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/seallm.preset.json) to work properly. - Run locally: - [LM-studio](https://lmstudio.ai/): - [SeaLLM-7B-v2-q4_0](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/SeaLLM-7B-v2.q4_0.gguf) and [SeaLLM-7B-v2-q8_0](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/SeaLLM-7B-v2.q8_0.gguf). - LM-studio requires this [seallm.preset.json](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2-gguf/blob/main/seallm.preset.json) to set chat template properly. - [ollama](https://ollama.ai/) `ollama run nxphi47/seallm-7b-v2:q4_0` - [MLX for Apple Silicon](https://github.com/ml-explore/mlx): [mlx-community/SeaLLM-7B-v2-4bit-mlx](https://huggingface.co/mlx-community/SeaLLM-7B-v2-4bit-mlx) <blockquote style="color:red"> <p><strong style="color: red">Terms of Use and License</strong>: By using our released weights, codes, and demos, you agree to and comply with the terms and conditions specified in our <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b/edit/main/LICENSE" target="_blank" rel="noopener">SeaLLMs Terms Of Use</a>. </blockquote> > **Disclaimer**: > We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. > Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. > In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos. > The logo was generated by DALL-E 3. ### What's new since SeaLLM-13B-v1 and SeaLLM-7B-v1? * SeaLLM-7B-v2 is continue-pretrained from [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) and underwent carefully designed tuning with focus in reasoning. ## Evaluation ### Zero-shot CoT Multilingual Math Reasoning [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) achieves with **78.2** score on the GSM8K with zero-shot CoT reasoning, making it the **state of the art** in the realm of 7B models. It also outperforms GPT-3.5 in the same GSM8K benchmark as translated into SEA languages (🇨🇳 🇻🇳 🇮🇩 🇹🇭). [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also surpasses GPT-3.5 on the Thai-translated MATH benchmark, with **22.4** vs 18.1 scores. ![fig_sea_math_side_by_side.png](fig_sea_math_side_by_side.png) <details> <summary>See details on English and translated GSM8K and MATH with zero-shot reasoning</summary> <br> | Model | GSM8K<br>en | MATH<br>en | GSM8K<br>zh | MATH<br>zh | GSM8K<br>vi | MATH<br>vi | GSM8K<br>id | MATH<br>id | GSM8K<br>th | MATH<br>th | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | GPT-3.5 | 80.8 | 34.1 | 48.2 | 21.5 | 55 | 26.5 | 64.3 | 26.4 | 35.8 | 18.1 | Qwen-14B-chat | 61.4 | 18.4 | 41.6 | 11.8 | 33.6 | 3.6 | 44.7 | 8.6 | 22 | 6 | Vistral-7b-chat | 48.2 | 12.5 | | | 48.7 | 3.1 | | | | | Qwen1.5-7B-chat | 56.8 | 15.3 | 40 | 2.7 | 37.7 | 9 | 36.9 | 7.7 | 21.9 | | SeaLLM-7B-v2 | 78.2 | 27.5 | 53.7 | 17.6 | 69.9 | 23.8 | 71.5 | 24.4 | 59.6 | 22.4 </details> Baselines were evaluated using their respective chat-template and system prompts ([Qwen1.5-7B-chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json), [Vistral](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat)). #### Zero-shot MGSM [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) also outperforms GPT-3.5 and Qwen-14B on the multilingual MGSM for Zh and Th. | Model | MGSM-Zh | MGSM-Th |-----| ----- | --- | ChatGPT (reported) | 61.2 | 47.2 | Qwen-14B-chat | 59.6 | 28 | SeaLLM-7B-v2 | **64.8** | **62.4** ### Zero-shot Commonsense Reasoning We compare [SeaLLM-7B-v2](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2) with ChatGPT and Mistral-7B-instruct on various zero-shot commonsense benchmarks (Arc-Challenge, Winogrande and Hellaswag). We use the 2-stage technique in [(Kojima et al., 2023)](https://arxiv.org/pdf/2205.11916.pdf) to grab the answer. Note that we **DID NOT** use "Let's think step-by-step" to invoke explicit CoT. | 0-shot reasoning | Arc-Challenge | Winogrande | Hellaswag |-----| ----- | --- | -- | | ChatGPT (reported) | 84.6* | 66.8* | 72.0* | ChatGPT (reproduced)| 84.1 | 63.1 | 79.5 | Mistral-7B-Instruct | 68.1 | 56.4 | 45.6 | Qwen1.5-7B-chat | 79.3 | 59.4 | 69.3 | SeaLLM-7B-v2 | 82.5 | 68.3 | 80.9 Baselines were evaluated using their respective chat-template and system prompts ([Qwen1.5-7B-chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json), [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)). ### Multilingual World Knowledge We evaluate models on 3 benchmarks following the recommended default setups: 5-shot MMLU for En, 3-shot [M3Exam](https://arxiv.org/pdf/2306.05179.pdf) (M3e) for En, Zh, Vi, Id, Th, and zero-shot [VMLU](https://vmlu.ai/) for Vi. | Model | Langs | En<br>MMLU | En<br>M3e | Zh<br>M3e | Vi<br>M3e | Vi<br>VMLU | Id<br>M3e | Th<br>M3e |-----| ----- | --- | -- | ----- | ---- | --- | --- | --- | | GPT-3.5 | Multi | 68.90 | 75.46 | 60.20 | 58.64 | 46.32 | 49.27 | 37.41 | Vistral-7B-chat | Mono | 56.86 | 67.00 | 44.56 | 54.33 | 50.03 | 36.49 | 25.27 | Qwen1.5-7B-chat | Multi | 61.00 | 52.07 | 81.96 | 43.38 | 45.02 | 24.29 | 20.25 | SeaLLM-7B-v2 | Multi | 61.89 | 70.91 | 55.43 | 51.15 | 45.74 | 42.25 | 35.52 VMLU reproduce script [here](https://github.com/DAMO-NLP-SG/SeaLLMs/blob/main/evaluation/vmlu/vmlu_run.py). Lm-eval was used to evaluate MMLU. 0-shot VMLU scores for baselines were evaluated using their respective chat-template and system prompts ([Qwen1.5-7B-chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/tokenizer_config.json)). ### MT-Bench On the English [MT-bench](https://arxiv.org/abs/2306.05685) metric, SeaLLM-7B-v2 achieves **7.54** score on the MT-bench (3rd place on the leaderboard for 7B category), outperforms many 70B models and is arguably the only one that handles 10 SEA languages. Refer to [mt_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/mt_bench/seallm_7b_v2.jsonl) for the MT-bench predictions of SeaLLM-7B-v2, and [here](https://github.com/lm-sys/FastChat/issues/3013#issue-2118685341) to reproduce it. | Model | Access | Langs | MT-Bench | --- | --- | --- | --- | | GPT-4-turbo | closed | multi | 9.32 | GPT-4-0613 | closed | multi | 9.18 | Mixtral-8x7b (46B) | open | multi | 8.3 | Starling-LM-7B-alpha | open | mono (en) | 8.0 | OpenChat-3.5-7B | open | mono (en) | 7.81 | **SeaLLM-7B-v2** | **open** | **multi (10+)** | **7.54** | [Qwen-14B](https://huggingface.co/Qwen/Qwen-14B-Chat) | open | multi | 6.96 | [Llama-2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | open | mono (en) | 6.86 | Mistral-7B-instuct | open | mono (en) | 6.84 ### Sea-Bench Similar to MT-Bench, [Sea-bench](https://huggingface.co/datasets/SeaLLMs/Sea-bench) is a set of categorized instruction test sets to measure models' ability as an assistant that is specifically focused on 9 SEA languages, including non-Latin low-resource languages. As shown, the huge improvements come from math-reasoning, reaching GPT-3.5 level of performance. ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png) Refer to [sea_bench/seallm_7b_v2.jsonl](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2/blob/main/evaluation/sea_bench/seallm_7b_v2.jsonl) for the Sea-bench predictions of SeaLLM-7B-v2. ### Usage #### Instruction format ```python prompt = """<|im_start|>system You are a helpful assistant.</s><|im_start|>user Hello world</s><|im_start|>assistant Hi there, how can I help?</s>""" # NOTE: previous commit has \n between </s> and <|im_start|>, that was incorrect! # <|im_start|> is not a special token. # Transformers chat_template should be consistent with vLLM format below. # ! ENSURE 1 and only 1 bos `<s>` at the beginning of sequence print(tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))) '<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '?', '</s>'] """ ``` #### Using transformers's chat_template ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # use bfloat16 to ensure the best performance. model = AutoModelForCausalLM.from_pretrained("SeaLLMs/SeaLLM-7B-v2", torch_dtype=torch.bfloat16, device_map=device) tokenizer = AutoTokenizer.from_pretrained("SeaLLMs/SeaLLM-7B-v2") messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world"}, {"role": "assistant", "content": "Hi there, how can I help you today?"}, {"role": "user", "content": "Explain general relativity in details."} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) print(tokenizer.convert_ids_to_tokens(encodeds[0])) # ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁a', '▁helpful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '▁you', '▁today', '?', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Ex', 'plain', '▁general', '▁rel', 'ativity', '▁in', '▁details', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>'] model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.pad_token_id) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` #### Using vLLM ```python from vllm import LLM, SamplingParams TURN_TEMPLATE = "<|im_start|>{role}\n{content}</s>" TURN_PREFIX = "<|im_start|>{role}\n" # There is no \n between </s> and <|im_start|>. def seallm_chat_convo_format(conversations, add_assistant_prefix: bool, system_prompt=None): # conversations: list of dict with key `role` and `content` (openai format) if conversations[0]['role'] != 'system' and system_prompt is not None: conversations = [{"role": "system", "content": system_prompt}] + conversations text = '' for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) text += prompt if add_assistant_prefix: prompt = TURN_PREFIX.format(role='assistant') text += prompt return text sparams = SamplingParams(temperature=0.1, max_tokens=1024, stop=['</s>', '<|im_start|>']) llm = LLM("SeaLLMs/SeaLLM-7B-v2", dtype="bfloat16") message = "Explain general relativity in details." prompt = seallm_chat_convo_format(message, True) gen = llm.generate(prompt, sampling_params) print(gen[0].outputs[0].text) ``` #### Fine-tuning SeaLLM-7B-v2 Should follow the chat format and accurately mask out source tokens. Here is an example. ```python conversations = [ {"role": "system", "content": "You are helful assistant."}, {"role": "user", "content": "Hello world."}, {"role": "assistant", "content": "Hi there, how can I help?"}, {"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, ] def seallm_7b_v2_tokenize_multi_turns(tokenizer, conversations, add_assistant_prefix=False): """ Inputs: conversations: list of dict following openai format, eg conversations = [ {"role": "system", "content": "You are helful assistant."}, {"role": "user", "content": "Hello world."}, {"role": "assistant", "content": "Hi there, how can I help?"}, {"role": "user", "content": "Tell me a joke."}, {"role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything."}, ] add_assistant_prefix: whether to add assistant_prefix, only for inference decoding Outputs: tokenize_output_sample, { "input_ids": ... "token_type_ids": 1 if train and 0 if masked out (not train) } During training, need to create a labels, with masked-out tokens = -100 to avoid loss computations. labels = sample['input_ids'].clone() labels[sample['token_type_ids'] == 0] = -100 """ TURN_TEMPLATE = "<|im_start|>{role}\n{content}</s>" TURN_PREFIX = "<|im_start|>{role}\n" sample = None assistant_prefix_len = None for turn_id, turn in enumerate(conversations): prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) turn_sample = tokenizer( prompt, padding=False, truncation=False, verbose=False, add_special_tokens=False, return_token_type_ids=True, ) if turn['role'] == 'assistant': if assistant_prefix_len is None: assistant_prefix_len = len(tokenizer.encode(TURN_PREFIX.format(role=turn['role']), add_special_tokens=False)) turn_sample['token_type_ids'][assistant_prefix_len:] = [1] * (len(turn_sample['input_ids']) - assistant_prefix_len) if sample is None: sample = turn_sample else: for k in turn_sample.keys(): sample[k].extend(turn_sample[k]) if add_assistant_prefix: assistant_prefix_sample = tokenizer( TURN_PREFIX.format(role="assistant"), padding=False, truncation=False, verbose=False, add_special_tokens=False, return_token_type_ids=True, ) for k in sample.keys(): sample[k].extend(assistant_prefix_sample[k]) if tokenizer.add_bos_token: sample['input_ids'] = [tokenizer.bos_token_id] + sample['input_ids'] sample['attention_mask'] = [1] + sample['attention_mask'] sample['token_type_ids'] = [sample['token_type_ids'][0]] + sample['token_type_ids'] return sample # ! testing sample = seallm_7b_v2_tokenize_multi_turns(tokenizer, conversations) print(tokenizer.convert_ids_to_tokens(sample['input_ids'])) print(sample['token_type_ids']) # ['<s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'system', '<0x0A>', 'You', '▁are', '▁hel', 'ful', '▁assistant', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Hello', '▁world', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Hi', '▁there', ',', '▁how', '▁can', '▁I', '▁help', '?', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'user', '<0x0A>', 'Tell', '▁me', '▁a', '▁joke', '.', '</s>', '▁<', '|', 'im', '_', 'start', '|', '>', 'ass', 'istant', '<0x0A>', 'Why', '▁don', "'", 't', '▁scientists', '▁trust', '▁atoms', '?', '▁Because', '▁they', '▁make', '▁up', '▁everything', '.', '</s>'] # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ``` ## Acknowledgement to Our Linguists We would like to express our special thanks to our professional and native linguists, Tantong Champaiboon, Nguyen Ngoc Yen Nhi and Tara Devina Putri, who helped build, evaluate, and fact-check our sampled pretraining and SFT dataset as well as evaluating our models across different aspects, especially safety. ## Citation If you find our project useful, we hope you would kindly star our repo and cite our work as follows: Corresponding Author: [[email protected]](mailto:[email protected]) **Author list and order will change!** * `*` and `^` are equal contributions. ``` @article{damonlpsg2023seallm, author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, Eprint = {arXiv:2312.00738}, } ```
RichardErkhov/davzoku_-_frankencria-llama2-12.5b-v1.3-m.2-gguf
RichardErkhov
"2024-06-28T20:15:08Z"
5,176
0
null
[ "gguf", "region:us" ]
null
"2024-06-28T16:13:49Z"
Entry not found
apple/OpenELM-270M-Instruct
apple
"2024-05-02T00:55:44Z"
5,175
111
transformers
[ "transformers", "safetensors", "openelm", "text-generation", "custom_code", "arxiv:2404.14619", "license:other", "autotrain_compatible", "region:us" ]
text-generation
"2024-04-12T21:51:40Z"
--- license: other license_name: apple-sample-code-license license_link: LICENSE --- # OpenELM *Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari* We introduce **OpenELM**, a family of **Open** **E**fficient **L**anguage **M**odels. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters. Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them. ## Usage We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`. You can try the model by running the following command: ``` python generate_openelm.py --model apple/OpenELM-270M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 ``` Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token. Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows: ``` python generate_openelm.py --model apple/OpenELM-270M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10 ``` Alternatively, try model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) by passing a smaller model through the `assistant_model` argument, for example: ``` python generate_openelm.py --model apple/OpenELM-270M-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL] ``` ## Main Results ### Zero-Shot | **Model Size** | **ARC-c** | **ARC-e** | **BoolQ** | **HellaSwag** | **PIQA** | **SciQ** | **WinoGrande** | **Average** | |-----------------------------------------------------------------------------|-----------|-----------|-----------|---------------|-----------|-----------|----------------|-------------| | [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 26.45 | 45.08 | **53.98** | 46.71 | 69.75 | **84.70** | **53.91** | 54.37 | | [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **30.55** | **46.68** | 48.56 | **52.07** | **70.78** | 84.40 | 52.72 | **55.11** | | [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 27.56 | 48.06 | 55.78 | 53.97 | 72.31 | 87.20 | 58.01 | 57.56 | | [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **30.38** | **50.00** | **60.37** | **59.34** | **72.63** | **88.00** | **58.96** | **59.95** | | [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 32.34 | **55.43** | 63.58 | 64.81 | **75.57** | **90.60** | 61.72 | 63.44 | | [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **37.97** | 52.23 | **70.00** | **71.20** | 75.03 | 89.30 | **62.75** | **65.50** | | [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 35.58 | 59.89 | 67.40 | 72.44 | 78.24 | **92.70** | 65.51 | 67.39 | | [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **39.42** | **61.74** | **68.17** | **76.36** | **79.00** | 92.50 | **66.85** | **69.15** | ### LLM360 | **Model Size** | **ARC-c** | **HellaSwag** | **MMLU** | **TruthfulQA** | **WinoGrande** | **Average** | |-----------------------------------------------------------------------------|-----------|---------------|-----------|----------------|----------------|-------------| | [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | 47.15 | 25.72 | **39.24** | **53.83** | 38.72 | | [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | **51.58** | **26.70** | 38.72 | 53.20 | **40.54** | | [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | 53.86 | **26.01** | 40.18 | 57.22 | 41.50 | | [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | **59.31** | 25.41 | **40.48** | **58.33** | **43.41** | | [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | 65.71 | **27.05** | 36.98 | 63.22 | 45.93 | | [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | **71.83** | 25.65 | **45.95** | **64.72** | **49.94** | | [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | 73.28 | **26.76** | 34.98 | 67.25 | 48.90 | | [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | **76.87** | 24.80 | **38.76** | **67.96** | **51.22** | ### OpenLLM Leaderboard | **Model Size** | **ARC-c** | **CrowS-Pairs** | **HellaSwag** | **MMLU** | **PIQA** | **RACE** | **TruthfulQA** | **WinoGrande** | **Average** | |-----------------------------------------------------------------------------|-----------|-----------------|---------------|-----------|-----------|-----------|----------------|----------------|-------------| | [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | **66.79** | 47.15 | 25.72 | 69.75 | 30.91 | **39.24** | **53.83** | 45.13 | | [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | 66.01 | **51.58** | **26.70** | **70.78** | 33.78 | 38.72 | 53.20 | **46.66** | | [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | **68.63** | 53.86 | **26.01** | 72.31 | 33.11 | 40.18 | 57.22 | 47.69 | | [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | 67.44 | **59.31** | 25.41 | **72.63** | **36.84** | **40.48** | **58.33** | **49.25** | | [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | **71.74** | 65.71 | **27.05** | **75.57** | 36.46 | 36.98 | 63.22 | 51.68 | | [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | 71.02 | **71.83** | 25.65 | 75.03 | **39.43** | **45.95** | **64.72** | **54.40** | | [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | **73.29** | 73.28 | **26.76** | 78.24 | **38.76** | 34.98 | 67.25 | 54.35 | | [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | 72.33 | **76.87** | 24.80 | **79.00** | 38.47 | **38.76** | **67.96** | **55.73** | See the technical report for more results and comparison. ## Evaluation ### Setup Install the following dependencies: ```bash # install public lm-eval-harness harness_repo="public-lm-eval-harness" git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo} cd ${harness_repo} # use main branch on 03-15-2024, SHA is dc90fec git checkout dc90fec pip install -e . cd .. # 66d6242 is the main branch on 2024-04-01 pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242 pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0 ``` ### Evaluate OpenELM ```bash # OpenELM-270M-Instruct hf_model=apple/OpenELM-270M-Instruct # this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True tokenizer=meta-llama/Llama-2-7b-hf add_bos_token=True batch_size=1 mkdir lm_eval_output shot=0 task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2 lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=5 task=mmlu,winogrande lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=25 task=arc_challenge,crows_pairs_english lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log shot=10 task=hellaswag lm_eval --model hf \ --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \ --tasks ${task} \ --device cuda:0 \ --num_fewshot ${shot} \ --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log ``` ## Bias, Risks, and Limitations The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements. ## Citation If you find our work useful, please cite: ```BibTex @article{mehtaOpenELMEfficientLanguage2024, title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open} {Training} and {Inference} {Framework}}, shorttitle = {{OpenELM}}, url = {https://arxiv.org/abs/2404.14619v1}, language = {en}, urldate = {2024-04-24}, journal = {arXiv.org}, author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad}, month = apr, year = {2024}, } @inproceedings{mehta2022cvnets, author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad}, title = {CVNets: High Performance Library for Computer Vision}, year = {2022}, booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, series = {MM '22} } ```
redponike/Llama-3-Instruct-8B-SPPO-Iter3-GGUF
redponike
"2024-06-26T16:41:34Z"
5,175
0
null
[ "gguf", "region:us" ]
null
"2024-06-26T14:12:55Z"
GGUF quants of [UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) I modified the tokenizer parameters to get properly working GGUFs.
mradermacher/Samantha-Qwen2-7B-i1-GGUF
mradermacher
"2024-06-17T18:15:04Z"
5,173
1
transformers
[ "transformers", "gguf", "en", "zh", "dataset:macadeliccc/opus_samantha", "dataset:HuggingfaceH4/ultrachat_200k", "dataset:teknium/OpenHermes-2.5", "dataset:Sao10K/Claude-3-Opus-Instruct-15K", "base_model:macadeliccc/Samantha-Qwen2-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-17T15:40:25Z"
--- base_model: macadeliccc/Samantha-Qwen2-7B datasets: - macadeliccc/opus_samantha - HuggingfaceH4/ultrachat_200k - teknium/OpenHermes-2.5 - Sao10K/Claude-3-Opus-Instruct-15K language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/macadeliccc/Samantha-Qwen2-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Samantha-Qwen2-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Samantha-Qwen2-7B-i1-GGUF/resolve/main/Samantha-Qwen2-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
DILAB-HYU/KoQuality-Polyglot-5.8b
DILAB-HYU
"2023-11-05T11:49:45Z"
5,170
2
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "polyglot-ko", "gpt-neox", "KoQuality", "ko", "dataset:DILAB-HYU/KoQuality", "base_model:EleutherAI/polyglot-ko-5.8b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-09-24T14:07:52Z"
--- language: - ko license: apache-2.0 tags: - generated_from_trainer - polyglot-ko - gpt-neox - KoQuality datasets: - DILAB-HYU/KoQuality pipeline_tag: text-generation base_model: EleutherAI/polyglot-ko-5.8b model-index: - name: KoAlpaca-Polyglot-5.8B results: [] --- # **KoQuality-Polyglot-5.8b** KoQuality-Polyglot-5.8b is a fine-tuned iteration of the [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) model, specifically trained on the [KoQuality dataset](https://huggingface.co/datasets/DILAB-HYU/KoQuality). Notably, when excluding models employing COT datasets, KoQuality-Polyglot-5.8b exhibits exceptional performance in same size models, even though it operates with a relatively small dataset. ## Open Ko-LLM LeaderBoard <img src="https://cdn-uploads.huggingface.co/production/uploads/6152b4b9ecf3ca6ab820e325/iYzR_mdvkcjnVquho0Y9R.png" width= "1000px" title="하얀 강아지"> Our approach centers around leveraging high-quality instruction datasets to deepen our understanding of commands, all the while preserving the performance of the Pre-trained Language Model (PLM). Compared to alternative models, we have achieved this with minimal learning, **utilizing only 1% of the dataset, which equates to 4006 instructions**. ## Overall Average accuracy score of the KoBEST datasets We use [KoBEST benchmark](https://huggingface.co/datasets/skt/kobest_v1) datasets(BoolQ, COPA, HellaSwag, SentiNeg, WiC) to compare the performance of our best model and other models accuracy. Our model outperforms other models in the average accuracy score of the KoBEST datasets. <img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/t5x4PphoNb-tW3iCzXXHT.png" width= "500px"> | Model | 0-shot | 1-shot | 2-shot | 5-shot | 10-shot | --- | --- | --- | --- | --- | --- | | polyglot-ko-5.8b | 0.4734 | 0.5929 | 0.6120 | 0.6388 | 0.6295 | koalpcaca-polyglot-5.8b | 0.4731 | 0.5284 | 0.5721 | 0.6054 | 0.6042 | kullm-polyglot-5.8b | 0.4415 | 0.6030 | 0.5849 | 0.6252 | 0.6451 | koquality-polyglot-5.8b | 0.4530 | 0.6050 | 0.6351 | 0.6420 | 0.6457 ## Evaluation results ### COPA (F1) <img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/QAie0x99S8-KEKvK0I_uZ.png" width= "500px"> ### BoolQ (F1) <img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/CtEWEQ5BBS05V9cDWA7kp.png" width= "500px"> ### HellaSwag (F1) <img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/cHws6qWkDlTfs5GVcQvtN.png" width= "500px"> ### SentiNeg (F1) <img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/VEG15XXOIbzJyQAusLa4B.png" width= "500px"> ### WiC (F1) <img src="https://cdn-uploads.huggingface.co/production/uploads/650fecfd247f564485f8fbcf/hV-uADJiydkVQOyYysej9.png" width= "500px"> ## Training hyperparameters - learning_rate: 5e-5 - train_batch_size: 4 - seed: 42 - distributed_type: multi-GPU (A100 80G) + No offloading - num_devices: 4 - gradient_accumulation_steps: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ## Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.11.0 - deepspeed 0.9.5 ## Citation ``` @misc{2023koqaulity, title = {KoQuality: Curation of High-quality Instruction Data for Korean Language Models}, author = {Na, Yohan and Kim, Dahye and Chae, Dong-Kyu}, journal={Proceedings of the 35th Annual Conference on Human and Cognitive Language Technology (HCLT 2023)}, pages={306-311}, year = {2023}, } ``` More details can be found here: [github.com/nayohan/KoQuality](https://github.com/nayohan/KoQuality) <br>
TheBloke/WizardLM-30B-Uncensored-GGUF
TheBloke
"2023-09-27T12:52:39Z"
5,169
11
transformers
[ "transformers", "gguf", "llama", "uncensored", "dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered", "base_model:ehartford/WizardLM-30B-Uncensored", "license:other", "text-generation-inference", "region:us" ]
null
"2023-09-19T23:15:29Z"
--- license: other tags: - uncensored datasets: - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered model_name: Wizardlm 30B Uncensored base_model: ehartford/WizardLM-30B-Uncensored inference: false model_creator: Eric Hartford model_type: llama prompt_template: '{prompt} ### Response: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Wizardlm 30B Uncensored - GGUF - Model creator: [Eric Hartford](https://huggingface.co/ehartford) - Original model: [Wizardlm 30B Uncensored](https://huggingface.co/ehartford/WizardLM-30B-Uncensored) <!-- description start --> ## Description This repo contains GGUF format model files for [Eric Hartford's Wizardlm 30B Uncensored](https://huggingface.co/ehartford/WizardLM-30B-Uncensored). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF) * [Eric Hartford's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/WizardLM-30B-Uncensored) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: WizardLM ``` {prompt} ### Response: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [WizardLM-30B-Uncensored.Q2_K.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q2_K.gguf) | Q2_K | 2 | 13.50 GB| 16.00 GB | smallest, significant quality loss - not recommended for most purposes | | [WizardLM-30B-Uncensored.Q3_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q3_K_S.gguf) | Q3_K_S | 3 | 14.06 GB| 16.56 GB | very small, high quality loss | | [WizardLM-30B-Uncensored.Q3_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q3_K_M.gguf) | Q3_K_M | 3 | 15.76 GB| 18.26 GB | very small, high quality loss | | [WizardLM-30B-Uncensored.Q3_K_L.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q3_K_L.gguf) | Q3_K_L | 3 | 17.28 GB| 19.78 GB | small, substantial quality loss | | [WizardLM-30B-Uncensored.Q4_0.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q4_0.gguf) | Q4_0 | 4 | 18.36 GB| 20.86 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [WizardLM-30B-Uncensored.Q4_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q4_K_S.gguf) | Q4_K_S | 4 | 18.44 GB| 20.94 GB | small, greater quality loss | | [WizardLM-30B-Uncensored.Q4_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q4_K_M.gguf) | Q4_K_M | 4 | 19.62 GB| 22.12 GB | medium, balanced quality - recommended | | [WizardLM-30B-Uncensored.Q5_0.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q5_0.gguf) | Q5_0 | 5 | 22.40 GB| 24.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [WizardLM-30B-Uncensored.Q5_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q5_K_S.gguf) | Q5_K_S | 5 | 22.40 GB| 24.90 GB | large, low quality loss - recommended | | [WizardLM-30B-Uncensored.Q5_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q5_K_M.gguf) | Q5_K_M | 5 | 23.05 GB| 25.55 GB | large, very low quality loss - recommended | | [WizardLM-30B-Uncensored.Q6_K.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q6_K.gguf) | Q6_K | 6 | 26.69 GB| 29.19 GB | very large, extremely low quality loss | | [WizardLM-30B-Uncensored.Q8_0.gguf](https://huggingface.co/TheBloke/WizardLM-30B-uncensored-GGUF/blob/main/WizardLM-30B-Uncensored.Q8_0.gguf) | Q8_0 | 8 | 34.57 GB| 37.07 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/WizardLM-30B-uncensored-GGUF and below it, a specific filename to download, such as: WizardLM-30B-Uncensored.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/WizardLM-30B-uncensored-GGUF WizardLM-30B-Uncensored.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/WizardLM-30B-uncensored-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WizardLM-30B-uncensored-GGUF WizardLM-30B-Uncensored.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m WizardLM-30B-Uncensored.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}\n### Response:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-30B-uncensored-GGUF", model_file="WizardLM-30B-Uncensored.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Eric Hartford's Wizardlm 30B Uncensored This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it. <!-- original-model-card end -->
deepset/bert-base-uncased-squad2
deepset
"2023-03-24T14:15:37Z"
5,168
4
transformers
[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "en", "dataset:squad_v2", "license:cc-by-4.0", "model-index", "endpoints_compatible", "region:us" ]
question-answering
"2022-03-02T23:29:05Z"
--- language: en license: cc-by-4.0 datasets: - squad_v2 model-index: - name: deepset/bert-base-uncased-squad2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - type: exact_match value: 75.6529 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTY2YmQ0ZDFjMjRlZWRiZWQ2YWQ4MTM0ODkyYTQ0NmYwMzBlNWViZWQ0ODFhMGJmMmY4ZGYwOTQyMDAyZGNjYyIsInZlcnNpb24iOjF9.UyqonQTsCB0BW86LfPy17kLt3a4r3wMeh04MDam5t_UhElp6N02YpiKOqcb1ethNHjAR0WGyxrcV3TI4d-wFAQ - type: f1 value: 78.6191 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWRkZWVjMDU2YTcxYWVkZTU1YmUzY2FkNWI5NDJkM2YwMjFmMmE0Njc3MjI5N2Q0NDdhZDNkZWNjMWE5YTRmZiIsInZlcnNpb24iOjF9.ol0Zacd9ZryXazXjgVssGFYG4s5FzbhGGaj1ZEDLVN2ziyzx23bo4GH9PSuGTFxRK2BO5_dxvDupLRqJOF59Bg --- # bert-base-uncased for QA ## Overview **Language model:** bert-base-uncased **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Infrastructure**: 1x Tesla v100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "bert-base-uncased" max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Performance ``` "exact": 73.67977764676156 "f1": 77.87647139308865 ``` ## Authors - Timo Möller: `timo.moeller [at] deepset.ai` - Julian Risch: `julian.risch [at] deepset.ai` - Malte Pietsch: `malte.pietsch [at] deepset.ai` - Michel Bartels: `michel.bartels [at] deepset.ai` ## About us ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo) We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
timm/inception_v4.tf_in1k
timm
"2023-05-10T01:04:54Z"
5,168
3
timm
[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "arxiv:1602.07261", "license:apache-2.0", "region:us" ]
image-classification
"2023-04-25T21:31:36Z"
--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for inception_v4.tf_in1k A Inception-v4 image classification model. Trained on ImageNet-1k paper authors. Ported from Tensorflow via Cadene's pretrained-models.pytorch. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 42.7 - GMACs: 12.3 - Activations (M): 15.1 - Image size: 299 x 299 - **Papers:** - https://arxiv.org/abs/1602.07261: https://arxiv.org/abs/1602.07261 - **Original:** - https://github.com/tensorflow/models - https://github.com/Cadene/pretrained-models.pytorch - **Dataset:** ImageNet-1k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('inception_v4.tf_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'inception_v4.tf_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 64, 147, 147]) # torch.Size([1, 160, 73, 73]) # torch.Size([1, 384, 35, 35]) # torch.Size([1, 1024, 17, 17]) # torch.Size([1, 1536, 8, 8]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'inception_v4.tf_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1536, 8, 8) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{Szegedy2016Inceptionv4IA, title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning}, author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alexander A. Alemi}, journal={ArXiv}, year={2016}, volume={abs/1602.07261} } ```
RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf
RichardErkhov
"2024-06-26T13:45:38Z"
5,168
0
null
[ "gguf", "region:us" ]
null
"2024-06-26T12:40:46Z"
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) WildWest-Variant3-7B - GGUF - Model creator: https://huggingface.co/BarryFutureman/ - Original model: https://huggingface.co/BarryFutureman/WildWest-Variant3-7B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [WildWest-Variant3-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q2_K.gguf) | Q2_K | 2.53GB | | [WildWest-Variant3-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [WildWest-Variant3-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.IQ3_S.gguf) | IQ3_S | 2.96GB | | [WildWest-Variant3-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [WildWest-Variant3-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.IQ3_M.gguf) | IQ3_M | 3.06GB | | [WildWest-Variant3-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q3_K.gguf) | Q3_K | 3.28GB | | [WildWest-Variant3-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q3_K_M.gguf) | Q3_K_M | 0.36GB | | [WildWest-Variant3-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q3_K_L.gguf) | Q3_K_L | 0.0GB | | [WildWest-Variant3-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.IQ4_XS.gguf) | IQ4_XS | 0.0GB | | [WildWest-Variant3-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q4_0.gguf) | Q4_0 | 0.0GB | | [WildWest-Variant3-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.IQ4_NL.gguf) | IQ4_NL | 0.0GB | | [WildWest-Variant3-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q4_K_S.gguf) | Q4_K_S | 0.0GB | | [WildWest-Variant3-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q4_K.gguf) | Q4_K | 0.0GB | | [WildWest-Variant3-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q4_K_M.gguf) | Q4_K_M | 0.0GB | | [WildWest-Variant3-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q4_1.gguf) | Q4_1 | 0.0GB | | [WildWest-Variant3-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q5_0.gguf) | Q5_0 | 4.65GB | | [WildWest-Variant3-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q5_K_S.gguf) | Q5_K_S | 0.78GB | | [WildWest-Variant3-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q5_K.gguf) | Q5_K | 0.28GB | | [WildWest-Variant3-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q5_K_M.gguf) | Q5_K_M | 0.06GB | | [WildWest-Variant3-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q5_1.gguf) | Q5_1 | 0.01GB | | [WildWest-Variant3-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q6_K.gguf) | Q6_K | 0.0GB | | [WildWest-Variant3-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/BarryFutureman_-_WildWest-Variant3-7B-gguf/blob/main/WildWest-Variant3-7B.Q8_0.gguf) | Q8_0 | 0.0GB | Original model description: --- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - merge --- # WildWest-Variant3-7B Based on a merge of the following models using mergekit * [BarryFutureman/NeuralTurdusVariant1-7B](https://huggingface.co/BarryFutureman/NeuralTurdusVariant1-7B) * [mlabonne/NeuralDaredevil-7B](https://huggingface.co/udkai/Turdus) * [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2) * [PetroGPT/Severus-7B-DPO](https://huggingface.co/PetroGPT/Severus-7B-DPO)
guillaumekln/faster-whisper-base
guillaumekln
"2023-05-12T18:57:32Z"
5,167
9
ctranslate2
[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]
automatic-speech-recognition
"2023-03-23T10:19:37Z"
--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper base model for CTranslate2 This repository contains the conversion of [openai/whisper-base](https://huggingface.co/openai/whisper-base) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("base") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-base --output_dir faster-whisper-base \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-base).**
czearing/article-title-generator
czearing
"2022-06-28T20:08:16Z"
5,166
18
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
"2022-06-28T19:44:19Z"
--- license: mit --- ## Article Title Generator The model is based on the T5 language model and trained using a large collection of Medium articles. ## Usage Example code: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("czearing/article-title-generator") model = AutoModel.from_pretrained("czearing/article-title-generator") ``` ## License MIT
DAMO-NLP-SG/VideoLLaMA2-7B-Base
DAMO-NLP-SG
"2024-06-17T09:17:26Z"
5,165
2
transformers
[ "transformers", "mistral", "text-generation", "multimodal large language model", "large video-language model", "visual-question-answering", "en", "dataset:OpenGVLab/VideoChat2-IT", "dataset:Lin-Chen/ShareGPT4V", "dataset:liuhaotian/LLaVA-Instruct-150K", "arxiv:2406.07476", "arxiv:2306.02858", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
visual-question-answering
"2024-06-11T13:19:34Z"
--- license: apache-2.0 datasets: - OpenGVLab/VideoChat2-IT - Lin-Chen/ShareGPT4V - liuhaotian/LLaVA-Instruct-150K language: - en metrics: - accuracy library_name: transformers pipeline_tag: visual-question-answering tags: - multimodal large language model - large video-language model --- <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63913b120cf6b11c487ca31d/ROs4bHIp4zJ7g7vzgUycu.png" width="150" style="margin-bottom: 0.2;"/> <p> <h3 align="center"><a href="https://arxiv.org/abs/2406.07476">VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs</a></h3> <h5 align="center"> If you like our project, please give us a star ⭐ on <a href="https://github.com/DAMO-NLP-SG/VideoLLaMA2">Github</a> for the latest update. </h2> <p align="center"><video src="https://cdn-uploads.huggingface.co/production/uploads/63913b120cf6b11c487ca31d/Wj7GuqQ0CB9JRoPo6_GoH.webm" width="800"></p> ## 📰 News * **[2024.06.12]** Release model weights and the first version of the technical report of VideoLLaMA 2. * **[2024.06.03]** Release training, evaluation, and serving codes of VideoLLaMA 2. ## 🌎 Model Zoo | Model Name | Type | Visual Encoder | Language Decoder | # Training Frames | |:-------------------|:--------------:|:----------------|:------------------|:----------------------:| | [VideoLLaMA2-7B-Base](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-Base) (This checkpoint) | Base | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 8 | | [VideoLLaMA2-7B](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B) | Chat | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 8 | | [VideoLLaMA2-7B-16F-Base](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-16F-Base) | Base | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 16 | | [VideoLLaMA2-7B-16F](https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2-7B-16F) | Chat | [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 16 | ## 🚀 Main Results ### Multi-Choice Video QA & Video Captioning <p><img src="https://github.com/DAMO-NLP-SG/VideoLLaMA2/assets/18526640/9cc4a5ae-d850-4eef-bd51-83688b94698e" width="800" "/></p> ### Open-Ended Video QA <p><img src="https://github.com/DAMO-NLP-SG/VideoLLaMA2/assets/18526640/2ed7aa53-db56-4829-8375-85aefbc5120a" width="800" "/></p> ## 🤖 Inference with VideoLLaMA2 ```python import torch import transformers import sys sys.path.append('./') from videollama2.conversation import conv_templates, SeparatorStyle from videollama2.constants import DEFAULT_MMODAL_TOKEN, MMODAL_TOKEN_INDEX from videollama2.mm_utils import get_model_name_from_path, tokenizer_MMODAL_token, KeywordsStoppingCriteria, process_video, process_image from videollama2.model.builder import load_pretrained_model def inference(): # Video Inference paths = ['assets/cat_and_chicken.mp4'] questions = ['What animals are in the video, what are they doing, and how does the video feel?'] # Reply: # The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it. modal_list = ['video'] # Video Inference paths = ['assets/sora.mp4'] questions = ['Please describe this video.'] # Reply: # The video features a series of colorful kites flying in the sky. The kites are first seen flying over trees, and then they are shown flying in the sky. The kites come in various shapes and colors, including red, green, blue, and yellow. The video captures the kites soaring gracefully through the air, with some kites flying higher than others. The sky is clear and blue, and the trees below are lush and green. The kites are the main focus of the video, and their vibrant colors and intricate designs are highlighted against the backdrop of the sky and trees. Overall, the video showcases the beauty and artistry of kite-flying, and it is a delight to watch the kites dance and glide through the air. modal_list = ['video'] # Image Inference paths = ['assets/sora.png'] questions = ['What is the woman wearing, what is she doing, and how does the image feel?'] # Reply: # The woman in the image is wearing a black coat and sunglasses, and she is walking down a rain-soaked city street. The image feels vibrant and lively, with the bright city lights reflecting off the wet pavement, creating a visually appealing atmosphere. The woman's presence adds a sense of style and confidence to the scene, as she navigates the bustling urban environment. modal_list = ['image'] # 1. Initialize the model. model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-Base' model_name = get_model_name_from_path(model_path) tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name) model = model.to('cuda:0') conv_mode = 'llama_2' # 2. Visual preprocess (load & transform image or video). if modal_list[0] == 'video': tensor = process_video(paths[0], processor, model.config.image_aspect_ratio).to(dtype=torch.float16, device='cuda', non_blocking=True) default_mm_token = DEFAULT_MMODAL_TOKEN["VIDEO"] modal_token_index = MMODAL_TOKEN_INDEX["VIDEO"] else: tensor = process_image(paths[0], processor, model.config.image_aspect_ratio)[0].to(dtype=torch.float16, device='cuda', non_blocking=True) default_mm_token = DEFAULT_MMODAL_TOKEN["IMAGE"] modal_token_index = MMODAL_TOKEN_INDEX["IMAGE"] tensor = [tensor] # 3. Text preprocess (tag process & generate prompt). question = default_mm_token + "\n" + questions[0] conv = conv_templates[conv_mode].copy() conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_token_index, return_tensors='pt').unsqueeze(0).to('cuda:0') # 4. Generate a response according to visual signals and prompts. stop_str = conv.sep if conv.sep_style in [SeparatorStyle.SINGLE] else conv.sep2 # keywords = ["<s>", "</s>"] keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images_or_videos=tensor, modal_list=modal_list, do_sample=True, temperature=0.2, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria], ) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True) print(outputs[0]) if __name__ == "__main__": inference() ``` ## Citation If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX: ```bibtex @article{damonlpsg2024videollama2, title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs}, author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong}, journal={arXiv preprint arXiv:2406.07476}, year={2024}, url = {https://arxiv.org/abs/2406.07476} } @article{damonlpsg2023videollama, title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding}, author = {Zhang, Hang and Li, Xin and Bing, Lidong}, journal = {arXiv preprint arXiv:2306.02858}, year = {2023}, url = {https://arxiv.org/abs/2306.02858} } ```
Mathoufle13/maker.V1
Mathoufle13
"2024-07-01T09:05:29Z"
5,158
0
transformers
[ "transformers", "gguf", "llama", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
"2024-07-01T08:37:57Z"
1289.4068 seconds used for training. 21.49 minutes used for training. Peak reserved memory = 9.545 GB. Peak reserved memory for training = 4.018 GB. Peak reserved memory % of max memory = 43.058 %. Peak reserved memory for training % of max memory = 18.125 %. args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 10, # Augmenté le nombre de steps de warmup max_steps = 200, # Augmenté le nombre total de steps learning_rate = 1e-4, # Réduit le taux d'apprentissage fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 42, output_dir = "outputs", ==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num examples = 399 | Num Epochs = 4 O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4 \ / Total batch size = 8 | Total steps = 200 "-____-" Number of trainable parameters = 20,971,520 [200/200 21:17, Epoch 4/4] Step Training Loss 1 2.027900 2 2.008700 3 1.946100 4 1.924700 5 1.995000 6 1.999000 7 1.870100 8 1.891400 9 1.807600 10 1.723200 11 1.665100 12 1.541000 13 1.509100 14 1.416600 15 1.398600 16 1.233200 17 1.172100 18 1.272100 19 1.146000 20 1.179000 21 1.206400 22 1.095400 23 0.937300 24 1.214300 25 1.040200 26 1.183400 27 1.033900 28 0.953100 29 0.935700 30 0.962200 31 0.908900 32 0.924900 33 0.931000 34 1.011300 35 0.951900 36 0.936000 37 0.903000 38 0.906900 39 0.945700 40 0.827000 41 0.931800 42 0.919600 43 0.926900 44 0.932900 45 0.872700 46 0.795200 47 0.888700 48 0.956800 49 1.004200 50 0.859500 51 0.802500 52 0.855400 53 0.885500 54 1.026600 55 0.844100 56 0.879800 57 0.797400 58 0.885300 59 0.842800 60 0.861600 61 0.789100 62 0.861600 63 0.856700 64 0.929200 65 0.782500 66 0.713600 67 0.781000 68 0.765100 69 0.784700 70 0.869500 71 0.742900 72 0.787900 73 0.750800 74 0.931700 75 0.713000 76 0.832100 77 0.928300 78 0.777600 79 0.694000 80 0.835400 81 0.822000 82 0.754600 83 0.813400 84 0.868800 85 0.732400 86 0.803700 87 0.694400 88 0.771300 89 0.864400 90 0.646700 91 0.690800 92 0.695000 93 0.732300 94 0.766900 95 0.864100 96 0.867200 97 0.774300 98 0.797700 99 0.772100 100 0.906700 101 0.693400 102 0.685500 103 0.712200 104 0.678400 105 0.761900 106 0.705300 107 0.775700 108 0.627600 109 0.599300 110 0.615100 111 0.618200 112 0.668700 113 0.699900 114 0.577000 115 0.711600 116 0.692900 117 0.585400 118 0.646400 119 0.569200 120 0.752300 121 0.745000 122 0.690100 123 0.744700 124 0.665800 125 0.866100 126 0.707400 127 0.679300 128 0.591400 129 0.655100 130 0.734000 131 0.637900 132 0.733900 133 0.652500 134 0.685400 135 0.641300 136 0.608200 137 0.754100 138 0.753700 139 0.671000 140 0.767200 141 0.668700 142 0.630300 143 0.734700 144 0.767700 145 0.722200 146 0.694400 147 0.710100 148 0.696300 149 0.612600 150 0.670400 151 0.512900 152 0.675100 153 0.579900 154 0.622900 155 0.652500 156 0.649200 157 0.546700 158 0.521600 159 0.522200 160 0.589400 161 0.552600 162 0.630700 163 0.595600 164 0.614300 165 0.489400 166 0.634500 167 0.620800 168 0.618600 169 0.637900 170 0.553900 171 0.656000 172 0.644000 173 0.694300 174 0.608900 175 0.673000 176 0.612500 177 0.654200 178 0.639200 179 0.599100 180 0.642100 181 0.529700 182 0.614000 183 0.582900 184 0.765100 185 0.502700 186 0.564300 187 0.740200 188 0.636100 189 0.638800 190 0.560100 191 0.620000 192 0.712800 193 0.531000 194 0.591600 195 0.608600 196 0.671800 197 0.572900 198 0.600900 199 0.586800 200 0.545900 --- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** Mathoufle13 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MaziyarPanahi/Meta-Llama-3-70B-Instruct-GPTQ
MaziyarPanahi
"2024-04-19T07:07:49Z"
5,154
17
transformers
[ "transformers", "safetensors", "llama", "text-generation", "finetuned", "quantized", "4-bit", "gptq", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us", "base_model:meta-llama/Meta-Llama-3-70B-Instruct" ]
text-generation
"2024-04-19T02:21:38Z"
--- license_name: llama3 tags: - finetuned - quantized - 4-bit - gptq - transformers - safetensors - llama - text-generation - facebook - meta - pytorch - llama-3 - conversational - en - license:other - autotrain_compatible - endpoints_compatible - has_space - text-generation-inference - region:us model_name: Meta-Llama-3-70B-Instruct-GPTQ base_model: meta-llama/Meta-Llama-3-70B-Instruct inference: false model_creator: meta-llama pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # Description [MaziyarPanahi/Meta-Llama-3-70B-Instruct-GPTQ](https://huggingface.co/MaziyarPanahi/Meta-Llama-3-70B-Instruct-GPTQ) is a quantized (GPTQ) version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) ## How to use ### Install the necessary packages ``` pip install --upgrade accelerate auto-gptq transformers ``` ### Example Python code ```python from transformers import AutoTokenizer, pipeline from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig import torch model_id = "MaziyarPanahi/Meta-Llama-3-70B-Instruct-GPTQ" quantize_config = BaseQuantizeConfig( bits=4, group_size=128, desc_act=False ) model = AutoGPTQForCausalLM.from_quantized( model_id, use_safetensors=True, device="cuda:0", quantize_config=quantize_config) tokenizer = AutoTokenizer.from_pretrained(model_id) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.1 ) outputs = pipe("What is a large language model?") print(outputs[0]["generated_text"]) ```
duyntnet/Llama-3-8B-Synthia-v3.5-imatrix-GGUF
duyntnet
"2024-06-06T02:38:19Z"
5,154
0
transformers
[ "transformers", "gguf", "imatrix", "Llama-3-8B-Synthia-v3.5", "text-generation", "en", "license:other", "region:us" ]
text-generation
"2024-06-05T22:46:49Z"
--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - Llama-3-8B-Synthia-v3.5 --- Quantizations of https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5 # From original readme ## Sample code to run inference ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "/home/migel/Tess-2.0-Llama-3-8B" output_file_path = "/home/migel/conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, trust_remote_code=False, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, pad_token_id=tokenizer.eos_token_id, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f"{string}" conversation = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are Synthia, a helful, female AI assitant. You always provide detailed answers without hesitation.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n""" while True: user_input = input("You: ") llm_prompt = f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n" json_data = {"prompt": user_input, "answer": answer} with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```
AlekseyElygin/Starling-LM-7B-beta-GGUF
AlekseyElygin
"2024-06-27T06:33:52Z"
5,153
0
transformers
[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/starling-lm-7b-beta-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-26T13:41:59Z"
--- base_model: unsloth/starling-lm-7b-beta-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - **Developed by:** AlekseyElygin - **License:** apache-2.0 - **Finetuned from model :** unsloth/starling-lm-7b-beta-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF
mradermacher
"2024-06-05T08:44:41Z"
5,152
0
transformers
[ "transformers", "gguf", "alignment-handbook", "trl", "sft", "generated_from_trainer", "en", "dataset:arcee-ai/MyAlee-Education-Instructions-V2", "base_model:arcee-ai/MyAlee-Mistral-Instruct-v2-32k-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-04T05:51:15Z"
--- base_model: arcee-ai/MyAlee-Mistral-Instruct-v2-32k-v2 datasets: - arcee-ai/MyAlee-Education-Instructions-V2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/arcee-ai/MyAlee-Mistral-Instruct-v2-32k-v2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/MyAlee-Mistral-Instruct-v2-32k-v2-i1-GGUF/resolve/main/MyAlee-Mistral-Instruct-v2-32k-v2.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF
mradermacher
"2024-06-06T21:48:10Z"
5,150
1
transformers
[ "transformers", "gguf", "Llama-3", "instruct", "finetune", "chatml", "axolotl", "roleplay", "en", "base_model:Gryphe/Pantheon-RP-1.0-8b-Llama-3", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-06T10:59:59Z"
--- base_model: Gryphe/Pantheon-RP-1.0-8b-Llama-3 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - Llama-3 - instruct - finetune - chatml - axolotl - roleplay --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Gryphe/Pantheon-RP-1.0-8b-Llama-3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Pantheon-RP-1.0-8b-Llama-3-i1-GGUF/resolve/main/Pantheon-RP-1.0-8b-Llama-3.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
TeeZee/Bielik-SOLAR-LIKE-10.7B-Instruct-v0.1
TeeZee
"2024-04-11T18:36:37Z"
5,146
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-04-10T19:00:33Z"
--- license: cc-by-nc-4.0 --- ### TeeZee/Bielik-SOLAR-LIKE-10.7B-Instruct-v0.1 ### Precise recipe used by Upstage to create [SOLAR](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) was applied to https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1 *(just merge, no finetuning) ### Results ### - model is still coherent in Polish language, even without finetuning after merge - instruct mode works in ooba without issues - model is censored and aligned - seems that this model scores highest amongst all versions of original Bielik models, further finetunig should improve results even more. ![imgage/png](https://huggingface.co/TeeZee/Bielik-SOLAR-LIKE-10.7B-Instruct-v0.1/resolve/main/OpenLLMLeaderboard_results.png) - on dedicated to Polish speaking LLM leaderboards, its 2nd, just behind instruct version used for this merge, and thats to be expected when applying DUS merge - very small quality loss. [Polish LLMs leaderboards](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) - overall it seems like a good base for further finetunig in Polish language.
cointegrated/rubert-base-cased-nli-twoway
cointegrated
"2023-10-06T11:57:41Z"
5,144
4
transformers
[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "rubert", "russian", "nli", "rte", "zero-shot-classification", "ru", "dataset:cointegrated/nli-rus-translated-v2021", "autotrain_compatible", "endpoints_compatible", "region:us" ]
zero-shot-classification
"2022-03-02T23:29:05Z"
--- language: ru pipeline_tag: zero-shot-classification tags: - rubert - russian - nli - rte - zero-shot-classification widget: - text: Я хочу поехать в Австралию candidate_labels: спорт,путешествия,музыка,кино,книги,наука,политика hypothesis_template: Тема текста - {}. datasets: - cointegrated/nli-rus-translated-v2021 --- # RuBERT for NLI (natural language inference) This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment or not entailment. For more details, see the card for a similar model: https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway
mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF
mradermacher
"2024-06-20T16:04:41Z"
5,144
1
transformers
[ "transformers", "gguf", "en", "base_model:deepseek-ai/DeepSeek-Coder-V2-Base", "license:other", "endpoints_compatible", "region:us" ]
null
"2024-06-18T15:32:17Z"
--- base_model: deepseek-ai/DeepSeek-Coder-V2-Base language: - en library_name: transformers license: other license_link: LICENSE license_name: deepseek-license quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 47.5 | for the desperate | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ1_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ1_M.gguf.part2of2) | i1-IQ1_M | 52.8 | mostly desperate | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_XXS.gguf.part2of2) | i1-IQ2_XXS | 61.6 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_XS.gguf.part2of2) | i1-IQ2_XS | 68.8 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_S.gguf.part2of2) | i1-IQ2_S | 70.0 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ2_M.gguf.part2of2) | i1-IQ2_M | 77.0 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 86.0 | IQ3_XXS probably better | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 90.9 | lower quality | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 96.4 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_S.gguf.part3of3) | i1-IQ3_S | 101.8 | beats Q3_K* | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_S.gguf.part3of3) | i1-Q3_K_S | 101.8 | IQ3_XS probably better | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ3_M.gguf.part3of3) | i1-IQ3_M | 103.5 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_M.gguf.part3of3) | i1-Q3_K_M | 112.8 | IQ3_S probably better | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_L.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_L.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q3_K_L.gguf.part3of3) | i1-Q3_K_L | 122.5 | IQ3_M probably better | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ4_XS.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ4_XS.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-IQ4_XS.gguf.part3of3) | i1-IQ4_XS | 125.7 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_0.gguf.part3of3) | i1-Q4_0 | 133.5 | fast, low quality | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_K_S.gguf.part3of3) | i1-Q4_K_S | 134.0 | optimal size/speed/quality | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q4_K_M.gguf.part3of3) | i1-Q4_K_M | 142.6 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_S.gguf.part4of4) | i1-Q5_K_S | 162.4 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q5_K_M.gguf.part4of4) | i1-Q5_K_M | 167.3 | | | [PART 1](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q6_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q6_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q6_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Base-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Base.i1-Q6_K.gguf.part4of4) | i1-Q6_K | 193.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mradermacher/NeuralKuno-7B-slerp-i1-GGUF
mradermacher
"2024-06-16T14:19:08Z"
5,143
0
transformers
[ "transformers", "gguf", "en", "base_model:WesPro/NeuralKuno-7B-slerp", "endpoints_compatible", "region:us" ]
null
"2024-06-16T09:53:06Z"
--- base_model: WesPro/NeuralKuno-7B-slerp language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/WesPro/NeuralKuno-7B-slerp <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/NeuralKuno-7B-slerp-i1-GGUF/resolve/main/NeuralKuno-7B-slerp.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mlabonne/Daredevil-8B-abliterated
mlabonne
"2024-05-29T14:23:30Z"
5,142
24
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-05-26T14:32:53Z"
--- library_name: transformers license: other --- # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## 🔎 Applications This is an uncensored model. You can use it for any application that doesn't require alignment, like role-playing. Tested on LM Studio using the "Llama 3" preset. ## ⚡ Quantization * **GGUF**: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Open LLM Leaderboard Daredevil-8B-abliterated is the second best-performing 8B model on the Open LLM Leaderboard in terms of MMLU score (27 May 24). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png) ### Nous Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). | Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench | |---|---:|---:|---:|---:|---:| | [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) | 55.87 | 44.13 | 73.52 | 59.05 | 46.77 | | [**mlabonne/Daredevil-8B-abliterated**](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) | **55.06** | **43.29** | **73.33** | **57.47** | **46.17** | | [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) | 52.26 | 41.6 | 69.95 | 54.22 | 43.26 | | [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 | | [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) | 51.21 | 40.23 | 69.5 | 52.44 | 42.69 | | [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) | 48.63 | 34.17 | 70.59 | 52.39 | 37.36 | | [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 | ## 🌳 Model family tree ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Daredevil-8B-abliterated" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
mradermacher/Trion-M-7b-i1-GGUF
mradermacher
"2024-06-10T23:52:18Z"
5,141
0
transformers
[ "transformers", "gguf", "Mistral", "en", "base_model:BlueNipples/Trion-M-7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-10T17:49:36Z"
--- base_model: BlueNipples/Trion-M-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - Mistral --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/BlueNipples/Trion-M-7b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Trion-M-7b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Trion-M-7b-i1-GGUF/resolve/main/Trion-M-7b.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
timm/convnext_nano.in12k_ft_in1k
timm
"2024-02-10T23:27:13Z"
5,139
1
timm
[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "dataset:imagenet-12k", "arxiv:2201.03545", "license:apache-2.0", "region:us" ]
image-classification
"2022-12-13T07:12:21Z"
--- license: apache-2.0 library_name: timm tags: - image-classification - timm datasets: - imagenet-1k - imagenet-12k --- # Model card for convnext_nano.in12k_ft_in1k A ConvNeXt image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. Fine-tuning performed on 8x GPU [Lambda Labs](https://lambdalabs.com/) cloud instances. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 15.6 - GMACs: 2.5 - Activations (M): 8.4 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/huggingface/pytorch-image-models - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('convnext_nano.in12k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'convnext_nano.in12k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 80, 56, 56]) # torch.Size([1, 160, 28, 28]) # torch.Size([1, 320, 14, 14]) # torch.Size([1, 640, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'convnext_nano.in12k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 640, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. | model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size| |------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| | [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 | | [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 | | [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 | | [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 | | [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 | | [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 | | [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 | | [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 | | [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 | | [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 | | [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 | | [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 | | [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 | | [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 | | [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 | | [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 | | [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 | | [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 | | [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 | | [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 | | [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 | | [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 | | [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 | | [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 | | [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 | | [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 | | [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 | | [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 | | [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 | | [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 | | [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 | | [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 | | [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 | | [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 | | [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 | | [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 | | [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 | | [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 | | [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 | | [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 | | [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 | | [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 | | [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 | | [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 | | [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 | | [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 | | [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 | | [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 | | [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 | | [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 | | [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 | | [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 | | [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 | | [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 | ## Citation ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ```
redponike/Prox-Llama-3-8B-abliterated-GGUF
redponike
"2024-06-21T06:28:39Z"
5,135
0
null
[ "gguf", "region:us" ]
null
"2024-06-20T19:10:33Z"
GGUF quants of [openvoid/Prox-Llama-3-8B-abliterated](https://huggingface.co/openvoid/Prox-Llama-3-8B-abliterated)
andersonbcdefg/bge-small-4096
andersonbcdefg
"2023-11-02T05:58:37Z"
5,134
10
transformers
[ "transformers", "pytorch", "onnx", "bert", "feature-extraction", "mteb", "model-index", "endpoints_compatible", "text-embeddings-inference", "region:us" ]
feature-extraction
"2023-10-29T00:52:52Z"
--- tags: - mteb model-index: - name: andersonbcdefg/bge-small-4096 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 68.74626865671641 - type: ap value: 31.113961861085855 - type: f1 value: 62.628656720790275 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 81.30347499999999 - type: ap value: 76.05639977935193 - type: f1 value: 81.23180016825499 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.566 - type: f1 value: 38.014543974125615 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 29.445 - type: map_at_10 value: 44.157999999999994 - type: map_at_100 value: 45.169 - type: map_at_1000 value: 45.178000000000004 - type: map_at_3 value: 39.545 - type: map_at_5 value: 42.233 - type: mrr_at_1 value: 29.445 - type: mrr_at_10 value: 44.157999999999994 - type: mrr_at_100 value: 45.169 - type: mrr_at_1000 value: 45.178000000000004 - type: mrr_at_3 value: 39.545 - type: mrr_at_5 value: 42.233 - type: ndcg_at_1 value: 29.445 - type: ndcg_at_10 value: 52.446000000000005 - type: ndcg_at_100 value: 56.782 - type: ndcg_at_1000 value: 56.989999999999995 - type: ndcg_at_3 value: 42.935 - type: ndcg_at_5 value: 47.833999999999996 - type: precision_at_1 value: 29.445 - type: precision_at_10 value: 7.8950000000000005 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 17.591 - type: precision_at_5 value: 12.959000000000001 - type: recall_at_1 value: 29.445 - type: recall_at_10 value: 78.947 - type: recall_at_100 value: 97.937 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 52.774 - type: recall_at_5 value: 64.794 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 43.85187820924144 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.5939502757938 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.539409343284674 - type: mrr value: 71.58982983775228 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.31440765254087 - type: cos_sim_spearman value: 81.59884723689632 - type: euclidean_pearson value: 80.65818473893147 - type: euclidean_spearman value: 81.40004752638717 - type: manhattan_pearson value: 80.52256901536644 - type: manhattan_spearman value: 80.57292024599603 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 79.98376623376623 - type: f1 value: 79.91981901371503 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.79541356345093 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 26.760513681350375 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.794 - type: map_at_10 value: 33.361000000000004 - type: map_at_100 value: 34.86 - type: map_at_1000 value: 35.0 - type: map_at_3 value: 30.579 - type: map_at_5 value: 31.996000000000002 - type: mrr_at_1 value: 30.186 - type: mrr_at_10 value: 39.681 - type: mrr_at_100 value: 40.616 - type: mrr_at_1000 value: 40.669 - type: mrr_at_3 value: 37.244 - type: mrr_at_5 value: 38.588 - type: ndcg_at_1 value: 30.186 - type: ndcg_at_10 value: 39.34 - type: ndcg_at_100 value: 45.266 - type: ndcg_at_1000 value: 47.9 - type: ndcg_at_3 value: 35.164 - type: ndcg_at_5 value: 36.854 - type: precision_at_1 value: 30.186 - type: precision_at_10 value: 7.639 - type: precision_at_100 value: 1.328 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 17.31 - type: precision_at_5 value: 12.275 - type: recall_at_1 value: 23.794 - type: recall_at_10 value: 50.463 - type: recall_at_100 value: 75.268 - type: recall_at_1000 value: 93.138 - type: recall_at_3 value: 37.797 - type: recall_at_5 value: 42.985 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.968999999999998 - type: map_at_10 value: 23.846999999999998 - type: map_at_100 value: 24.712999999999997 - type: map_at_1000 value: 24.833 - type: map_at_3 value: 22.024 - type: map_at_5 value: 23.087 - type: mrr_at_1 value: 22.038 - type: mrr_at_10 value: 27.808 - type: mrr_at_100 value: 28.532999999999998 - type: mrr_at_1000 value: 28.604000000000003 - type: mrr_at_3 value: 26.029999999999998 - type: mrr_at_5 value: 27.122 - type: ndcg_at_1 value: 22.038 - type: ndcg_at_10 value: 27.559 - type: ndcg_at_100 value: 31.541999999999998 - type: ndcg_at_1000 value: 34.343 - type: ndcg_at_3 value: 24.585 - type: ndcg_at_5 value: 26.026 - type: precision_at_1 value: 22.038 - type: precision_at_10 value: 5.019 - type: precision_at_100 value: 0.8920000000000001 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.423 - type: precision_at_5 value: 8.28 - type: recall_at_1 value: 17.968999999999998 - type: recall_at_10 value: 34.583000000000006 - type: recall_at_100 value: 51.849000000000004 - type: recall_at_1000 value: 70.832 - type: recall_at_3 value: 26.057000000000002 - type: recall_at_5 value: 29.816 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.183999999999997 - type: map_at_10 value: 40.245 - type: map_at_100 value: 41.324 - type: map_at_1000 value: 41.402 - type: map_at_3 value: 37.395 - type: map_at_5 value: 38.964999999999996 - type: mrr_at_1 value: 33.981 - type: mrr_at_10 value: 43.471 - type: mrr_at_100 value: 44.303 - type: mrr_at_1000 value: 44.352999999999994 - type: mrr_at_3 value: 41.149 - type: mrr_at_5 value: 42.466 - type: ndcg_at_1 value: 33.981 - type: ndcg_at_10 value: 45.776 - type: ndcg_at_100 value: 50.441 - type: ndcg_at_1000 value: 52.16 - type: ndcg_at_3 value: 40.756 - type: ndcg_at_5 value: 43.132 - type: precision_at_1 value: 33.981 - type: precision_at_10 value: 7.617999999999999 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 18.558 - type: precision_at_5 value: 12.915 - type: recall_at_1 value: 29.183999999999997 - type: recall_at_10 value: 59.114 - type: recall_at_100 value: 79.549 - type: recall_at_1000 value: 91.925 - type: recall_at_3 value: 45.551 - type: recall_at_5 value: 51.38399999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.286 - type: map_at_10 value: 27.143 - type: map_at_100 value: 28.107 - type: map_at_1000 value: 28.212 - type: map_at_3 value: 25.149 - type: map_at_5 value: 26.179999999999996 - type: mrr_at_1 value: 22.034000000000002 - type: mrr_at_10 value: 28.875 - type: mrr_at_100 value: 29.785 - type: mrr_at_1000 value: 29.876 - type: mrr_at_3 value: 27.023999999999997 - type: mrr_at_5 value: 28.058 - type: ndcg_at_1 value: 22.034000000000002 - type: ndcg_at_10 value: 31.148999999999997 - type: ndcg_at_100 value: 35.936 - type: ndcg_at_1000 value: 38.682 - type: ndcg_at_3 value: 27.230999999999998 - type: ndcg_at_5 value: 29.034 - type: precision_at_1 value: 22.034000000000002 - type: precision_at_10 value: 4.836 - type: precision_at_100 value: 0.754 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 11.562999999999999 - type: precision_at_5 value: 8.068 - type: recall_at_1 value: 20.286 - type: recall_at_10 value: 41.827999999999996 - type: recall_at_100 value: 63.922000000000004 - type: recall_at_1000 value: 84.639 - type: recall_at_3 value: 31.227 - type: recall_at_5 value: 35.546 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.488 - type: map_at_10 value: 18.595 - type: map_at_100 value: 19.783 - type: map_at_1000 value: 19.918 - type: map_at_3 value: 16.274 - type: map_at_5 value: 17.558 - type: mrr_at_1 value: 16.791 - type: mrr_at_10 value: 22.53 - type: mrr_at_100 value: 23.651 - type: mrr_at_1000 value: 23.738999999999997 - type: mrr_at_3 value: 20.232 - type: mrr_at_5 value: 21.644 - type: ndcg_at_1 value: 16.791 - type: ndcg_at_10 value: 22.672 - type: ndcg_at_100 value: 28.663 - type: ndcg_at_1000 value: 31.954 - type: ndcg_at_3 value: 18.372 - type: ndcg_at_5 value: 20.47 - type: precision_at_1 value: 16.791 - type: precision_at_10 value: 4.2540000000000004 - type: precision_at_100 value: 0.8370000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 8.706 - type: precision_at_5 value: 6.666999999999999 - type: recall_at_1 value: 13.488 - type: recall_at_10 value: 31.451 - type: recall_at_100 value: 58.085 - type: recall_at_1000 value: 81.792 - type: recall_at_3 value: 19.811 - type: recall_at_5 value: 24.973 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.436 - type: map_at_10 value: 29.105999999999998 - type: map_at_100 value: 30.442000000000004 - type: map_at_1000 value: 30.567 - type: map_at_3 value: 26.430999999999997 - type: map_at_5 value: 27.866000000000003 - type: mrr_at_1 value: 26.083000000000002 - type: mrr_at_10 value: 33.975 - type: mrr_at_100 value: 35.014 - type: mrr_at_1000 value: 35.07 - type: mrr_at_3 value: 31.649 - type: mrr_at_5 value: 32.944 - type: ndcg_at_1 value: 26.083000000000002 - type: ndcg_at_10 value: 34.229 - type: ndcg_at_100 value: 40.439 - type: ndcg_at_1000 value: 43.081 - type: ndcg_at_3 value: 29.64 - type: ndcg_at_5 value: 31.704 - type: precision_at_1 value: 26.083000000000002 - type: precision_at_10 value: 6.246 - type: precision_at_100 value: 1.1199999999999999 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 13.858999999999998 - type: precision_at_5 value: 10.01 - type: recall_at_1 value: 21.436 - type: recall_at_10 value: 44.938 - type: recall_at_100 value: 72.029 - type: recall_at_1000 value: 90.009 - type: recall_at_3 value: 31.954 - type: recall_at_5 value: 37.303 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.217 - type: map_at_10 value: 25.16 - type: map_at_100 value: 26.490000000000002 - type: map_at_1000 value: 26.619 - type: map_at_3 value: 22.926 - type: map_at_5 value: 24.251 - type: mrr_at_1 value: 22.831000000000003 - type: mrr_at_10 value: 30.009000000000004 - type: mrr_at_100 value: 31.045 - type: mrr_at_1000 value: 31.122 - type: mrr_at_3 value: 28.025 - type: mrr_at_5 value: 29.07 - type: ndcg_at_1 value: 22.831000000000003 - type: ndcg_at_10 value: 29.664 - type: ndcg_at_100 value: 35.900999999999996 - type: ndcg_at_1000 value: 38.932 - type: ndcg_at_3 value: 26.051000000000002 - type: ndcg_at_5 value: 27.741 - type: precision_at_1 value: 22.831000000000003 - type: precision_at_10 value: 5.479 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 12.481 - type: precision_at_5 value: 8.973 - type: recall_at_1 value: 18.217 - type: recall_at_10 value: 38.336 - type: recall_at_100 value: 65.854 - type: recall_at_1000 value: 87.498 - type: recall_at_3 value: 28.158 - type: recall_at_5 value: 32.841 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.100666666666665 - type: map_at_10 value: 26.22883333333333 - type: map_at_100 value: 27.34241666666667 - type: map_at_1000 value: 27.468416666666666 - type: map_at_3 value: 23.953916666666668 - type: map_at_5 value: 25.20125 - type: mrr_at_1 value: 22.729249999999997 - type: mrr_at_10 value: 29.86491666666667 - type: mrr_at_100 value: 30.76925 - type: mrr_at_1000 value: 30.846333333333337 - type: mrr_at_3 value: 27.733999999999998 - type: mrr_at_5 value: 28.94058333333333 - type: ndcg_at_1 value: 22.729249999999997 - type: ndcg_at_10 value: 30.708250000000003 - type: ndcg_at_100 value: 35.89083333333333 - type: ndcg_at_1000 value: 38.75891666666666 - type: ndcg_at_3 value: 26.661083333333334 - type: ndcg_at_5 value: 28.54 - type: precision_at_1 value: 22.729249999999997 - type: precision_at_10 value: 5.433833333333333 - type: precision_at_100 value: 0.9486666666666665 - type: precision_at_1000 value: 0.13808333333333334 - type: precision_at_3 value: 12.292166666666668 - type: precision_at_5 value: 8.825 - type: recall_at_1 value: 19.100666666666665 - type: recall_at_10 value: 40.54208333333334 - type: recall_at_100 value: 63.67975 - type: recall_at_1000 value: 84.13574999999999 - type: recall_at_3 value: 29.311000000000003 - type: recall_at_5 value: 34.1105 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.762 - type: map_at_10 value: 23.905 - type: map_at_100 value: 24.663 - type: map_at_1000 value: 24.765 - type: map_at_3 value: 22.032 - type: map_at_5 value: 23.025000000000002 - type: mrr_at_1 value: 20.244999999999997 - type: mrr_at_10 value: 26.162999999999997 - type: mrr_at_100 value: 26.907999999999998 - type: mrr_at_1000 value: 26.987 - type: mrr_at_3 value: 24.361 - type: mrr_at_5 value: 25.326999999999998 - type: ndcg_at_1 value: 20.244999999999997 - type: ndcg_at_10 value: 27.577 - type: ndcg_at_100 value: 31.473000000000003 - type: ndcg_at_1000 value: 34.217999999999996 - type: ndcg_at_3 value: 24.092 - type: ndcg_at_5 value: 25.657000000000004 - type: precision_at_1 value: 20.244999999999997 - type: precision_at_10 value: 4.433 - type: precision_at_100 value: 0.692 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 10.634 - type: precision_at_5 value: 7.362 - type: recall_at_1 value: 17.762 - type: recall_at_10 value: 36.661 - type: recall_at_100 value: 54.581999999999994 - type: recall_at_1000 value: 75.28099999999999 - type: recall_at_3 value: 27.084999999999997 - type: recall_at_5 value: 31.064999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 12.998000000000001 - type: map_at_10 value: 18.926000000000002 - type: map_at_100 value: 19.836000000000002 - type: map_at_1000 value: 19.96 - type: map_at_3 value: 16.932 - type: map_at_5 value: 17.963 - type: mrr_at_1 value: 15.692 - type: mrr_at_10 value: 22.206 - type: mrr_at_100 value: 23.021 - type: mrr_at_1000 value: 23.108999999999998 - type: mrr_at_3 value: 20.114 - type: mrr_at_5 value: 21.241 - type: ndcg_at_1 value: 15.692 - type: ndcg_at_10 value: 22.997999999999998 - type: ndcg_at_100 value: 27.541 - type: ndcg_at_1000 value: 30.758000000000003 - type: ndcg_at_3 value: 19.117 - type: ndcg_at_5 value: 20.778 - type: precision_at_1 value: 15.692 - type: precision_at_10 value: 4.277 - type: precision_at_100 value: 0.774 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 9.027000000000001 - type: precision_at_5 value: 6.641 - type: recall_at_1 value: 12.998000000000001 - type: recall_at_10 value: 32.135999999999996 - type: recall_at_100 value: 52.937 - type: recall_at_1000 value: 76.348 - type: recall_at_3 value: 21.292 - type: recall_at_5 value: 25.439 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.219 - type: map_at_10 value: 27.306 - type: map_at_100 value: 28.337 - type: map_at_1000 value: 28.459 - type: map_at_3 value: 25.423000000000002 - type: map_at_5 value: 26.375999999999998 - type: mrr_at_1 value: 23.787 - type: mrr_at_10 value: 30.977 - type: mrr_at_100 value: 31.85 - type: mrr_at_1000 value: 31.939 - type: mrr_at_3 value: 29.073 - type: mrr_at_5 value: 30.095 - type: ndcg_at_1 value: 23.787 - type: ndcg_at_10 value: 31.615 - type: ndcg_at_100 value: 36.641 - type: ndcg_at_1000 value: 39.707 - type: ndcg_at_3 value: 27.994000000000003 - type: ndcg_at_5 value: 29.508000000000003 - type: precision_at_1 value: 23.787 - type: precision_at_10 value: 5.271 - type: precision_at_100 value: 0.865 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 12.748999999999999 - type: precision_at_5 value: 8.806 - type: recall_at_1 value: 20.219 - type: recall_at_10 value: 41.108 - type: recall_at_100 value: 63.596 - type: recall_at_1000 value: 85.54899999999999 - type: recall_at_3 value: 31.129 - type: recall_at_5 value: 34.845 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.949 - type: map_at_10 value: 26.629 - type: map_at_100 value: 28.006999999999998 - type: map_at_1000 value: 28.221 - type: map_at_3 value: 24.099999999999998 - type: map_at_5 value: 25.487 - type: mrr_at_1 value: 24.111 - type: mrr_at_10 value: 30.592000000000002 - type: mrr_at_100 value: 31.448999999999998 - type: mrr_at_1000 value: 31.538 - type: mrr_at_3 value: 28.128999999999998 - type: mrr_at_5 value: 29.503 - type: ndcg_at_1 value: 24.111 - type: ndcg_at_10 value: 31.373 - type: ndcg_at_100 value: 36.897999999999996 - type: ndcg_at_1000 value: 40.288000000000004 - type: ndcg_at_3 value: 26.895000000000003 - type: ndcg_at_5 value: 29.009 - type: precision_at_1 value: 24.111 - type: precision_at_10 value: 6.067 - type: precision_at_100 value: 1.269 - type: precision_at_1000 value: 0.22 - type: precision_at_3 value: 12.385 - type: precision_at_5 value: 9.249 - type: recall_at_1 value: 19.949 - type: recall_at_10 value: 40.394000000000005 - type: recall_at_100 value: 65.812 - type: recall_at_1000 value: 88.247 - type: recall_at_3 value: 28.116000000000003 - type: recall_at_5 value: 33.4 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.905999999999999 - type: map_at_10 value: 20.523 - type: map_at_100 value: 21.547 - type: map_at_1000 value: 21.665 - type: map_at_3 value: 18.182000000000002 - type: map_at_5 value: 19.661 - type: mrr_at_1 value: 14.972 - type: mrr_at_10 value: 22.092 - type: mrr_at_100 value: 23.055999999999997 - type: mrr_at_1000 value: 23.150000000000002 - type: mrr_at_3 value: 19.778000000000002 - type: mrr_at_5 value: 21.229 - type: ndcg_at_1 value: 14.972 - type: ndcg_at_10 value: 24.547 - type: ndcg_at_100 value: 29.948999999999998 - type: ndcg_at_1000 value: 33.084 - type: ndcg_at_3 value: 20.036 - type: ndcg_at_5 value: 22.567 - type: precision_at_1 value: 14.972 - type: precision_at_10 value: 4.067 - type: precision_at_100 value: 0.743 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 8.811 - type: precision_at_5 value: 6.654 - type: recall_at_1 value: 13.905999999999999 - type: recall_at_10 value: 35.493 - type: recall_at_100 value: 60.67399999999999 - type: recall_at_1000 value: 84.371 - type: recall_at_3 value: 23.555 - type: recall_at_5 value: 29.729 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 7.529 - type: map_at_10 value: 12.794 - type: map_at_100 value: 14.315 - type: map_at_1000 value: 14.523 - type: map_at_3 value: 10.367999999999999 - type: map_at_5 value: 11.546 - type: mrr_at_1 value: 16.872999999999998 - type: mrr_at_10 value: 25.709 - type: mrr_at_100 value: 26.907999999999998 - type: mrr_at_1000 value: 26.962000000000003 - type: mrr_at_3 value: 22.486 - type: mrr_at_5 value: 24.245 - type: ndcg_at_1 value: 16.872999999999998 - type: ndcg_at_10 value: 19.005 - type: ndcg_at_100 value: 25.990999999999996 - type: ndcg_at_1000 value: 29.955 - type: ndcg_at_3 value: 14.573 - type: ndcg_at_5 value: 16.118 - type: precision_at_1 value: 16.872999999999998 - type: precision_at_10 value: 6.235 - type: precision_at_100 value: 1.374 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 10.793 - type: precision_at_5 value: 8.73 - type: recall_at_1 value: 7.529 - type: recall_at_10 value: 24.007 - type: recall_at_100 value: 48.742000000000004 - type: recall_at_1000 value: 71.35000000000001 - type: recall_at_3 value: 13.467 - type: recall_at_5 value: 17.502000000000002 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 5.614 - type: map_at_10 value: 11.42 - type: map_at_100 value: 15.873000000000001 - type: map_at_1000 value: 17.021 - type: map_at_3 value: 8.495 - type: map_at_5 value: 9.790000000000001 - type: mrr_at_1 value: 42.0 - type: mrr_at_10 value: 52.477 - type: mrr_at_100 value: 53.095000000000006 - type: mrr_at_1000 value: 53.135 - type: mrr_at_3 value: 49.833 - type: mrr_at_5 value: 51.183 - type: ndcg_at_1 value: 31.374999999999996 - type: ndcg_at_10 value: 25.27 - type: ndcg_at_100 value: 29.709999999999997 - type: ndcg_at_1000 value: 36.975 - type: ndcg_at_3 value: 27.688000000000002 - type: ndcg_at_5 value: 25.987 - type: precision_at_1 value: 42.0 - type: precision_at_10 value: 21.2 - type: precision_at_100 value: 7.053 - type: precision_at_1000 value: 1.512 - type: precision_at_3 value: 32.333 - type: precision_at_5 value: 26.6 - type: recall_at_1 value: 5.614 - type: recall_at_10 value: 16.112000000000002 - type: recall_at_100 value: 36.165000000000006 - type: recall_at_1000 value: 60.362 - type: recall_at_3 value: 9.761000000000001 - type: recall_at_5 value: 12.279 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 40.085 - type: f1 value: 35.53934111316537 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 34.185 - type: map_at_10 value: 44.491 - type: map_at_100 value: 45.204 - type: map_at_1000 value: 45.254 - type: map_at_3 value: 42.006 - type: map_at_5 value: 43.516 - type: mrr_at_1 value: 37.024 - type: mrr_at_10 value: 47.524 - type: mrr_at_100 value: 48.185 - type: mrr_at_1000 value: 48.227 - type: mrr_at_3 value: 45.086999999999996 - type: mrr_at_5 value: 46.575 - type: ndcg_at_1 value: 37.024 - type: ndcg_at_10 value: 50.126000000000005 - type: ndcg_at_100 value: 53.577 - type: ndcg_at_1000 value: 54.906 - type: ndcg_at_3 value: 45.25 - type: ndcg_at_5 value: 47.842 - type: precision_at_1 value: 37.024 - type: precision_at_10 value: 7.132 - type: precision_at_100 value: 0.898 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 18.767 - type: precision_at_5 value: 12.676000000000002 - type: recall_at_1 value: 34.185 - type: recall_at_10 value: 64.703 - type: recall_at_100 value: 80.58 - type: recall_at_1000 value: 90.742 - type: recall_at_3 value: 51.483000000000004 - type: recall_at_5 value: 57.775 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 9.358 - type: map_at_10 value: 16.391 - type: map_at_100 value: 17.698 - type: map_at_1000 value: 17.912 - type: map_at_3 value: 13.831 - type: map_at_5 value: 15.187000000000001 - type: mrr_at_1 value: 18.673000000000002 - type: mrr_at_10 value: 26.907999999999998 - type: mrr_at_100 value: 27.842 - type: mrr_at_1000 value: 27.933000000000003 - type: mrr_at_3 value: 24.486 - type: mrr_at_5 value: 25.766 - type: ndcg_at_1 value: 18.673000000000002 - type: ndcg_at_10 value: 22.137 - type: ndcg_at_100 value: 28.126 - type: ndcg_at_1000 value: 32.489000000000004 - type: ndcg_at_3 value: 18.723 - type: ndcg_at_5 value: 19.858 - type: precision_at_1 value: 18.673000000000002 - type: precision_at_10 value: 6.389 - type: precision_at_100 value: 1.262 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 12.757 - type: precision_at_5 value: 9.753 - type: recall_at_1 value: 9.358 - type: recall_at_10 value: 28.605000000000004 - type: recall_at_100 value: 51.713 - type: recall_at_1000 value: 78.408 - type: recall_at_3 value: 17.674 - type: recall_at_5 value: 21.97 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 22.997999999999998 - type: map_at_10 value: 32.957 - type: map_at_100 value: 33.972 - type: map_at_1000 value: 34.072 - type: map_at_3 value: 30.44 - type: map_at_5 value: 31.869999999999997 - type: mrr_at_1 value: 45.995999999999995 - type: mrr_at_10 value: 54.473000000000006 - type: mrr_at_100 value: 55.103 - type: mrr_at_1000 value: 55.139 - type: mrr_at_3 value: 52.349999999999994 - type: mrr_at_5 value: 53.61900000000001 - type: ndcg_at_1 value: 45.995999999999995 - type: ndcg_at_10 value: 41.333 - type: ndcg_at_100 value: 45.635999999999996 - type: ndcg_at_1000 value: 47.847 - type: ndcg_at_3 value: 36.825 - type: ndcg_at_5 value: 39.099000000000004 - type: precision_at_1 value: 45.995999999999995 - type: precision_at_10 value: 9.020999999999999 - type: precision_at_100 value: 1.244 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 23.34 - type: precision_at_5 value: 15.8 - type: recall_at_1 value: 22.997999999999998 - type: recall_at_10 value: 45.105000000000004 - type: recall_at_100 value: 62.188 - type: recall_at_1000 value: 76.907 - type: recall_at_3 value: 35.010000000000005 - type: recall_at_5 value: 39.5 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 80.0944 - type: ap value: 74.43301569395831 - type: f1 value: 80.04407647044388 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 10.171 - type: map_at_10 value: 17.558 - type: map_at_100 value: 18.694 - type: map_at_1000 value: 18.787000000000003 - type: map_at_3 value: 14.826 - type: map_at_5 value: 16.249 - type: mrr_at_1 value: 10.473 - type: mrr_at_10 value: 17.967 - type: mrr_at_100 value: 19.089 - type: mrr_at_1000 value: 19.177 - type: mrr_at_3 value: 15.222 - type: mrr_at_5 value: 16.655 - type: ndcg_at_1 value: 10.473 - type: ndcg_at_10 value: 22.148 - type: ndcg_at_100 value: 28.028 - type: ndcg_at_1000 value: 30.659 - type: ndcg_at_3 value: 16.474 - type: ndcg_at_5 value: 19.017 - type: precision_at_1 value: 10.473 - type: precision_at_10 value: 3.7969999999999997 - type: precision_at_100 value: 0.6779999999999999 - type: precision_at_1000 value: 0.09 - type: precision_at_3 value: 7.187 - type: precision_at_5 value: 5.599 - type: recall_at_1 value: 10.171 - type: recall_at_10 value: 36.459 - type: recall_at_100 value: 64.512 - type: recall_at_1000 value: 85.27900000000001 - type: recall_at_3 value: 20.868000000000002 - type: recall_at_5 value: 26.933 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 90.35795713634292 - type: f1 value: 89.72064544336776 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 66.4546283629731 - type: f1 value: 49.487271168215095 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.58238063214527 - type: f1 value: 65.54281371907213 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.47343644922664 - type: f1 value: 72.80522894672785 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.53600917473176 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.04699774280647 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.984352865575797 - type: mrr value: 32.02736001972659 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 4.666 - type: map_at_10 value: 10.066 - type: map_at_100 value: 12.794 - type: map_at_1000 value: 14.184 - type: map_at_3 value: 7.622 - type: map_at_5 value: 8.587 - type: mrr_at_1 value: 39.318999999999996 - type: mrr_at_10 value: 47.678 - type: mrr_at_100 value: 48.355 - type: mrr_at_1000 value: 48.400999999999996 - type: mrr_at_3 value: 45.82 - type: mrr_at_5 value: 46.656 - type: ndcg_at_1 value: 37.926 - type: ndcg_at_10 value: 29.049999999999997 - type: ndcg_at_100 value: 26.826 - type: ndcg_at_1000 value: 35.841 - type: ndcg_at_3 value: 33.513 - type: ndcg_at_5 value: 31.227 - type: precision_at_1 value: 39.318999999999996 - type: precision_at_10 value: 21.424000000000003 - type: precision_at_100 value: 7.231999999999999 - type: precision_at_1000 value: 2.012 - type: precision_at_3 value: 30.857 - type: precision_at_5 value: 26.378 - type: recall_at_1 value: 4.666 - type: recall_at_10 value: 13.898 - type: recall_at_100 value: 26.983 - type: recall_at_1000 value: 59.485 - type: recall_at_3 value: 8.953 - type: recall_at_5 value: 10.496 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 9.26 - type: map_at_10 value: 17.907999999999998 - type: map_at_100 value: 19.245 - type: map_at_1000 value: 19.339000000000002 - type: map_at_3 value: 14.634 - type: map_at_5 value: 16.386 - type: mrr_at_1 value: 10.574 - type: mrr_at_10 value: 19.438 - type: mrr_at_100 value: 20.638 - type: mrr_at_1000 value: 20.715 - type: mrr_at_3 value: 16.276 - type: mrr_at_5 value: 17.971999999999998 - type: ndcg_at_1 value: 10.574 - type: ndcg_at_10 value: 23.451 - type: ndcg_at_100 value: 29.982 - type: ndcg_at_1000 value: 32.449 - type: ndcg_at_3 value: 16.817 - type: ndcg_at_5 value: 19.867 - type: precision_at_1 value: 10.574 - type: precision_at_10 value: 4.609 - type: precision_at_100 value: 0.8330000000000001 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 8.266 - type: precision_at_5 value: 6.6739999999999995 - type: recall_at_1 value: 9.26 - type: recall_at_10 value: 39.224 - type: recall_at_100 value: 69.107 - type: recall_at_1000 value: 87.908 - type: recall_at_3 value: 21.490000000000002 - type: recall_at_5 value: 28.560999999999996 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 65.655 - type: map_at_10 value: 79.199 - type: map_at_100 value: 79.937 - type: map_at_1000 value: 79.964 - type: map_at_3 value: 76.19399999999999 - type: map_at_5 value: 78.08800000000001 - type: mrr_at_1 value: 75.53999999999999 - type: mrr_at_10 value: 82.89 - type: mrr_at_100 value: 83.074 - type: mrr_at_1000 value: 83.077 - type: mrr_at_3 value: 81.577 - type: mrr_at_5 value: 82.452 - type: ndcg_at_1 value: 75.53999999999999 - type: ndcg_at_10 value: 83.62899999999999 - type: ndcg_at_100 value: 85.411 - type: ndcg_at_1000 value: 85.646 - type: ndcg_at_3 value: 80.23700000000001 - type: ndcg_at_5 value: 82.107 - type: precision_at_1 value: 75.53999999999999 - type: precision_at_10 value: 12.695 - type: precision_at_100 value: 1.493 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.983 - type: precision_at_5 value: 23.164 - type: recall_at_1 value: 65.655 - type: recall_at_10 value: 92.269 - type: recall_at_100 value: 98.598 - type: recall_at_1000 value: 99.815 - type: recall_at_3 value: 82.616 - type: recall_at_5 value: 87.75800000000001 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 43.67844919460687 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 54.32866004447611 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.238 - type: map_at_10 value: 8.539 - type: map_at_100 value: 10.267 - type: map_at_1000 value: 10.552999999999999 - type: map_at_3 value: 6.165 - type: map_at_5 value: 7.22 - type: mrr_at_1 value: 15.9 - type: mrr_at_10 value: 25.557999999999996 - type: mrr_at_100 value: 26.867 - type: mrr_at_1000 value: 26.939 - type: mrr_at_3 value: 22.633 - type: mrr_at_5 value: 24.233 - type: ndcg_at_1 value: 15.9 - type: ndcg_at_10 value: 14.954 - type: ndcg_at_100 value: 22.486 - type: ndcg_at_1000 value: 27.986 - type: ndcg_at_3 value: 14.069 - type: ndcg_at_5 value: 12.200999999999999 - type: precision_at_1 value: 15.9 - type: precision_at_10 value: 7.9399999999999995 - type: precision_at_100 value: 1.8929999999999998 - type: precision_at_1000 value: 0.32299999999999995 - type: precision_at_3 value: 13.5 - type: precision_at_5 value: 10.9 - type: recall_at_1 value: 3.238 - type: recall_at_10 value: 16.1 - type: recall_at_100 value: 38.427 - type: recall_at_1000 value: 65.498 - type: recall_at_3 value: 8.212 - type: recall_at_5 value: 11.032 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 80.7612029200118 - type: cos_sim_spearman value: 74.17706899450974 - type: euclidean_pearson value: 78.6240925347838 - type: euclidean_spearman value: 74.22104652352341 - type: manhattan_pearson value: 78.49956480878576 - type: manhattan_spearman value: 74.0528957569391 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 80.0377294417705 - type: cos_sim_spearman value: 72.19570903733732 - type: euclidean_pearson value: 77.060604990743 - type: euclidean_spearman value: 71.54251658956483 - type: manhattan_pearson value: 77.28301977645965 - type: manhattan_spearman value: 71.77449045278667 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 79.69841558517969 - type: cos_sim_spearman value: 80.54022353649157 - type: euclidean_pearson value: 80.03651743688496 - type: euclidean_spearman value: 80.45116824930123 - type: manhattan_pearson value: 79.89688370680031 - type: manhattan_spearman value: 80.27208259746283 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 79.92235427443056 - type: cos_sim_spearman value: 76.20243980748161 - type: euclidean_pearson value: 79.28031963400572 - type: euclidean_spearman value: 76.3568261868673 - type: manhattan_pearson value: 79.24527845959733 - type: manhattan_spearman value: 76.39886696744185 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 84.2762365324788 - type: cos_sim_spearman value: 85.19929628214842 - type: euclidean_pearson value: 84.82568872953075 - type: euclidean_spearman value: 85.11039387706913 - type: manhattan_pearson value: 84.72922084197847 - type: manhattan_spearman value: 85.04448532444505 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 80.23256564746382 - type: cos_sim_spearman value: 81.92968415429543 - type: euclidean_pearson value: 81.12612888308936 - type: euclidean_spearman value: 81.97396557448675 - type: manhattan_pearson value: 81.15685601512081 - type: manhattan_spearman value: 82.01929408689 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.35057935029289 - type: cos_sim_spearman value: 86.60658025867397 - type: euclidean_pearson value: 86.48666975508912 - type: euclidean_spearman value: 86.70310223264862 - type: manhattan_pearson value: 86.23959282751626 - type: manhattan_spearman value: 86.48318896577922 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.15375299804011 - type: cos_sim_spearman value: 65.4588500819246 - type: euclidean_pearson value: 65.60180021985416 - type: euclidean_spearman value: 65.55596512146833 - type: manhattan_pearson value: 66.12421335157649 - type: manhattan_spearman value: 66.05163838991123 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 81.82391915730462 - type: cos_sim_spearman value: 81.93942545767499 - type: euclidean_pearson value: 83.16752744889406 - type: euclidean_spearman value: 82.31380947581034 - type: manhattan_pearson value: 82.98915741609575 - type: manhattan_spearman value: 82.16585239338073 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 77.19504204180527 - type: mrr value: 92.85429983959396 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 49.528 - type: map_at_10 value: 57.62199999999999 - type: map_at_100 value: 58.544 - type: map_at_1000 value: 58.573 - type: map_at_3 value: 54.56999999999999 - type: map_at_5 value: 56.552 - type: mrr_at_1 value: 52.0 - type: mrr_at_10 value: 58.939 - type: mrr_at_100 value: 59.653 - type: mrr_at_1000 value: 59.68 - type: mrr_at_3 value: 56.389 - type: mrr_at_5 value: 57.989000000000004 - type: ndcg_at_1 value: 52.0 - type: ndcg_at_10 value: 61.964 - type: ndcg_at_100 value: 65.871 - type: ndcg_at_1000 value: 66.724 - type: ndcg_at_3 value: 56.621 - type: ndcg_at_5 value: 59.551 - type: precision_at_1 value: 52.0 - type: precision_at_10 value: 8.333 - type: precision_at_100 value: 1.04 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 21.778 - type: precision_at_5 value: 14.933 - type: recall_at_1 value: 49.528 - type: recall_at_10 value: 74.2 - type: recall_at_100 value: 91.5 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 60.06700000000001 - type: recall_at_5 value: 67.133 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81287128712871 - type: cos_sim_ap value: 95.15039468118793 - type: cos_sim_f1 value: 90.48817312531455 - type: cos_sim_precision value: 91.08409321175279 - type: cos_sim_recall value: 89.9 - type: dot_accuracy value: 99.78019801980199 - type: dot_ap value: 93.60256835857994 - type: dot_f1 value: 88.73096446700508 - type: dot_precision value: 90.10309278350516 - type: dot_recall value: 87.4 - type: euclidean_accuracy value: 99.81188118811882 - type: euclidean_ap value: 95.15954231276913 - type: euclidean_f1 value: 90.48096192384769 - type: euclidean_precision value: 90.66265060240963 - type: euclidean_recall value: 90.3 - type: manhattan_accuracy value: 99.81188118811882 - type: manhattan_ap value: 95.17107000565468 - type: manhattan_f1 value: 90.5 - type: manhattan_precision value: 90.5 - type: manhattan_recall value: 90.5 - type: max_accuracy value: 99.81287128712871 - type: max_ap value: 95.17107000565468 - type: max_f1 value: 90.5 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 51.77488276525734 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.30657214418171 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 47.84571922992432 - type: mrr value: 48.549107142857146 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.840750357585556 - type: cos_sim_spearman value: 29.832953864936567 - type: dot_pearson value: 30.499687946740657 - type: dot_spearman value: 30.73436062481656 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.16999999999999998 - type: map_at_10 value: 1.014 - type: map_at_100 value: 5.623 - type: map_at_1000 value: 15.190999999999999 - type: map_at_3 value: 0.377 - type: map_at_5 value: 0.577 - type: mrr_at_1 value: 68.0 - type: mrr_at_10 value: 74.45 - type: mrr_at_100 value: 74.846 - type: mrr_at_1000 value: 74.846 - type: mrr_at_3 value: 71.333 - type: mrr_at_5 value: 73.533 - type: ndcg_at_1 value: 64.0 - type: ndcg_at_10 value: 47.52 - type: ndcg_at_100 value: 37.419999999999995 - type: ndcg_at_1000 value: 36.318 - type: ndcg_at_3 value: 51.13999999999999 - type: ndcg_at_5 value: 49.101 - type: precision_at_1 value: 68.0 - type: precision_at_10 value: 50.8 - type: precision_at_100 value: 39.160000000000004 - type: precision_at_1000 value: 16.948 - type: precision_at_3 value: 52.0 - type: precision_at_5 value: 51.6 - type: recall_at_1 value: 0.16999999999999998 - type: recall_at_10 value: 1.269 - type: recall_at_100 value: 8.937000000000001 - type: recall_at_1000 value: 35.036 - type: recall_at_3 value: 0.396 - type: recall_at_5 value: 0.6669999999999999 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.672 - type: map_at_10 value: 6.739000000000001 - type: map_at_100 value: 12.006 - type: map_at_1000 value: 13.474 - type: map_at_3 value: 2.617 - type: map_at_5 value: 4.329000000000001 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 30.764000000000003 - type: mrr_at_100 value: 32.457 - type: mrr_at_1000 value: 32.481 - type: mrr_at_3 value: 26.531 - type: mrr_at_5 value: 28.877999999999997 - type: ndcg_at_1 value: 18.367 - type: ndcg_at_10 value: 17.471999999999998 - type: ndcg_at_100 value: 29.341 - type: ndcg_at_1000 value: 41.005 - type: ndcg_at_3 value: 14.64 - type: ndcg_at_5 value: 17.039 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 17.551 - type: precision_at_100 value: 6.673 - type: precision_at_1000 value: 1.4160000000000001 - type: precision_at_3 value: 14.966 - type: precision_at_5 value: 18.776 - type: recall_at_1 value: 1.672 - type: recall_at_10 value: 12.795000000000002 - type: recall_at_100 value: 41.289 - type: recall_at_1000 value: 76.947 - type: recall_at_3 value: 3.334 - type: recall_at_5 value: 6.864000000000001 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.3424 - type: ap value: 13.45149708639965 - type: f1 value: 53.278180518373574 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 57.60045274476513 - type: f1 value: 57.9395926195531 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 36.649067825169446 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.68599868868093 - type: cos_sim_ap value: 65.7938550603812 - type: cos_sim_f1 value: 61.81946735800141 - type: cos_sim_precision value: 55.85604770017035 - type: cos_sim_recall value: 69.2084432717678 - type: dot_accuracy value: 82.09453418370389 - type: dot_ap value: 61.00867337905922 - type: dot_f1 value: 58.56196783349101 - type: dot_precision value: 53.06472353193313 - type: dot_recall value: 65.32981530343008 - type: euclidean_accuracy value: 83.68599868868093 - type: euclidean_ap value: 66.17065796133883 - type: euclidean_f1 value: 62.440610152538135 - type: euclidean_precision value: 59.3393536121673 - type: euclidean_recall value: 65.88390501319262 - type: manhattan_accuracy value: 83.57870894677237 - type: manhattan_ap value: 65.89925640001532 - type: manhattan_f1 value: 62.2255119664446 - type: manhattan_precision value: 58.43373493975904 - type: manhattan_recall value: 66.54353562005278 - type: max_accuracy value: 83.68599868868093 - type: max_ap value: 66.17065796133883 - type: max_f1 value: 62.440610152538135 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.68579966623976 - type: cos_sim_ap value: 83.2666595805096 - type: cos_sim_f1 value: 75.11536297129996 - type: cos_sim_precision value: 73.24943294065999 - type: cos_sim_recall value: 77.07884200800738 - type: dot_accuracy value: 86.76213761788334 - type: dot_ap value: 80.85199640255004 - type: dot_f1 value: 73.27634898520165 - type: dot_precision value: 71.70756872282409 - type: dot_recall value: 74.91530643671081 - type: euclidean_accuracy value: 87.79640625606395 - type: euclidean_ap value: 83.52666327503474 - type: euclidean_f1 value: 75.37022886875523 - type: euclidean_precision value: 71.4522249051397 - type: euclidean_recall value: 79.74283954419464 - type: manhattan_accuracy value: 87.80804905499282 - type: manhattan_ap value: 83.4995899990913 - type: manhattan_f1 value: 75.44320420223242 - type: manhattan_precision value: 71.68307223069458 - type: manhattan_recall value: 79.6196489066831 - type: max_accuracy value: 87.80804905499282 - type: max_ap value: 83.52666327503474 - type: max_f1 value: 75.44320420223242 ---
Rostlab/prot_bert_bfd_localization
Rostlab
"2021-05-18T22:05:26Z"
5,133
1
transformers
[ "transformers", "pytorch", "jax", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2022-03-02T23:29:04Z"
Entry not found
hubertsiuzdak/snac_32khz
hubertsiuzdak
"2024-04-03T23:48:23Z"
5,132
3
transformers
[ "transformers", "pytorch", "audio", "license:mit", "endpoints_compatible", "region:us" ]
null
"2024-02-27T17:18:19Z"
--- license: mit tags: - audio --- # SNAC 🍿 Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess audio into discrete codes at a low bitrate. 👉 This model was primarily trained on music data, and its recommended use case is music (and SFX) generation. See below for other pretrained models. 🔗 GitHub repository: https://github.com/hubertsiuzdak/snac/ ## Overview SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC. However, SNAC introduces a simple change where coarse tokens are sampled less frequently, covering a broader time span. This model compresses 32 kHz audio into discrete codes at a 1.9 kbps bitrate. It uses 4 RVQ levels with token rates of 10, 21, 42, and 83 Hz. ## Pretrained models Currently, all models support only single audio channel (mono). | Model | Bitrate | Sample Rate | Params | Recommended use case | |-----------------------------------------------------------------------------|-----------|-------------|--------|--------------------------| | [hubertsiuzdak/snac_24khz](https://huggingface.co/hubertsiuzdak/snac_24khz) | 0.98 kbps | 24 kHz | 19.8 M | 🗣️ Speech | | hubertsiuzdak/snac_32khz (this model) | 1.9 kbps | 32 kHz | 54.5 M | 🎸 Music / Sound Effects | | [hubertsiuzdak/snac_44khz](https://huggingface.co/hubertsiuzdak/snac_44khz) | 2.6 kbps | 44 kHz | 54.5 M | 🎸 Music / Sound Effects | ## Usage Install it using: ```bash pip install snac ``` To encode (and decode) audio with SNAC in Python, use the following code: ```python import torch from snac import SNAC model = SNAC.from_pretrained("hubertsiuzdak/snac_32khz").eval().cuda() audio = torch.randn(1, 1, 32000).cuda() # B, 1, T with torch.inference_mode(): codes = model.encode(audio) audio_hat = model.decode(codes) ``` You can also encode and reconstruct in a single call: ```python with torch.inference_mode(): audio_hat, codes = model(audio) ``` ⚠️ Note that `codes` is a list of token sequences of variable lengths, each corresponding to a different temporal resolution. ``` >>> [code.shape[1] for code in codes] [12, 24, 48, 96] ``` ## Acknowledgements Module definitions are adapted from the [Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec).
mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF
mradermacher
"2024-06-13T21:06:02Z"
5,132
0
transformers
[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "not-for-all-audiences", "rp", "roleplay", "role-play", "en", "base_model:Casual-Autopsy/L3-Penumbral-Mind-RP-8B", "license:llama3", "endpoints_compatible", "region:us" ]
null
"2024-06-12T13:18:04Z"
--- base_model: Casual-Autopsy/L3-Penumbral-Mind-RP-8B language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - not-for-all-audiences - rp - roleplay - role-play --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Casual-Autopsy/L3-Penumbral-Mind-RP-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/L3-Penumbral-Mind-RP-8B-i1-GGUF/resolve/main/L3-Penumbral-Mind-RP-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2
Finnish-NLP
"2024-04-28T17:08:50Z"
5,130
1
transformers
[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:mozilla-foundation/common_voice_7_0", "arxiv:2111.09296", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
"2022-03-27T18:10:56Z"
--- license: apache-2.0 language: fi metrics: - wer - cer tags: - automatic-speech-recognition - fi - finnish - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: wav2vec2-xlsr-1b-finnish-lm-v2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: fi metrics: - name: Test WER type: wer value: 4.09 - name: Test CER type: cer value: 0.88 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: FLEURS ASR type: google/fleurs args: fi_fi metrics: - name: Test WER type: wer value: 12.11 - name: Test CER type: cer value: 5.65 --- # Wav2vec2-xls-r-1b for Finnish ASR This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in [this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20). This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model. **Note**: this model is exactly the same as the [aapot/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-lm-v2) model so that model has just been copied/moved to this `Finnish-NLP` Hugging Face organization. ## Model description Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296). This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR. ## Intended uses & limitations You can use this model for Finnish ASR (speech-to-text) task. ### How to use Check the [run-finnish-asr-models.ipynb](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model. ### Limitations and bias This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example. The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding. ## Training data This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets: | Dataset | Hours | % of total hours | |:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:| | [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % | | [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % | | [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % | | [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % | | [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % | | [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % | Datasets were filtered to include maximum length of 20 seconds long audio samples. ## Training procedure This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud. Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets. For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned [Finnish Wikipedia](https://huggingface.co/datasets/wikipedia) (August 2021) dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters: - attention_dropout: 0.094 - hidden_dropout: 0.047 - feat_proj_dropout: 0.04 - mask_time_prob: 0.082 - layerdrop: 0.041 - activation_dropout: 0.055 - ctc_loss_reduction: "mean" ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.7778 | 0.17 | 500 | 0.2851 | 0.3572 | | 0.5506 | 0.34 | 1000 | 0.1595 | 0.2130 | | 0.6569 | 0.5 | 1500 | 0.1458 | 0.2046 | | 0.5997 | 0.67 | 2000 | 0.1374 | 0.1975 | | 0.542 | 0.84 | 2500 | 0.1390 | 0.1956 | | 0.4815 | 1.01 | 3000 | 0.1266 | 0.1813 | | 0.6982 | 1.17 | 3500 | 0.1441 | 0.1965 | | 0.4522 | 1.34 | 4000 | 0.1232 | 0.1822 | | 0.4655 | 1.51 | 4500 | 0.1209 | 0.1702 | | 0.4069 | 1.68 | 5000 | 0.1149 | 0.1688 | | 0.4226 | 1.84 | 5500 | 0.1121 | 0.1560 | | 0.3993 | 2.01 | 6000 | 0.1091 | 0.1557 | | 0.406 | 2.18 | 6500 | 0.1115 | 0.1553 | | 0.4098 | 2.35 | 7000 | 0.1144 | 0.1560 | | 0.3995 | 2.51 | 7500 | 0.1028 | 0.1476 | | 0.4101 | 2.68 | 8000 | 0.1129 | 0.1511 | | 0.3636 | 2.85 | 8500 | 0.1025 | 0.1517 | | 0.3534 | 3.02 | 9000 | 0.1068 | 0.1480 | | 0.3836 | 3.18 | 9500 | 0.1072 | 0.1459 | | 0.3531 | 3.35 | 10000 | 0.0928 | 0.1367 | | 0.3649 | 3.52 | 10500 | 0.1042 | 0.1426 | | 0.3645 | 3.69 | 11000 | 0.0979 | 0.1433 | | 0.3685 | 3.85 | 11500 | 0.0947 | 0.1346 | | 0.3325 | 4.02 | 12000 | 0.0991 | 0.1352 | | 0.3497 | 4.19 | 12500 | 0.0919 | 0.1358 | | 0.3303 | 4.36 | 13000 | 0.0888 | 0.1272 | | 0.3323 | 4.52 | 13500 | 0.0888 | 0.1277 | | 0.3452 | 4.69 | 14000 | 0.0894 | 0.1279 | | 0.337 | 4.86 | 14500 | 0.0917 | 0.1289 | | 0.3114 | 5.03 | 15000 | 0.0942 | 0.1313 | | 0.3099 | 5.19 | 15500 | 0.0902 | 0.1239 | | 0.3079 | 5.36 | 16000 | 0.0871 | 0.1256 | | 0.3293 | 5.53 | 16500 | 0.0861 | 0.1263 | | 0.3123 | 5.7 | 17000 | 0.0876 | 0.1203 | | 0.3093 | 5.86 | 17500 | 0.0848 | 0.1226 | | 0.2903 | 6.03 | 18000 | 0.0914 | 0.1221 | | 0.297 | 6.2 | 18500 | 0.0841 | 0.1185 | | 0.2797 | 6.37 | 19000 | 0.0858 | 0.1165 | | 0.2878 | 6.53 | 19500 | 0.0874 | 0.1161 | | 0.2974 | 6.7 | 20000 | 0.0835 | 0.1173 | | 0.3051 | 6.87 | 20500 | 0.0835 | 0.1178 | | 0.2941 | 7.04 | 21000 | 0.0852 | 0.1155 | | 0.258 | 7.21 | 21500 | 0.0832 | 0.1132 | | 0.2778 | 7.37 | 22000 | 0.0829 | 0.1110 | | 0.2751 | 7.54 | 22500 | 0.0822 | 0.1069 | | 0.2887 | 7.71 | 23000 | 0.0819 | 0.1103 | | 0.2509 | 7.88 | 23500 | 0.0787 | 0.1055 | | 0.2501 | 8.04 | 24000 | 0.0807 | 0.1076 | | 0.2399 | 8.21 | 24500 | 0.0784 | 0.1052 | | 0.2539 | 8.38 | 25000 | 0.0772 | 0.1075 | | 0.248 | 8.55 | 25500 | 0.0772 | 0.1055 | | 0.2689 | 8.71 | 26000 | 0.0763 | 0.1027 | | 0.2855 | 8.88 | 26500 | 0.0756 | 0.1035 | | 0.2421 | 9.05 | 27000 | 0.0771 | 0.0998 | | 0.2497 | 9.22 | 27500 | 0.0756 | 0.0971 | | 0.2367 | 9.38 | 28000 | 0.0741 | 0.0974 | | 0.2473 | 9.55 | 28500 | 0.0739 | 0.0982 | | 0.2396 | 9.72 | 29000 | 0.0756 | 0.0991 | | 0.2602 | 9.89 | 29500 | 0.0737 | 0.0975 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0 ## Evaluation results Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0), [Common Voice 9.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0) and with the [FLEURS ASR Finnish test split](https://huggingface.co/datasets/google/fleurs). This model's training data includes the training splits of Common Voice 7.0 but our newer `Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned` and `Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish` models include the Common Voice 9.0 so we ran tests for both Common Voice versions. Note: Common Voice doesn't seem to fully preserve the test split as fixed between the dataset versions so it is possible that some of the training examples of Common Voice 9.0 are in the test split of the Common Voice 7.0 and vice versa. Thus, Common Voice test result comparisons are not fully accurate between the models trained with different Common Voice versions but the comparison should still be meaningful enough. ### Common Voice 7.0 testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 --dataset mozilla-foundation/common_voice_7_0 --config fi --split test ``` This model (the fift row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts: | | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-------------------------------------------------------|------------------|---------------|------------------|---------------|------------------| |Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned | 95 million |5.85 |13.52 |1.35 |2.44 | |Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish | 300 million |4.13 |**9.66** |0.90 |1.66 | |Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm | 300 million |8.16 |17.92 |1.97 |3.36 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm | 1000 million |5.65 |13.11 |1.20 |2.23 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 | 1000 million |**4.09** |9.73 |**0.88** |**1.65** | ### Common Voice 9.0 testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 --dataset mozilla-foundation/common_voice_9_0 --config fi --split test ``` This model (the fift row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts: | | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-------------------------------------------------------|------------------|---------------|------------------|---------------|------------------| |Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned | 95 million |5.93 |14.08 |1.40 |2.59 | |Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish | 300 million |4.13 |9.83 |0.92 |1.71 | |Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm | 300 million |7.42 |16.45 |1.79 |3.07 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm | 1000 million |5.35 |13.00 |1.14 |2.20 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 | 1000 million |**3.72** |**8.96** |**0.80** |**1.52** | ### FLEURS ASR testing To evaluate this model, run the `eval.py` script in this repository: ```bash python3 eval.py --model_id Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 --dataset google/fleurs --config fi_fi --split test ``` This model (the fift row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts: | | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) | |-------------------------------------------------------|------------------|---------------|------------------|---------------|------------------| |Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned | 95 million |13.99 |17.16 |6.07 |6.61 | |Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish | 300 million |12.44 |**14.63** |5.77 |6.22 | |Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm | 300 million |17.72 |23.30 |6.78 |7.67 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm | 1000 million |20.34 |16.67 |6.97 |6.35 | |Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 | 1000 million |**12.11** |14.89 |**5.65** |**6.06** | ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗
MIT/ast-finetuned-speech-commands-v2
MIT
"2023-09-10T18:03:01Z"
5,130
11
transformers
[ "transformers", "pytorch", "safetensors", "audio-spectrogram-transformer", "audio-classification", "dataset:speech_commands", "arxiv:2104.01778", "license:bsd-3-clause", "model-index", "endpoints_compatible", "region:us" ]
audio-classification
"2022-11-14T19:11:22Z"
--- license: bsd-3-clause datasets: - speech_commands tags: - audio-classification model-index: - name: MIT/ast-finetuned-speech-commands-v2 results: - task: type: audio-classification dataset: name: Speech Commands v2 type: speech_commands metrics: - type: accuracy value: 98.12 --- # Audio Spectrogram Transformer (fine-tuned on Speech Commands v2) Audio Spectrogram Transformer (AST) model fine-tuned on Speech Commands v2. It was introduced in the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Gong et al. and first released in [this repository](https://github.com/YuanGongND/ast). Disclaimer: The team releasing Audio Spectrogram Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Audio Spectrogram Transformer is equivalent to [ViT](https://huggingface.co/docs/transformers/model_doc/vit), but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks. ## Usage You can use the raw model for classifying audio into one of the Speech Commands v2 classes. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/audio-spectrogram-transformer) for more info.
oliverguhr/spelling-correction-english-base
oliverguhr
"2023-12-18T08:46:53Z"
5,128
64
transformers
[ "transformers", "pytorch", "tensorboard", "onnx", "safetensors", "bart", "text2text-generation", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
"2022-05-23T15:26:21Z"
--- language: - en license: mit widget: - text: "lets do a comparsion" example_title: "1" - text: "Their going to be here so0n" example_title: "2" - text: "ze shop is cloed due to covid 19" example_title: "3" metrics: - cer --- This is an experimental model that should fix your typos and punctuation. If you like to run your own experiments or train for a different language, have a look at [the code](https://github.com/oliverguhr/spelling). ## Model description This is a proof of concept spelling correction model for English. ## Intended uses & limitations This project is work in progress, be aware that the model can produce artefacts. You can test the model using the pipeline-interface: ```python from transformers import pipeline fix_spelling = pipeline("text2text-generation",model="oliverguhr/spelling-correction-english-base") print(fix_spelling("lets do a comparsion",max_length=2048)) ```
moussaKam/frugalscore_tiny_bert-base_bert-score
moussaKam
"2022-02-01T10:50:21Z"
5,120
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "arxiv:2110.08559", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2022-03-02T23:29:05Z"
# FrugalScore FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance Paper: https://arxiv.org/abs/2110.08559?context=cs Project github: https://github.com/moussaKam/FrugalScore The pretrained checkpoints presented in the paper : | FrugalScore | Student | Teacher | Method | |----------------------------------------------------|-------------|----------------|------------| | [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore | | [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore | | [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore | | [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore | | [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore | | [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore | | [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore | | [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore |
DiscoResearch/Llama3-German-8B
DiscoResearch
"2024-05-29T11:35:49Z"
5,119
33
transformers
[ "transformers", "safetensors", "llama", "text-generation", "de", "arxiv:2404.10830", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-05-23T16:36:25Z"
--- license: llama3 language: - de library_name: transformers --- # Llama3-German-8B (version 0.1) Llama3-German-8B-v0.1 is a large language model based on [Meta's Llama3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). It is specialized for the German language through continuous pretraining on 65 billion high-quality tokens, similar to previous [LeoLM](https://huggingface.co/LeoLM) or [Occiglot](https://huggingface.co/collections/occiglot/occiglot-eu5-7b-v01-65dbed502a6348b052695e01) models. Llama3 itself was trained on 15T tokens, of which only <1T were multilingual, resulting in suboptimal performance in German with reduced linguistic capabilities and frequent grammatical errors, motivating the necessity for continued pretraining. Benchmark results on our model show minimal degradation in English performance, despite the absence of replay during training. Importantly, Llama3-German-8B-v0.1 demonstrates strong improvements in German, particularly on the Hellaswag benchmark, which measures linguistic understanding and general reasoning. [DiscoResearch/Llama3-German-8B-v0.1](https://huggingface.co/collections/DiscoResearch/discoleo-8b-llama3-for-german-6650527496c0fafefd4c9729) is the result of a joint effort between [DiscoResearch](https://huggingface.co/DiscoResearch) and [Occiglot](https://huggingface.co/occiglot) with support from the [DFKI](https://www.dfki.de/web/) (German Research Center for Artificial Intelligence) and [hessian.Ai](https://hessian.ai). Occiglot kindly handled data preprocessing, filtering, and deduplication as part of their latest [dataset release](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5), as well as sharing their compute allocation at hessian.Ai's 42 Supercomputer. ## How to use This is a base model and should probably be subject to finetuning before use. See our [collection](https://huggingface.co/collections/DiscoResearch/discoleo-8b-llama3-for-german-6650527496c0fafefd4c9729) for various finetuned and long-context versions. ## Model Training and Hyperparameters The model was trained on 128 GPUs on [hessian.Ai 42](hessian.ai) for ~60 hours. See detailed hyperparameters below. | Parameter | Value | |-------------------|-----------------------------------| | Sequence Length | 8192 tokens | | Learning Rate | 1.5e-5 to 1.5e-6 (cosine schedule)| | Batch Size | 4194304 (512*8192) tokens | | Micro Batch Size | 4*8192 tokens | | Training Steps | 15500 | | Warmup Steps | 155 (1%) | | Weight Decay | 0.05 | | Optimizer | AdamW | ## Data Collection and Preprocessing For pre-training, we used 65B German tokens from the [occiglot-fineweb-0.5](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5) dataset. The data comprises multiple curated datasets from [LLM-Datasets](https://github.com/malteos/llm-datasets) as well as 12 [Common-Crawl](https://commoncrawl.org) releases that were processed with [OSCAR's Ungoliant pipeline](https://github.com/oscar-project/ungoliant). All data was further filtered with a set of language-specific filters based on [Huggingface's fine-web](https://github.com/huggingface/datatrove/blob/main/examples/fineweb.py) and globally deduplicated. For more information please refer to the [dataset card](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5) and corresponding [blog-post](https://occiglot.eu/posts/occiglot-fineweb/). ## Evaluation and Results We evaluated the model using a suite of common English Benchmarks and their German counterparts with [GermanBench](https://github.com/bjoernpl/GermanBenchmark). The following figure shows the benchmark results in comparison to the base model [meta-llama/Meta-Llama3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) and two different hyperparameter configurations. We swept different learning rates to identify a well-working setup. The final released model is the 1.5e-5 lr version. ![alt text](base_model_evals.png) Find the detailed benchmark scores for the base and long-context models in this table. | Model | truthful_qa_de | truthfulqa_mc | arc_challenge | arc_challenge_de | hellaswag | hellaswag_de | MMLU | MMLU-DE | mean | |--------------------------------------|----------------|---------------|---------------|------------------|-----------|--------------|--------|---------|------------| | DiscoResearch/Llama3-German-8B | **0.49499** | 0.44838 | 0.55802 | **0.49829** | 0.79924 | **0.65395** | 0.62240| **0.54413** | **0.57743** | | DiscoResearch/Llama3-German-8B-32k | 0.48920 | **0.45138** | 0.54437 | 0.49232 | 0.79078 | 0.64310 | 0.58774| 0.47971 | 0.55982 | | meta-llama/Meta-Llama-3-8B-Instruct | 0.47498 | 0.43923 | **0.59642** | 0.47952 | **0.82025**| 0.60008 | **0.66658**| 0.53541 | 0.57656 | ## Long-Context Extension In addition to the base model, we release a long-context version of Llama3-German-8B ([DiscoResearch/Llama3-German-8B-32k](https://huggingface.co/DiscoResearch/Llama3-German-8B-32k) capable of processing context lengths up to 65k tokens. This variant was trained on an additional 100 million tokens at 32k context length, using a rope_theta value of `1.5e6` and a learning rate of `1.5e-5` with a batch size of `256*8192` tokens and otherwise equal hyperparameters to the base model. ## Instruction Tuning We also provide an instruction-tuned version: [DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1), utilizing the DiscoLM German dataset for fine-tuning (also available as a long-context model at [DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1)). Find more details in the respective model cards. Also check out our experimental merge ([DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental)) between [meta-llama/Meta-Llama3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and our finetuned model in an attempt to keep the extraordinary capabilities of Llama3-Instruct and add exceptional German skills. ## Document Packing We employed a more intelligent document packing strategy based on the ["Fewer Truncations Improve Language Modeling" paper by Ding et al.](https://arxiv.org/abs/2404.10830v2), using the first-fit-decreasing algorithm to pack documents into batches without truncation. We packed our data in chunks of 10000 documents for more efficient processing while maintaining >99% packing efficiency. Documents longer than the sequence length are split into chunks of sequence length. This approach results in overall higher benchmark scores when training on the same data with equal hyperparameters. The following numbers are from initial experiments with `3e-5 lr` and 12k steps and show improvements comparable to those shown in the original paper. | Task | Naive Packing | Fewer Truncations Packing | Percentage Increase | |-------------------|---------------|---------------------------|---------------------| | truthfulqa_mc | 0.452648 | 0.467687 | 3.32% | | arc_challenge | 0.517918 | 0.528157 | 1.98% | | truthful_qa_de | 0.485529 | 0.492979 | 1.53% | | arc_challenge_de | 0.480375 | 0.493174 | 2.66% | | hellaswag | 0.776041 | 0.773352 | -0.35% | | hellaswag_de | 0.655248 | 0.653356 | -0.29% | | MMLU | 0.573719 | 0.579802 | 1.06% | | MMLU-DE | 0.504509 | 0.503863 | -0.13% | The following is our simple implementation of the first-fit-decreasing algorithm described in the paper. ```python def pack_documents(tokenized_documents): # Sort documents by their length in descending order sorted_docs = sorted(tokenized_documents, key=len, reverse=True) # Initialize bins bins = [] # Function to find the first bin that can accommodate the document def find_bin(doc): for b in bins: if sum(len(d) for d in b) + len(doc) <= 8192: return b return None # Place each document in the first available bin or create a new bin for doc in sorted_docs: target_bin = find_bin(doc) if target_bin is not None: target_bin.append(doc) else: # Create a new bin with this document if no suitable bin is found bins.append([doc]) # Return results return bins ``` ## Model Configurations We release DiscoLeo-8B in the following configurations: 1. [Base model with continued pretraining](https://huggingface.co/DiscoResearch/Llama3-German-8B) 2. [Long-context version (32k context length)](https://huggingface.co/DiscoResearch/Llama3-German-8B-32k) 3. [Instruction-tuned version of the base model](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1) 4. [Instruction-tuned version of the long-context model](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-Instruct-8B-32k-v0.1) 5. [Experimental `DARE-TIES` Merge with Llama3-Instruct](https://huggingface.co/DiscoResearch/Llama3-DiscoLeo-8B-DARE-Experimental) 6. [Collection of Quantized versions](https://huggingface.co/collections/DiscoResearch/discoleo-8b-quants-6651bcf8f72c9a37ce485d42) ## How to use: Here's how to use the model with transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device="cuda" model = AutoModelForCausalLM.from_pretrained( "DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("DiscoResearch/Llama3-DiscoLeo-Instruct-8B-v0.1") prompt = "Schreibe ein Essay über die Bedeutung der Energiewende für Deutschlands Wirtschaft" messages = [ {"role": "system", "content": "Du bist ein hilfreicher Assistent."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Acknowledgements The model was trained and evaluated by [Björn Plüster](https://huggingface.co/bjoernp) ([DiscoResearch](https://huggingface.co/DiscoResearch), [ellamind](https://ellamind.com)) with data preparation and project supervision by [Manuel Brack](http://manuel-brack.eu) ([DFKI](https://www.dfki.de/web/), [TU-Darmstadt](https://www.tu-darmstadt.de/)). Initial work on dataset collection and curation was performed by [Malte Ostendorff](https://ostendorff.org) and [Pedro Ortiz Suarez](https://portizs.eu). Instruction tuning was done with the DiscoLM German dataset created by [Jan-Philipp Harries](https://huggingface.co/jphme) and [Daniel Auras](https://huggingface.co/rasdani) ([DiscoResearch](https://huggingface.co/DiscoResearch), [ellamind](https://ellamind.com)). We extend our gratitude to [LAION](https://laion.ai/) and friends, especially [Christoph Schuhmann](https://entwickler.de/experten/christoph-schuhmann) and [Jenia Jitsev](https://huggingface.co/JJitsev), for initiating this collaboration. The model training was supported by a compute grant at the [42 supercomputer](https://hessian.ai/) which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)). The curation of the training data is partially funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html) through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).
mradermacher/SI-FT-CL-7B-Python-GGUF
mradermacher
"2024-06-05T22:29:57Z"
5,117
0
transformers
[ "transformers", "gguf", "en", "base_model:zichao22/SI-FT-CL-7B-Python", "endpoints_compatible", "region:us" ]
null
"2024-06-05T22:05:06Z"
--- base_model: zichao22/SI-FT-CL-7B-Python language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/zichao22/SI-FT-CL-7B-Python <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q2_K.gguf) | Q2_K | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.IQ3_XS.gguf) | IQ3_XS | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.IQ3_S.gguf) | IQ3_S | 3.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q3_K_S.gguf) | Q3_K_S | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.IQ3_M.gguf) | IQ3_M | 3.2 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q3_K_L.gguf) | Q3_K_L | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.IQ4_XS.gguf) | IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q5_K_S.gguf) | Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q5_K_M.gguf) | Q5_K_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q6_K.gguf) | Q6_K | 5.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/SI-FT-CL-7B-Python-GGUF/resolve/main/SI-FT-CL-7B-Python.f16.gguf) | f16 | 13.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
h2oai/h2ogpt-oig-oasst1-256-6_9b
h2oai
"2023-06-02T22:36:04Z"
5,114
5
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "gpt", "llm", "large language model", "open-source", "en", "dataset:h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-04-17T18:09:08Z"
--- license: apache-2.0 language: - en library_name: transformers inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico tags: - gpt - llm - large language model - open-source datasets: - h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1 --- # h2oGPT Model Card ## Summary H2O.ai's `h2ogpt-oig-oasst1-256-6_9b` is a 6.9 billion parameter instruction-following large language model licensed for commercial use. - Base model: [EleutherAI/pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) - Fine-tuning dataset: [h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1](https://huggingface.co/datasets/h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v1) - Data-prep and fine-tuning code: [H2O.ai Github](https://github.com/h2oai/h2ogpt) - Training logs: [zip](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-256-6_9b/blob/main/pythia-6.9b.h2ogpt-oig-oasst1-instruct-cleaned-v1.json.1_epochs.5fc91911bc2bfaaf3b6c2de577c4b0ae45a07a4a.9.zip) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. ```bash pip install transformers==4.28.1 pip install accelerate==0.18.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline(model="h2oai/h2ogpt-oig-oasst1-256-6_9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", prompt_type='human_bot') res = generate_text("Why is drinking water so healthy?", max_new_tokens=100) print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-256-6_9b/blob/main/h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-oig-oasst1-256-6_9b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-oig-oasst1-256-6_9b", torch_dtype=torch.bfloat16, device_map="auto") generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer, prompt_type='human_bot') res = generate_text("Why is drinking water so healthy?", max_new_tokens=100) print(res[0]["generated_text"]) ``` ## Model Architecture ``` GPTNeoXForCausalLM( (gpt_neox): GPTNeoXModel( (embed_in): Embedding(50432, 4096) (layers): ModuleList( (0-31): 32 x GPTNeoXLayer( (input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) (post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) (attention): GPTNeoXAttention( (rotary_emb): RotaryEmbedding() (query_key_value): Linear(in_features=4096, out_features=12288, bias=True) (dense): Linear(in_features=4096, out_features=4096, bias=True) ) (mlp): GPTNeoXMLP( (dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True) (dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True) (act): GELUActivation() ) ) ) (final_layer_norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) ) (embed_out): Linear(in_features=4096, out_features=50432, bias=False) ) ``` ## Model Configuration ```json GPTNeoXConfig { "_name_or_path": "h2oai/h2ogpt-oig-oasst1-256-6_9b", "architectures": [ "GPTNeoXForCausalLM" ], "bos_token_id": 0, "custom_pipelines": { "text-generation": { "impl": "h2oai_pipeline.H2OTextGenerationPipeline", "pt": "AutoModelForCausalLM" } }, "eos_token_id": 0, "hidden_act": "gelu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 16384, "layer_norm_eps": 1e-05, "max_position_embeddings": 2048, "model_type": "gpt_neox", "num_attention_heads": 32, "num_hidden_layers": 32, "rotary_emb_base": 10000, "rotary_pct": 0.25, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.28.1", "use_cache": true, "use_parallel_residual": true, "vocab_size": 50432 } ```
TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF
TheBloke
"2023-09-27T12:46:19Z"
5,112
13
transformers
[ "transformers", "gguf", "llama", "code llama", "base_model:Phind/Phind-CodeLlama-34B-Python-v1", "license:llama2", "model-index", "text-generation-inference", "region:us" ]
null
"2023-08-26T09:28:26Z"
--- license: llama2 tags: - code llama base_model: Phind/Phind-CodeLlama-34B-Python-v1 inference: false model_creator: Phind model_type: llama prompt_template: '{prompt} \n ' quantized_by: TheBloke model-index: - name: Phind-CodeLlama-34B-v1 results: - task: type: text-generation dataset: name: HumanEval type: openai_humaneval metrics: - type: pass@1 value: 69.5% name: pass@1 verified: false --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Phind CodeLlama 34B Python v1 - GGUF - Model creator: [Phind](https://huggingface.co/Phind) - Original model: [Phind CodeLlama 34B Python v1](https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1) <!-- description start --> ## Description This repo contains GGUF format model files for [Phind's Phind CodeLlama 34B Python v1](https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF) * [Phind's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Plain-with-newline ``` {prompt} \n ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [phind-codellama-34b-python-v1.Q2_K.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q2_K.gguf) | Q2_K | 2 | 14.21 GB| 16.71 GB | smallest, significant quality loss - not recommended for most purposes | | [phind-codellama-34b-python-v1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q3_K_S.gguf) | Q3_K_S | 3 | 14.61 GB| 17.11 GB | very small, high quality loss | | [phind-codellama-34b-python-v1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q3_K_M.gguf) | Q3_K_M | 3 | 16.28 GB| 18.78 GB | very small, high quality loss | | [phind-codellama-34b-python-v1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q3_K_L.gguf) | Q3_K_L | 3 | 17.77 GB| 20.27 GB | small, substantial quality loss | | [phind-codellama-34b-python-v1.Q4_0.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q4_0.gguf) | Q4_0 | 4 | 19.05 GB| 21.55 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [phind-codellama-34b-python-v1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q4_K_S.gguf) | Q4_K_S | 4 | 19.15 GB| 21.65 GB | small, greater quality loss | | [phind-codellama-34b-python-v1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q4_K_M.gguf) | Q4_K_M | 4 | 20.22 GB| 22.72 GB | medium, balanced quality - recommended | | [phind-codellama-34b-python-v1.Q5_0.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q5_0.gguf) | Q5_0 | 5 | 23.24 GB| 25.74 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [phind-codellama-34b-python-v1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q5_K_S.gguf) | Q5_K_S | 5 | 23.24 GB| 25.74 GB | large, low quality loss - recommended | | [phind-codellama-34b-python-v1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q5_K_M.gguf) | Q5_K_M | 5 | 23.84 GB| 26.34 GB | large, very low quality loss - recommended | | [phind-codellama-34b-python-v1.Q6_K.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q6_K.gguf) | Q6_K | 6 | 27.68 GB| 30.18 GB | very large, extremely low quality loss | | [phind-codellama-34b-python-v1.Q8_0.gguf](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF/blob/main/phind-codellama-34b-python-v1.Q8_0.gguf) | Q8_0 | 8 | 35.86 GB| 38.36 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF and below it, a specific filename to download, such as: phind-codellama-34b-python-v1.q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF phind-codellama-34b-python-v1.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF phind-codellama-34b-python-v1.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m phind-codellama-34b-python-v1.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt} \n" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model from Python using ctransformers #### First install the package ```bash # Base ctransformers with no GPU acceleration pip install ctransformers>=0.2.24 # Or with CUDA GPU acceleration pip install ctransformers[cuda]>=0.2.24 # Or with ROCm GPU acceleration CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers ``` #### Simple example code to load one of these GGUF models ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/Phind-CodeLlama-34B-Python-v1-GGUF", model_file="phind-codellama-34b-python-v1.q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here's guides on using llama-cpp-python or ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Phind's Phind CodeLlama 34B Python v1 # **Phind-CodeLlama-34B-Python-v1** We've fine-tuned CodeLlama-34B and CodeLlama-34B-Python on an internal Phind dataset that achieve 67.6% and 69.5% pass@1 on HumanEval, respectively. GPT-4 achieves 67%. We've applied OpenAI's decontamination methodology to our dataset to ensure result validity. More details can be found on our [blog post](https://www.phind.com/blog/code-llama-beats-gpt4). ## Model Details This model is fine-tuned from CodeLlama-34B-Python and achieves 69.5% pass@1 on HumanEval. ## Dataset Details We fined-tuned on a proprietary dataset of ~80k high quality programming problems and solutions. This dataset consists of instruction-answer pairs instead of code completion examples, making it structurally different from HumanEval. The Phind models were trained for 2 epochs, for a total of ~160k examples shown. LoRA was not used -- both models are a native finetune. We used DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in three hours on 32 A100-80GB GPUs. We used a sequence length of 4096 tokens. ## How to Get Started with the Model Make sure to install Transformers from the main git branch: ```bash pip install git+https://github.com/huggingface/transformers.git ``` ## How to Prompt the Model **Please note that this model is somewhat instruction-tuned, but not chat-tuned.** Do not try to use the Llama chat markup with this model. Instead, simply tell it what you want and add "\n: " at the end of your task. For example: ``` Write me a linked list implementation: \n ``` ## How to reproduce HumanEval Results To reproduce our results: ```python from transformers import AutoTokenizer, LlamaForCausalLM from human_eval.data import write_jsonl, read_problems from tqdm import tqdm # initialize the model model_path = "Phind/Phind-CodeLlama-34B-v1" model = LlamaForCausalLM.from_pretrained(model_path, device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_path) # HumanEval helper def generate_one_completion(prompt: str): tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096) # Generate generate_ids = model.generate(inputs.input_ids.to("cuda"), max_new_tokens=256, do_sample=True, top_p=0.75, top_k=40, temperature=0.1) completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] completion = completion.replace(prompt, "").split("\n\n\n")[0] return completion # perform HumanEval problems = read_problems() num_samples_per_task = 1 samples = [ dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"])) for task_id in tqdm(problems) for _ in range(num_samples_per_task) ] write_jsonl("samples.jsonl", samples) # run `evaluate_functional_correctness samples.jsonl` in your HumanEval code sandbox ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments. ## Training details <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> - **Hardware Type:** 32x A100-80GB - **Hours used:** 90 GPU-hours - **Cloud Provider:** AWS - **Compute Region:** us-east-1 <!-- original-model-card end -->
mradermacher/Shiki-m7-i1-GGUF
mradermacher
"2024-06-05T08:42:14Z"
5,111
0
transformers
[ "transformers", "gguf", "en", "base_model:Sao10K/Shiki-m7", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
null
"2024-06-04T22:10:08Z"
--- base_model: Sao10K/Shiki-m7 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Sao10K/Shiki-m7 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Shiki-m7-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Shiki-m7-i1-GGUF/resolve/main/Shiki-m7.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mradermacher/IceBlendedLatteRP-7b-i1-GGUF
mradermacher
"2024-06-04T05:50:29Z"
5,105
0
transformers
[ "transformers", "gguf", "mergekit", "merge", "alpaca", "mistral", "en", "base_model:icefog72/IceBlendedLatteRP-7b", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
null
"2024-06-03T05:22:07Z"
--- base_model: icefog72/IceBlendedLatteRP-7b language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge - alpaca - mistral --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/icefog72/IceBlendedLatteRP-7b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/IceBlendedLatteRP-7b-i1-GGUF/resolve/main/IceBlendedLatteRP-7b.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
lakkeo/stable-cypher-instruct-3b
lakkeo
"2024-07-01T08:06:43Z"
5,105
1
transformers
[ "transformers", "safetensors", "gguf", "stablelm", "text-generation", "causal-lm", "code", "cypher", "graph", "neo4j", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "8-bit", "bitsandbytes", "region:us" ]
text-generation
"2024-06-29T16:08:10Z"
--- license: apache-2.0 language: - en metrics: - bleu - rouge tags: - causal-lm - code - cypher - graph - neo4j inference: false widget: - text: "Show me the people who have Python and Cloud skills and have been in the company for at least 3 years." example_title: "Example 1" - text: "What is the IMDb rating of Pulp Fiction?" example_title: "Example 2" - text: "Display the first 3 users followed by 'Neo4j' who have more than 10000 followers." example_title: "Example 3" --- ## Model Description A specialized 3B parameters model beating SoA models such as GPT4-o at generating CYPHER. It's a finetune of https://huggingface.co/stabilityai/stable-code-instruct-3b trained on https://github.com/neo4j-labs/text2cypher/tree/main/datasets/synthetic_opus_demodbs to generate CYPHER queries from text to query GraphDB such as neo4j. ## Usage ### Safetensors (recommended) ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("lakkeo/stable-cypher-instruct-3b", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("lakkeo/stable-cypher-instruct-3b", torch_dtype=torch.bfloat16, trust_remote_code=True) messages = [ { "role": "user", "content": "Show me the people who have Python and Cloud skills and have been in the company for at least 3 years." } ] prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) inputs = tokenizer([prompt], return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=128, do_sample=True, top_p=0.9, temperature=0.2, pad_token_id=tokenizer.eos_token_id, ) outputs = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=False)[0] ``` ### GGUF ```python from llama_cpp import Llama # Load the GGUF model print("Loading model...") model = Llama( model_path=r"C:\Users\John\stable-cypher-instruct-3b.Q4_K_M.gguf", n_ctx=512, n_batch=512, n_gpu_layers=-1, # Use all available GPU layers max_tokens=128, top_p=0.9, temperature=0.2, verbose=False ) # Define your question question = "Show me the people who have Python and Cloud skills and have been in the company for at least 3 years." # Create the full prompt (simulating the apply_chat_template function) full_prompt = f"<|im_start|>system\nCreate a Cypher statement to answer the following question:<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n" # Generate response print("Generating response...") response = model( full_prompt, max_tokens=128, stop=["<|im_end|>", "<|im_start|>"], echo=False ) # Extract and print the generated response answer = response['choices'][0]['text'].strip() print("\nQuestion:", question) print("\nGenerated Cypher statement:") print(answer) ``` ## Performance | Metric | stable-code-instruct-3b | gpt4-o | stable-cypher-instruct-3b | | :----------: | :---------------------: | :--------: | :-----------------------: | | BLEU-4 | 19.07 | 32.35 | **88.63** | | ROUGE-1 | 39.49 | 69.17 | **95.09** | | ROUGE-2 | 24.82 | 46.97 | **90.71** | | ROUGE-L | 29.63 | 65.24 | **91.51** | | Jaro-Winkler | 52.21 | 86.38 | **95.69** | | Jaccard | 25.55 | 72.80 | **90.78** | | Pass@1 | 0.00 | 0.00 | **51.80** | ### Example ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6504bb76423b46492e7f38c7/pweL4qgmFaknLBYp-CGHm.png) ### Eval params ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6504bb76423b46492e7f38c7/AT80-09XrHNz-dJs9TH3M.png) ## Reproducability This is the config file from Llama Factory : ```json { "top.model_name": "Custom", "top.finetuning_type": "lora", "top.adapter_path": [], "top.quantization_bit": "none", "top.template": "default", "top.rope_scaling": "none", "top.booster": "none", "train.training_stage": "Supervised Fine-Tuning", "train.dataset_dir": "data", "train.dataset": [ "cypher_opus" ], "train.learning_rate": "2e-4", "train.num_train_epochs": "5.0", "train.max_grad_norm": "1.0", "train.max_samples": "5000", "train.compute_type": "fp16", "train.cutoff_len": 256, "train.batch_size": 16, "train.gradient_accumulation_steps": 2, "train.val_size": 0.1, "train.lr_scheduler_type": "cosine", "train.logging_steps": 10, "train.save_steps": 100, "train.warmup_steps": 20, "train.neftune_alpha": 0, "train.optim": "adamw_torch", "train.resize_vocab": false, "train.packing": false, "train.upcast_layernorm": false, "train.use_llama_pro": false, "train.shift_attn": false, "train.report_to": false, "train.num_layer_trainable": 3, "train.name_module_trainable": "all", "train.lora_rank": 64, "train.lora_alpha": 64, "train.lora_dropout": 0.1, "train.loraplus_lr_ratio": 0, "train.create_new_adapter": false, "train.use_rslora": false, "train.use_dora": true, "train.lora_target": "", "train.additional_target": "", "train.dpo_beta": 0.1, "train.dpo_ftx": 0, "train.orpo_beta": 0.1, "train.reward_model": null, "train.use_galore": false, "train.galore_rank": 16, "train.galore_update_interval": 200, "train.galore_scale": 0.25, "train.galore_target": "all" } ``` I used llama.cpp to merge the LoRa and generate the quants. The progress achieved from the base model is significant but you will still need to finetune on your company's syntax and entities. I've been tickering with the training parameters for a few batches of training but there is room for improvements. I'm open to the idea of making a full tutorial if there is enough interest in this project.
microsoft/llava-med-v1.5-mistral-7b
microsoft
"2024-05-14T16:54:10Z"
5,104
23
transformers
[ "transformers", "safetensors", "llava_mistral", "text-generation", "image-text-to-text", "medical", "vision", "arxiv:2306.00890", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-text-to-text
"2024-05-14T15:53:59Z"
--- license: apache-2.0 tags: - image-text-to-text - medical - vision --- # LLaVA-Med v1.5, using [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as LLM for a better commercial license Large Language and Vision Assistant for bioMedicine (i.e., “LLaVA-Med”) is a large language and vision model trained using a curriculum learning method for adapting LLaVA to the biomedical domain. It is an open-source release intended for research use only to facilitate reproducibility of the corresponding paper which claims improved performance for open-ended biomedical questions answering tasks, including common visual question answering (VQA) benchmark datasets such as PathVQA and VQA-RAD. LLaVA-Med was proposed in [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://arxiv.org/abs/2306.00890) by Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao. **Model date:** LLaVA-Med-v1.5-Mistral-7B was trained in April 2024. **Paper or resources for more information:** https://aka.ms/llava-med **Where to send questions or comments about the model:** https://github.com/microsoft/LLaVA-Med/issues ## License [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) license. ## Intended use The data, code, and model checkpoints are intended to be used solely for (I) future research on visual-language processing and (II) reproducibility of the experimental results reported in the reference paper. The data, code, and model checkpoints are not intended to be used in clinical care or for any clinical decision making purposes. ### Primary Intended Use The primary intended use is to support AI researchers reproducing and building on top of this work. LLaVA-Med and its associated models should be helpful for exploring various biomedical vision-language processing (VLP ) and vision question answering (VQA) research questions. ### Out-of-Scope Use Any deployed use case of the model --- commercial or otherwise --- is out of scope. Although we evaluated the models using a broad set of publicly-available research benchmarks, the models and evaluations are intended for research use only and not intended for deployed use cases. Please refer to [the associated paper](https://aka.ms/llava-med) for more details. ## Data This model builds upon [PMC-15M dataset](https://aka.ms/biomedclip-paper), which is a large-scale parallel image-text dataset for biomedical vision-language processing. It contains 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central. It covers a diverse range of biomedical image types, such as microscopy, radiography, histology, and more. ## How to use See the [Serving](https://github.com/microsoft/LLaVA-Med?tab=readme-ov-file#serving) and [Evaluation](https://github.com/microsoft/LLaVA-Med?tab=readme-ov-file#evaluation) sections in the [LLaVA-Med repo](https://aka.ms/llava-med). ## Limitations This model was developed using English corpora, and thus may be considered English-only. This model is evaluated on a narrow set of biomedical benchmark tasks, described in [LLaVA-Med paper](https://aka.ms/llava-med). As such, it is not suitable for use in any clinical setting. Under some conditions, the model may make inaccurate predictions and display limitations, which may require additional mitigation strategies. In particular, this model is likely to carry many of the limitations of the model from which it is derived, [LLaVA](https://llava-vl.github.io/). Further, this model was developed in part using the [PMC-15M](https://aka.ms/biomedclip-paper) dataset. The figure-caption pairs that make up this dataset may contain biases reflecting the current practice of academic publication. For example, the corresponding papers may be enriched for positive findings, contain examples of extreme cases, and otherwise reflect distributions that are not representative of other sources of biomedical data. ### BibTeX entry and citation info ```bibtex @article{li2023llavamed, title={Llava-med: Training a large language-and-vision assistant for biomedicine in one day}, author={Li, Chunyuan and Wong, Cliff and Zhang, Sheng and Usuyama, Naoto and Liu, Haotian and Yang, Jianwei and Naumann, Tristan and Poon, Hoifung and Gao, Jianfeng}, journal={arXiv preprint arXiv:2306.00890}, year={2023} } ```
TheBloke/llama2_7b_chat_uncensored-GGUF
TheBloke
"2023-09-27T12:49:40Z"
5,099
27
transformers
[ "transformers", "gguf", "llama", "dataset:ehartford/wizard_vicuna_70k_unfiltered", "base_model:georgesung/llama2_7b_chat_uncensored", "license:other", "text-generation-inference", "region:us" ]
null
"2023-09-18T07:29:20Z"
--- license: other datasets: - ehartford/wizard_vicuna_70k_unfiltered model_name: Llama2 7B Chat Uncensored base_model: georgesung/llama2_7b_chat_uncensored inference: false model_creator: George Sung model_type: llama prompt_template: '### HUMAN: {prompt} ### RESPONSE: ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama2 7B Chat Uncensored - GGUF - Model creator: [George Sung](https://huggingface.co/georgesung) - Original model: [Llama2 7B Chat Uncensored](https://huggingface.co/georgesung/llama2_7b_chat_uncensored) <!-- description start --> ## Description This repo contains GGUF format model files for [George Sung's Llama2 7B Chat Uncensored](https://huggingface.co/georgesung/llama2_7b_chat_uncensored). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF) * [George Sung's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/georgesung/llama2_7b_chat_uncensored) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Human-Response ``` ### HUMAN: {prompt} ### RESPONSE: ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [George Sung's Llama2 7B Chat Uncensored](https://huggingface.co/georgesung/llama2_7b_chat_uncensored). <!-- licensing end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [llama2_7b_chat_uncensored.Q2_K.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes | | [llama2_7b_chat_uncensored.Q3_K_S.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss | | [llama2_7b_chat_uncensored.Q3_K_M.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss | | [llama2_7b_chat_uncensored.Q3_K_L.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss | | [llama2_7b_chat_uncensored.Q4_0.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [llama2_7b_chat_uncensored.Q4_K_S.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss | | [llama2_7b_chat_uncensored.Q4_K_M.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended | | [llama2_7b_chat_uncensored.Q5_0.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [llama2_7b_chat_uncensored.Q5_K_S.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended | | [llama2_7b_chat_uncensored.Q5_K_M.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended | | [llama2_7b_chat_uncensored.Q6_K.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss | | [llama2_7b_chat_uncensored.Q8_0.gguf](https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGUF/blob/main/llama2_7b_chat_uncensored.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/llama2_7b_chat_uncensored-GGUF and below it, a specific filename to download, such as: llama2_7b_chat_uncensored.q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/llama2_7b_chat_uncensored-GGUF llama2_7b_chat_uncensored.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/llama2_7b_chat_uncensored-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/llama2_7b_chat_uncensored-GGUF llama2_7b_chat_uncensored.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m llama2_7b_chat_uncensored.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### HUMAN:\n{prompt}\n\n### RESPONSE:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model from Python using ctransformers #### First install the package ```bash # Base ctransformers with no GPU acceleration pip install ctransformers>=0.2.24 # Or with CUDA GPU acceleration pip install ctransformers[cuda]>=0.2.24 # Or with ROCm GPU acceleration CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers ``` #### Simple example code to load one of these GGUF models ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/llama2_7b_chat_uncensored-GGUF", model_file="llama2_7b_chat_uncensored.q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here's guides on using llama-cpp-python or ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: George Sung's Llama2 7B Chat Uncensored # Overview Fine-tuned [Llama-2 7B](https://huggingface.co/TheBloke/Llama-2-7B-fp16) with an uncensored/unfiltered Wizard-Vicuna conversation dataset [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered). Used QLoRA for fine-tuning. Trained for one epoch on a 24GB GPU (NVIDIA A10G) instance, took ~19 hours to train. The version here is the fp16 HuggingFace model. ## GGML & GPTQ versions Thanks to [TheBloke](https://huggingface.co/TheBloke), he has created the GGML and GPTQ versions: * https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGML * https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GPTQ # Prompt style The model was trained with the following prompt style: ``` ### HUMAN: Hello ### RESPONSE: Hi, how are you? ### HUMAN: I'm fine. ### RESPONSE: How can I help you? ... ``` # Training code Code used to train the model is available [here](https://github.com/georgesung/llm_qlora). To reproduce the results: ``` git clone https://github.com/georgesung/llm_qlora cd llm_qlora pip install -r requirements.txt python train.py configs/llama2_7b_chat_uncensored.yaml ``` # Fine-tuning guide https://georgesung.github.io/ai/qlora-ift/ <!-- original-model-card end -->
Khalsuu/filipino-wav2vec2-l-xls-r-300m-official
Khalsuu
"2022-05-13T05:58:50Z"
5,094
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:filipino_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
"2022-05-13T03:24:53Z"
--- license: apache-2.0 tags: - generated_from_trainer datasets: - filipino_voice model-index: - name: filipino-wav2vec2-l-xls-r-300m-official results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # filipino-wav2vec2-l-xls-r-300m-official This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4672 - Wer: 0.2922 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3671 | 2.09 | 400 | 0.5584 | 0.5987 | | 0.48 | 4.19 | 800 | 0.4244 | 0.4195 | | 0.2796 | 6.28 | 1200 | 0.3742 | 0.3765 | | 0.1916 | 8.38 | 1600 | 0.4291 | 0.3667 | | 0.1463 | 10.47 | 2000 | 0.3745 | 0.3415 | | 0.1165 | 12.57 | 2400 | 0.4472 | 0.3407 | | 0.0955 | 14.66 | 2800 | 0.4269 | 0.3290 | | 0.0823 | 16.75 | 3200 | 0.4608 | 0.3475 | | 0.0709 | 18.85 | 3600 | 0.4706 | 0.3281 | | 0.0603 | 20.94 | 4000 | 0.4380 | 0.3183 | | 0.0527 | 23.04 | 4400 | 0.4473 | 0.3067 | | 0.0449 | 25.13 | 4800 | 0.4550 | 0.3029 | | 0.041 | 27.23 | 5200 | 0.4671 | 0.3020 | | 0.0358 | 29.32 | 5600 | 0.4672 | 0.2922 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
AliAbdelrasheed/maqa_llama_4bit
AliAbdelrasheed
"2024-06-24T23:07:13Z"
5,094
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "ar", "base_model:maqa_llama_4bit_GGUF", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
text-generation
"2024-06-21T14:28:57Z"
--- base_model: maqa_llama_4bit_GGUF language: - en - ar license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** AliAbdelrasheed - **License:** apache-2.0 - **Finetuned from model :** maqa_llama_4bit_GGUF This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
TheBloke/CodeLlama-70B-Instruct-AWQ
TheBloke
"2024-01-30T23:03:15Z"
5,092
11
transformers
[ "transformers", "safetensors", "llama", "text-generation", "llama-2", "conversational", "code", "arxiv:2308.12950", "base_model:codellama/CodeLlama-70b-Instruct-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]
text-generation
"2024-01-30T18:31:55Z"
--- base_model: codellama/CodeLlama-70b-Instruct-hf inference: false language: - code license: llama2 model_creator: Code Llama model_name: Codellama 70B Instruct model_type: llama pipeline_tag: text-generation prompt_template: "Source: system\n\n {system_message}<step> Source: user\n\n {prompt}\ \ <step> Source: assistant\n \n" quantized_by: TheBloke tags: - llama-2 --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Codellama 70B Instruct - AWQ - Model creator: [Code Llama](https://huggingface.co/codellama) - Original model: [Codellama 70B Instruct](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) <!-- description start --> ## Description This repo contains AWQ model files for [Code Llama's Codellama 70B Instruct](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/CodeLlama-70B-Instruct-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-70B-Instruct-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-70B-Instruct-GGUF) * [Code Llama's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: CodeLlama-70B-Instruct ``` Source: system {system_message}<step> Source: user {prompt} <step> Source: assistant ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/CodeLlama-70B-Instruct-AWQ/tree/main) | 4 | 128 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/viewer/) | 4096 | 36.61 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/CodeLlama-70B-Instruct-AWQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `CodeLlama-70B-Instruct-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/CodeLlama-70B-Instruct-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''Source: system {system_message}<step> Source: user {prompt} <step> Source: assistant ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/CodeLlama-70B-Instruct-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/CodeLlama-70B-Instruct-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''Source: system {system_message}<step> Source: user {prompt} <step> Source: assistant ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/CodeLlama-70B-Instruct-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''Source: system {system_message}<step> Source: user {prompt} <step> Source: assistant ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Code Llama's Codellama 70B Instruct # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | | 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) | Model capabilities: - [x] Code completion. - [ ] Infilling. - [x] Instructions / chat. - [ ] Python specialist. ## Model Use Install `transformers` ```bash pip install transformers accelerate ``` **Chat use:** The 70B Instruct model uses a [different prompt template](#chat_prompt) than the smaller versions. To use it with `transformers`, we recommend you use the built-in chat template: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "codellama/CodeLlama-70b-Instruct-hf" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto", ) chat = [ {"role": "system", "content": "You are a helpful and honest code assistant expert in JavaScript. Please, provide all answers to programming questions in JavaScript"}, {"role": "user", "content": "Write a function that computes the set of sums of all contiguous sublists of a given list."}, ] inputs = tokenizer.apply_chat_template(chat, return_tensors="pt").to("cuda") output = model.generate(input_ids=inputs, max_new_tokens=200) output = output[0].to("cpu") print(tokenizer.decode(output)) ``` You can also use the model for **text or code completion**. This examples uses transformers' `pipeline` interface: ```py from transformers import AutoTokenizer import transformers import torch model_id = "codellama/CodeLlama-70b-hf" tokenizer = AutoTokenizer.from_pretrained(model_id) pipeline = transformers.pipeline( "text-generation", model=model_id, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'def fibonacci(', do_sample=True, temperature=0.2, top_p=0.9, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=100, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` <a name="chat_prompt"></a> ## Chat prompt CodeLlama 70B Instruct uses a different format for the chat prompt than previous Llama 2 or CodeLlama models. As mentioned above, the easiest way to use it is with the help of the tokenizer's chat template. If you need to build the string or tokens, manually, here's how to do it. We'll do our tests with the following made-up dialog: ```py chat = [ {"role": "system", "content": "System prompt "}, {"role": "user", "content": "First user query"}, {"role": "assistant", "content": "Model response to first query"}, {"role": "user", "content": "Second user query"}, ] ``` First, let's see what the prompt looks like if we use the chat template: ```py tokenizer.apply_chat_template(chat, tokenize=False) ``` ``` '<s>Source: system\n\n System prompt <step> Source: user\n\n First user query <step> Source: assistant\n\n Model response to first query <step> Source: user\n\n Second user query <step> Source: assistant\nDestination: user\n\n ' ``` So each turn of the conversation has a `Source` (`system`, `user`, or `assistant`), and then the content appears after two newlines and a space. Turns are separated with the special token ` <step> `. After the last turn (which must necessarily come from the `user`), we invite the model to respond by using the special syntax `Source: assistant\nDestination: user\n\n `. Let's see how we can build the same string ourselves: ```py output = "<s>" for m in chat: output += f"Source: {m['role']}\n\n {m['content'].strip()}" output += " <step> " output += "Source: assistant\nDestination: user\n\n " output ``` ``` '<s>Source: system\n\n System prompt <step> Source: user\n\n First user query <step> Source: assistant\n\n Model response to first query <step> Source: user\n\n Second user query <step> Source: assistant\nDestination: user\n\n ' ``` To verify that we got it right, we'll compare against the [reference code in the original GitHub repo](https://github.com/facebookresearch/codellama/blob/1af62e1f43db1fa5140fa43cb828465a603a48f3/llama/generation.py#L506). We used the same dialog and tokenized it with the `dialog_prompt_tokens` function and got the following tokens: ```py reference_tokens = [1, 7562, 29901, 1788, 13, 13, 2184, 9508, 32015, 7562, 29901, 1404, 13, 13, 3824, 1404, 2346, 32015, 7562, 29901, 20255, 13, 13, 8125, 2933, 304, 937, 2346, 32015, 7562, 29901, 1404, 13, 13, 6440, 1404, 2346, 32015, 7562, 29901, 20255, 13, 14994, 3381, 29901, 1404, 13, 13, 29871] ``` Let's see what we get with the string we built using our Python loop. Note that we don't add "special tokens" because the string already starts with `<s>`, the beginning of sentence token: ```py tokens = tokenizer.encode(output, add_special_tokens=False) assert reference_tokens == tokens ``` Similarly, let's verify that the chat template produces the same token sequence: ```py assert reference_tokens == tokenizer.apply_chat_template(chat) ``` As a final detail, please note that if the dialog does not start with a `system` turn, the [original code will insert one with an empty content string](https://github.com/facebookresearch/codellama/blob/1af62e1f43db1fa5140fa43cb828465a603a48f3/llama/generation.py#L418). ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. **This repository contains the Instruct version of the 70B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant **does not** support long context of up to 100k tokens. **Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. **Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
digiplay/CleanLinearMix
digiplay
"2023-11-04T16:01:36Z"
5,091
4
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2023-11-04T15:40:26Z"
--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/42433?modelVersionId=47110
aurelio-ai/sr-test-vit
aurelio-ai
"2024-06-01T09:58:09Z"
5,087
0
transformers
[ "transformers", "pytorch", "tf", "vit", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
"2024-06-01T09:57:16Z"
Tiny ViT model used for [semantic-router](https://github.com/aurelio-labs/semantic-router) tests.
izhx/udever-bloom-560m
izhx
"2023-11-07T06:57:25Z"
5,082
0
transformers
[ "transformers", "pytorch", "bloom", "feature-extraction", "mteb", "ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu", "arxiv:2310.08232", "license:bigscience-bloom-rail-1.0", "model-index", "endpoints_compatible", "text-generation-inference", "region:us" ]
feature-extraction
"2023-10-24T10:49:45Z"
--- license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu tags: - mteb model-index: - name: udever-bloom-560m results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 25.170024237678657 - type: cos_sim_spearman value: 25.32025098111752 - type: euclidean_pearson value: 25.34284673812859 - type: euclidean_spearman value: 25.52812937004611 - type: manhattan_pearson value: 25.734179522960822 - type: manhattan_spearman value: 25.92247507041032 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 32.3359541791282 - type: cos_sim_spearman value: 33.45815274836323 - type: euclidean_pearson value: 35.14748229440635 - type: euclidean_spearman value: 33.377829932851334 - type: manhattan_pearson value: 35.359130773295625 - type: manhattan_spearman value: 33.524469762932426 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.35820895522389 - type: ap value: 35.45566303125099 - type: f1 value: 66.49474786522534 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 66.423982869379 - type: ap value: 78.32781372746805 - type: f1 value: 64.24959400774807 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.65817091454274 - type: ap value: 21.73416645163647 - type: f1 value: 60.52120070712094 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 56.86295503211991 - type: ap value: 12.906256075113513 - type: f1 value: 46.68625513679152 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 83.8095 - type: ap value: 78.5195717101614 - type: f1 value: 83.74169093676316 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.97 - type: f1 value: 38.57853211177342 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 26.846000000000004 - type: f1 value: 26.473886891677306 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.974 - type: f1 value: 38.31719230291287 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.38799999999999 - type: f1 value: 37.53319978613875 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 28.311999999999998 - type: f1 value: 27.988313617729755 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 35.704 - type: f1 value: 34.863182924437254 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 21.053 - type: map_at_10 value: 35.811 - type: map_at_100 value: 37.035000000000004 - type: map_at_1000 value: 37.055 - type: map_at_3 value: 30.666 - type: map_at_5 value: 33.525 - type: mrr_at_1 value: 21.266 - type: mrr_at_10 value: 35.906 - type: mrr_at_100 value: 37.122 - type: mrr_at_1000 value: 37.141999999999996 - type: mrr_at_3 value: 30.714000000000002 - type: mrr_at_5 value: 33.576 - type: ndcg_at_1 value: 21.053 - type: ndcg_at_10 value: 44.545 - type: ndcg_at_100 value: 49.844 - type: ndcg_at_1000 value: 50.298 - type: ndcg_at_3 value: 33.889 - type: ndcg_at_5 value: 39.059 - type: precision_at_1 value: 21.053 - type: precision_at_10 value: 7.269 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.414 - type: precision_at_5 value: 11.166 - type: recall_at_1 value: 21.053 - type: recall_at_10 value: 72.688 - type: recall_at_100 value: 96.017 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 43.242999999999995 - type: recall_at_5 value: 55.832 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.26646269393896 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.00218289816601 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 57.381567373603424 - type: mrr value: 70.09431473420392 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.14803223261677 - type: cos_sim_spearman value: 84.43626128689064 - type: euclidean_pearson value: 85.03130036472703 - type: euclidean_spearman value: 84.05974668365359 - type: manhattan_pearson value: 85.59339889467545 - type: manhattan_spearman value: 83.86938090025696 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 44.19468290937555 - type: cos_sim_spearman value: 43.93025426799595 - type: euclidean_pearson value: 45.273900549350735 - type: euclidean_spearman value: 45.07419415738924 - type: manhattan_pearson value: 45.469211385235376 - type: manhattan_spearman value: 45.27440191151001 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 11.440501043841337 - type: f1 value: 11.295895880968951 - type: precision value: 11.237446950317073 - type: recall value: 11.440501043841337 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 96.53312788906008 - type: f1 value: 96.18093770636143 - type: precision value: 96.00667693888035 - type: recall value: 96.53312788906008 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 1.6972635954277795 - type: f1 value: 1.5885146938143124 - type: precision value: 1.5581125970067466 - type: recall value: 1.6972635954277795 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 96.31384939441811 - type: f1 value: 96.15587151132175 - type: precision value: 96.07688256977357 - type: recall value: 96.31384939441811 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 80.97402597402598 - type: f1 value: 80.88177660652944 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.266950159712465 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 28.65092446021672 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 35.21075820650184 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 35.121931960714484 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 63.41256934884578 - type: mrr value: 68.6492857142857 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 63.663067375541104 - type: mrr value: 68.92075396825396 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.997 - type: map_at_10 value: 35.477 - type: map_at_100 value: 36.722 - type: map_at_1000 value: 36.849 - type: map_at_3 value: 32.083 - type: map_at_5 value: 33.884 - type: mrr_at_1 value: 32.046 - type: mrr_at_10 value: 41.455999999999996 - type: mrr_at_100 value: 42.214 - type: mrr_at_1000 value: 42.268 - type: mrr_at_3 value: 38.722 - type: mrr_at_5 value: 40.266999999999996 - type: ndcg_at_1 value: 32.046 - type: ndcg_at_10 value: 41.705999999999996 - type: ndcg_at_100 value: 46.695 - type: ndcg_at_1000 value: 49.128 - type: ndcg_at_3 value: 36.6 - type: ndcg_at_5 value: 38.725 - type: precision_at_1 value: 32.046 - type: precision_at_10 value: 8.197000000000001 - type: precision_at_100 value: 1.323 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 18.073 - type: precision_at_5 value: 13.047 - type: recall_at_1 value: 24.997 - type: recall_at_10 value: 54.013 - type: recall_at_100 value: 75.29400000000001 - type: recall_at_1000 value: 91.611 - type: recall_at_3 value: 38.627 - type: recall_at_5 value: 45.019999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.194 - type: map_at_10 value: 30.076000000000004 - type: map_at_100 value: 31.0 - type: map_at_1000 value: 31.125999999999998 - type: map_at_3 value: 28.137 - type: map_at_5 value: 29.206 - type: mrr_at_1 value: 28.535 - type: mrr_at_10 value: 34.833999999999996 - type: mrr_at_100 value: 35.504999999999995 - type: mrr_at_1000 value: 35.57 - type: mrr_at_3 value: 33.089 - type: mrr_at_5 value: 34.115 - type: ndcg_at_1 value: 28.535 - type: ndcg_at_10 value: 34.285 - type: ndcg_at_100 value: 38.286 - type: ndcg_at_1000 value: 41.007 - type: ndcg_at_3 value: 31.395 - type: ndcg_at_5 value: 32.687 - type: precision_at_1 value: 28.535 - type: precision_at_10 value: 6.166 - type: precision_at_100 value: 1.042 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 14.862 - type: precision_at_5 value: 10.331 - type: recall_at_1 value: 23.194 - type: recall_at_10 value: 41.648 - type: recall_at_100 value: 58.999 - type: recall_at_1000 value: 77.46300000000001 - type: recall_at_3 value: 32.931 - type: recall_at_5 value: 36.736999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.899 - type: map_at_10 value: 42.657000000000004 - type: map_at_100 value: 43.717 - type: map_at_1000 value: 43.79 - type: map_at_3 value: 39.635 - type: map_at_5 value: 41.538000000000004 - type: mrr_at_1 value: 36.864999999999995 - type: mrr_at_10 value: 46.137 - type: mrr_at_100 value: 46.946 - type: mrr_at_1000 value: 46.986 - type: mrr_at_3 value: 43.469 - type: mrr_at_5 value: 45.262 - type: ndcg_at_1 value: 36.864999999999995 - type: ndcg_at_10 value: 48.164 - type: ndcg_at_100 value: 52.769999999999996 - type: ndcg_at_1000 value: 54.393 - type: ndcg_at_3 value: 42.887 - type: ndcg_at_5 value: 45.871 - type: precision_at_1 value: 36.864999999999995 - type: precision_at_10 value: 7.843 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 19.352 - type: precision_at_5 value: 13.618 - type: recall_at_1 value: 31.899 - type: recall_at_10 value: 61.131 - type: recall_at_100 value: 81.504 - type: recall_at_1000 value: 93.146 - type: recall_at_3 value: 46.971000000000004 - type: recall_at_5 value: 54.42399999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.621000000000002 - type: map_at_10 value: 23.621 - type: map_at_100 value: 24.636 - type: map_at_1000 value: 24.739 - type: map_at_3 value: 21.623 - type: map_at_5 value: 22.511 - type: mrr_at_1 value: 19.096 - type: mrr_at_10 value: 25.288 - type: mrr_at_100 value: 26.238 - type: mrr_at_1000 value: 26.314 - type: mrr_at_3 value: 23.202 - type: mrr_at_5 value: 24.213 - type: ndcg_at_1 value: 19.096 - type: ndcg_at_10 value: 27.529999999999998 - type: ndcg_at_100 value: 32.763 - type: ndcg_at_1000 value: 35.538 - type: ndcg_at_3 value: 23.362 - type: ndcg_at_5 value: 24.961 - type: precision_at_1 value: 19.096 - type: precision_at_10 value: 4.417999999999999 - type: precision_at_100 value: 0.739 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 9.981 - type: precision_at_5 value: 6.959999999999999 - type: recall_at_1 value: 17.621000000000002 - type: recall_at_10 value: 38.079 - type: recall_at_100 value: 62.499 - type: recall_at_1000 value: 83.783 - type: recall_at_3 value: 26.687 - type: recall_at_5 value: 30.459000000000003 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.019 - type: map_at_10 value: 15.869 - type: map_at_100 value: 17.078 - type: map_at_1000 value: 17.205000000000002 - type: map_at_3 value: 13.794 - type: map_at_5 value: 14.814 - type: mrr_at_1 value: 13.930000000000001 - type: mrr_at_10 value: 19.172 - type: mrr_at_100 value: 20.325 - type: mrr_at_1000 value: 20.415 - type: mrr_at_3 value: 17.122999999999998 - type: mrr_at_5 value: 18.124000000000002 - type: ndcg_at_1 value: 13.930000000000001 - type: ndcg_at_10 value: 19.646 - type: ndcg_at_100 value: 25.684 - type: ndcg_at_1000 value: 29.14 - type: ndcg_at_3 value: 15.614 - type: ndcg_at_5 value: 17.247 - type: precision_at_1 value: 13.930000000000001 - type: precision_at_10 value: 3.868 - type: precision_at_100 value: 0.8 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 7.420999999999999 - type: precision_at_5 value: 5.672 - type: recall_at_1 value: 11.019 - type: recall_at_10 value: 28.116000000000003 - type: recall_at_100 value: 54.794 - type: recall_at_1000 value: 79.838 - type: recall_at_3 value: 17.124 - type: recall_at_5 value: 21.086 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.791 - type: map_at_10 value: 33.442 - type: map_at_100 value: 34.719 - type: map_at_1000 value: 34.849000000000004 - type: map_at_3 value: 30.885 - type: map_at_5 value: 32.245000000000005 - type: mrr_at_1 value: 30.606 - type: mrr_at_10 value: 38.922000000000004 - type: mrr_at_100 value: 39.822 - type: mrr_at_1000 value: 39.881 - type: mrr_at_3 value: 36.622 - type: mrr_at_5 value: 37.907000000000004 - type: ndcg_at_1 value: 30.606 - type: ndcg_at_10 value: 38.867000000000004 - type: ndcg_at_100 value: 44.364 - type: ndcg_at_1000 value: 47.073 - type: ndcg_at_3 value: 34.63 - type: ndcg_at_5 value: 36.479 - type: precision_at_1 value: 30.606 - type: precision_at_10 value: 7.0360000000000005 - type: precision_at_100 value: 1.174 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 16.522000000000002 - type: precision_at_5 value: 11.588 - type: recall_at_1 value: 24.791 - type: recall_at_10 value: 49.736000000000004 - type: recall_at_100 value: 72.67099999999999 - type: recall_at_1000 value: 91.29599999999999 - type: recall_at_3 value: 37.345 - type: recall_at_5 value: 42.400999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.669999999999998 - type: map_at_10 value: 28.605000000000004 - type: map_at_100 value: 29.769000000000002 - type: map_at_1000 value: 29.881999999999998 - type: map_at_3 value: 25.886 - type: map_at_5 value: 27.317999999999998 - type: mrr_at_1 value: 25.457 - type: mrr_at_10 value: 33.423 - type: mrr_at_100 value: 34.269 - type: mrr_at_1000 value: 34.336 - type: mrr_at_3 value: 30.974 - type: mrr_at_5 value: 32.23 - type: ndcg_at_1 value: 25.457 - type: ndcg_at_10 value: 33.785 - type: ndcg_at_100 value: 39.145 - type: ndcg_at_1000 value: 41.772 - type: ndcg_at_3 value: 29.014 - type: ndcg_at_5 value: 31.019999999999996 - type: precision_at_1 value: 25.457 - type: precision_at_10 value: 6.2330000000000005 - type: precision_at_100 value: 1.045 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 13.813 - type: precision_at_5 value: 9.863 - type: recall_at_1 value: 20.669999999999998 - type: recall_at_10 value: 44.651 - type: recall_at_100 value: 68.037 - type: recall_at_1000 value: 86.282 - type: recall_at_3 value: 31.381999999999998 - type: recall_at_5 value: 36.778 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.796583333333338 - type: map_at_10 value: 26.900166666666664 - type: map_at_100 value: 27.956583333333334 - type: map_at_1000 value: 28.08083333333333 - type: map_at_3 value: 24.598416666666665 - type: map_at_5 value: 25.81791666666667 - type: mrr_at_1 value: 23.68591666666667 - type: mrr_at_10 value: 30.65558333333333 - type: mrr_at_100 value: 31.503583333333335 - type: mrr_at_1000 value: 31.576083333333333 - type: mrr_at_3 value: 28.50525 - type: mrr_at_5 value: 29.690666666666665 - type: ndcg_at_1 value: 23.68591666666667 - type: ndcg_at_10 value: 31.425000000000004 - type: ndcg_at_100 value: 36.34316666666666 - type: ndcg_at_1000 value: 39.164249999999996 - type: ndcg_at_3 value: 27.330083333333338 - type: ndcg_at_5 value: 29.14408333333333 - type: precision_at_1 value: 23.68591666666667 - type: precision_at_10 value: 5.5862500000000015 - type: precision_at_100 value: 0.9571666666666666 - type: precision_at_1000 value: 0.13866666666666666 - type: precision_at_3 value: 12.663499999999999 - type: precision_at_5 value: 9.035333333333332 - type: recall_at_1 value: 19.796583333333338 - type: recall_at_10 value: 41.289416666666675 - type: recall_at_100 value: 63.251250000000006 - type: recall_at_1000 value: 83.4515 - type: recall_at_3 value: 29.727916666666665 - type: recall_at_5 value: 34.45824999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.121 - type: map_at_10 value: 22.104 - type: map_at_100 value: 23.003 - type: map_at_1000 value: 23.108 - type: map_at_3 value: 20.233 - type: map_at_5 value: 21.186 - type: mrr_at_1 value: 18.865000000000002 - type: mrr_at_10 value: 24.951 - type: mrr_at_100 value: 25.779000000000003 - type: mrr_at_1000 value: 25.863999999999997 - type: mrr_at_3 value: 23.083000000000002 - type: mrr_at_5 value: 24.049 - type: ndcg_at_1 value: 18.865000000000002 - type: ndcg_at_10 value: 26.031 - type: ndcg_at_100 value: 30.589 - type: ndcg_at_1000 value: 33.565 - type: ndcg_at_3 value: 22.369 - type: ndcg_at_5 value: 23.932000000000002 - type: precision_at_1 value: 18.865000000000002 - type: precision_at_10 value: 4.324999999999999 - type: precision_at_100 value: 0.722 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 10.072000000000001 - type: precision_at_5 value: 7.086 - type: recall_at_1 value: 16.121 - type: recall_at_10 value: 35.577 - type: recall_at_100 value: 56.298 - type: recall_at_1000 value: 79.089 - type: recall_at_3 value: 25.239 - type: recall_at_5 value: 29.242 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 10.968 - type: map_at_10 value: 15.639 - type: map_at_100 value: 16.459 - type: map_at_1000 value: 16.584 - type: map_at_3 value: 14.127 - type: map_at_5 value: 14.911 - type: mrr_at_1 value: 13.73 - type: mrr_at_10 value: 18.822 - type: mrr_at_100 value: 19.592000000000002 - type: mrr_at_1000 value: 19.683999999999997 - type: mrr_at_3 value: 17.223 - type: mrr_at_5 value: 18.082 - type: ndcg_at_1 value: 13.73 - type: ndcg_at_10 value: 18.881999999999998 - type: ndcg_at_100 value: 23.182 - type: ndcg_at_1000 value: 26.479000000000003 - type: ndcg_at_3 value: 16.067999999999998 - type: ndcg_at_5 value: 17.265 - type: precision_at_1 value: 13.73 - type: precision_at_10 value: 3.544 - type: precision_at_100 value: 0.679 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 7.674 - type: precision_at_5 value: 5.561 - type: recall_at_1 value: 10.968 - type: recall_at_10 value: 25.596000000000004 - type: recall_at_100 value: 45.411 - type: recall_at_1000 value: 69.555 - type: recall_at_3 value: 17.582 - type: recall_at_5 value: 20.785 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.886 - type: map_at_10 value: 27.029999999999998 - type: map_at_100 value: 27.968 - type: map_at_1000 value: 28.108 - type: map_at_3 value: 25.001 - type: map_at_5 value: 26.185000000000002 - type: mrr_at_1 value: 24.067 - type: mrr_at_10 value: 30.756 - type: mrr_at_100 value: 31.593 - type: mrr_at_1000 value: 31.685999999999996 - type: mrr_at_3 value: 28.793999999999997 - type: mrr_at_5 value: 29.997 - type: ndcg_at_1 value: 24.067 - type: ndcg_at_10 value: 31.095 - type: ndcg_at_100 value: 35.893 - type: ndcg_at_1000 value: 39.158 - type: ndcg_at_3 value: 27.321 - type: ndcg_at_5 value: 29.247 - type: precision_at_1 value: 24.067 - type: precision_at_10 value: 5.103 - type: precision_at_100 value: 0.8460000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.601 - type: recall_at_1 value: 20.886 - type: recall_at_10 value: 39.797 - type: recall_at_100 value: 61.399 - type: recall_at_1000 value: 84.555 - type: recall_at_3 value: 29.721999999999998 - type: recall_at_5 value: 34.455999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.394 - type: map_at_10 value: 28.303 - type: map_at_100 value: 29.726000000000003 - type: map_at_1000 value: 29.955 - type: map_at_3 value: 25.705 - type: map_at_5 value: 26.989 - type: mrr_at_1 value: 25.691999999999997 - type: mrr_at_10 value: 32.495000000000005 - type: mrr_at_100 value: 33.461999999999996 - type: mrr_at_1000 value: 33.534000000000006 - type: mrr_at_3 value: 30.137999999999998 - type: mrr_at_5 value: 31.383 - type: ndcg_at_1 value: 25.691999999999997 - type: ndcg_at_10 value: 33.300000000000004 - type: ndcg_at_100 value: 39.062000000000005 - type: ndcg_at_1000 value: 42.176 - type: ndcg_at_3 value: 28.859 - type: ndcg_at_5 value: 30.805 - type: precision_at_1 value: 25.691999999999997 - type: precision_at_10 value: 6.383 - type: precision_at_100 value: 1.387 - type: precision_at_1000 value: 0.22899999999999998 - type: precision_at_3 value: 13.439 - type: precision_at_5 value: 9.959999999999999 - type: recall_at_1 value: 21.394 - type: recall_at_10 value: 42.853 - type: recall_at_100 value: 69.284 - type: recall_at_1000 value: 89.646 - type: recall_at_3 value: 29.786 - type: recall_at_5 value: 34.797 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.999 - type: map_at_10 value: 19.979 - type: map_at_100 value: 20.682000000000002 - type: map_at_1000 value: 20.775 - type: map_at_3 value: 18.072 - type: map_at_5 value: 19.028 - type: mrr_at_1 value: 15.342 - type: mrr_at_10 value: 21.611 - type: mrr_at_100 value: 22.298000000000002 - type: mrr_at_1000 value: 22.375 - type: mrr_at_3 value: 19.624 - type: mrr_at_5 value: 20.659 - type: ndcg_at_1 value: 15.342 - type: ndcg_at_10 value: 23.809 - type: ndcg_at_100 value: 27.685 - type: ndcg_at_1000 value: 30.542 - type: ndcg_at_3 value: 19.842000000000002 - type: ndcg_at_5 value: 21.490000000000002 - type: precision_at_1 value: 15.342 - type: precision_at_10 value: 3.9190000000000005 - type: precision_at_100 value: 0.627 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 8.688 - type: precision_at_5 value: 6.1370000000000005 - type: recall_at_1 value: 13.999 - type: recall_at_10 value: 34.276 - type: recall_at_100 value: 52.825 - type: recall_at_1000 value: 75.154 - type: recall_at_3 value: 23.339 - type: recall_at_5 value: 27.314 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 8.27 - type: map_at_10 value: 14.161999999999999 - type: map_at_100 value: 15.775 - type: map_at_1000 value: 15.947 - type: map_at_3 value: 11.701 - type: map_at_5 value: 12.952 - type: mrr_at_1 value: 18.632 - type: mrr_at_10 value: 28.871000000000002 - type: mrr_at_100 value: 29.985 - type: mrr_at_1000 value: 30.037999999999997 - type: mrr_at_3 value: 25.451 - type: mrr_at_5 value: 27.366 - type: ndcg_at_1 value: 18.632 - type: ndcg_at_10 value: 21.017 - type: ndcg_at_100 value: 28.022999999999996 - type: ndcg_at_1000 value: 31.518 - type: ndcg_at_3 value: 16.611 - type: ndcg_at_5 value: 18.149 - type: precision_at_1 value: 18.632 - type: precision_at_10 value: 6.736000000000001 - type: precision_at_100 value: 1.414 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 12.313 - type: precision_at_5 value: 9.759 - type: recall_at_1 value: 8.27 - type: recall_at_10 value: 26.218999999999998 - type: recall_at_100 value: 50.77 - type: recall_at_1000 value: 70.8 - type: recall_at_3 value: 15.526000000000002 - type: recall_at_5 value: 19.724 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 10.598 - type: map_at_10 value: 15.869 - type: map_at_100 value: 17.081 - type: map_at_1000 value: 17.267 - type: map_at_3 value: 13.877 - type: map_at_5 value: 14.884 - type: mrr_at_1 value: 17.279 - type: mrr_at_10 value: 22.554 - type: mrr_at_100 value: 23.521 - type: mrr_at_1000 value: 23.619 - type: mrr_at_3 value: 20.647 - type: mrr_at_5 value: 21.625 - type: ndcg_at_1 value: 17.279 - type: ndcg_at_10 value: 20.029 - type: ndcg_at_100 value: 25.968000000000004 - type: ndcg_at_1000 value: 30.158 - type: ndcg_at_3 value: 16.947000000000003 - type: ndcg_at_5 value: 18.069 - type: precision_at_1 value: 17.279 - type: precision_at_10 value: 4.704 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 9.777 - type: precision_at_5 value: 7.207 - type: recall_at_1 value: 10.598 - type: recall_at_10 value: 26.034000000000002 - type: recall_at_100 value: 51.385999999999996 - type: recall_at_1000 value: 80.49 - type: recall_at_3 value: 16.834 - type: recall_at_5 value: 20.317 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 70.40288634996993 - type: cos_sim_ap value: 78.43387766087626 - type: cos_sim_f1 value: 73.09982840415867 - type: cos_sim_precision value: 64.31616341030195 - type: cos_sim_recall value: 84.66214636427402 - type: dot_accuracy value: 65.52014431749849 - type: dot_ap value: 70.89507344960353 - type: dot_f1 value: 70.7030509759333 - type: dot_precision value: 59.43922255854708 - type: dot_recall value: 87.2340425531915 - type: euclidean_accuracy value: 69.84966927239927 - type: euclidean_ap value: 78.08825177727368 - type: euclidean_f1 value: 72.68394399761692 - type: euclidean_precision value: 63.16879530548844 - type: euclidean_recall value: 85.57400046761748 - type: manhattan_accuracy value: 69.9579073962718 - type: manhattan_ap value: 78.38355697667261 - type: manhattan_f1 value: 73.06507508663844 - type: manhattan_precision value: 62.10112911143839 - type: manhattan_recall value: 88.73041851765257 - type: max_accuracy value: 70.40288634996993 - type: max_ap value: 78.43387766087626 - type: max_f1 value: 73.09982840415867 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 23.973 - type: map_at_10 value: 30.074 - type: map_at_100 value: 31.05 - type: map_at_1000 value: 31.147000000000002 - type: map_at_3 value: 27.977 - type: map_at_5 value: 29.247 - type: mrr_at_1 value: 24.025 - type: mrr_at_10 value: 30.093999999999998 - type: mrr_at_100 value: 31.068 - type: mrr_at_1000 value: 31.165 - type: mrr_at_3 value: 27.994000000000003 - type: mrr_at_5 value: 29.243000000000002 - type: ndcg_at_1 value: 24.025 - type: ndcg_at_10 value: 33.566 - type: ndcg_at_100 value: 38.818999999999996 - type: ndcg_at_1000 value: 41.477000000000004 - type: ndcg_at_3 value: 29.293000000000003 - type: ndcg_at_5 value: 31.564999999999998 - type: precision_at_1 value: 24.025 - type: precision_at_10 value: 4.489 - type: precision_at_100 value: 0.709 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 11.064 - type: precision_at_5 value: 7.734000000000001 - type: recall_at_1 value: 23.973 - type: recall_at_10 value: 44.731 - type: recall_at_100 value: 70.52199999999999 - type: recall_at_1000 value: 91.491 - type: recall_at_3 value: 33.087 - type: recall_at_5 value: 38.567 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.950000000000001 - type: map_at_10 value: 13.236999999999998 - type: map_at_100 value: 16.137 - type: map_at_1000 value: 16.785 - type: map_at_3 value: 10.378 - type: map_at_5 value: 11.62 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 61.861 - type: mrr_at_100 value: 62.436 - type: mrr_at_1000 value: 62.456 - type: mrr_at_3 value: 60.458 - type: mrr_at_5 value: 61.208 - type: ndcg_at_1 value: 43.75 - type: ndcg_at_10 value: 28.224 - type: ndcg_at_100 value: 29.244999999999997 - type: ndcg_at_1000 value: 34.410000000000004 - type: ndcg_at_3 value: 33.955 - type: ndcg_at_5 value: 30.597 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 20.825 - type: precision_at_100 value: 5.462 - type: precision_at_1000 value: 1.1320000000000001 - type: precision_at_3 value: 37.0 - type: precision_at_5 value: 28.849999999999998 - type: recall_at_1 value: 6.950000000000001 - type: recall_at_10 value: 17.159 - type: recall_at_100 value: 31.657999999999998 - type: recall_at_1000 value: 49.155 - type: recall_at_3 value: 11.393 - type: recall_at_5 value: 13.568 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 16.333000000000002 - type: map_at_10 value: 44.080999999999996 - type: map_at_100 value: 47.958 - type: map_at_1000 value: 48.183 - type: map_at_3 value: 31.468 - type: map_at_5 value: 38.213 - type: mrr_at_1 value: 63.0 - type: mrr_at_10 value: 72.006 - type: mrr_at_100 value: 72.299 - type: mrr_at_1000 value: 72.313 - type: mrr_at_3 value: 70.375 - type: mrr_at_5 value: 71.33 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 56.044000000000004 - type: ndcg_at_100 value: 63.629999999999995 - type: ndcg_at_1000 value: 66.156 - type: ndcg_at_3 value: 55.85 - type: ndcg_at_5 value: 53.559 - type: precision_at_1 value: 63.0 - type: precision_at_10 value: 27.279999999999998 - type: precision_at_100 value: 4.005 - type: precision_at_1000 value: 0.462 - type: precision_at_3 value: 49.633 - type: precision_at_5 value: 40.6 - type: recall_at_1 value: 16.333000000000002 - type: recall_at_10 value: 57.152 - type: recall_at_100 value: 80.231 - type: recall_at_1000 value: 92.95400000000001 - type: recall_at_3 value: 34.793 - type: recall_at_5 value: 44.989000000000004 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 33.7 - type: map_at_10 value: 42.327999999999996 - type: map_at_100 value: 43.230000000000004 - type: map_at_1000 value: 43.274 - type: map_at_3 value: 39.883 - type: map_at_5 value: 41.178 - type: mrr_at_1 value: 33.7 - type: mrr_at_10 value: 42.327999999999996 - type: mrr_at_100 value: 43.230000000000004 - type: mrr_at_1000 value: 43.274 - type: mrr_at_3 value: 39.883 - type: mrr_at_5 value: 41.178 - type: ndcg_at_1 value: 33.7 - type: ndcg_at_10 value: 46.996 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 52.823 - type: ndcg_at_3 value: 41.891 - type: ndcg_at_5 value: 44.232 - type: precision_at_1 value: 33.7 - type: precision_at_10 value: 6.1899999999999995 - type: precision_at_100 value: 0.8410000000000001 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 15.9 - type: precision_at_5 value: 10.68 - type: recall_at_1 value: 33.7 - type: recall_at_10 value: 61.9 - type: recall_at_100 value: 84.1 - type: recall_at_1000 value: 93.60000000000001 - type: recall_at_3 value: 47.699999999999996 - type: recall_at_5 value: 53.400000000000006 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.76500000000001 - type: f1 value: 40.46330006682868 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 45.078 - type: map_at_10 value: 55.443 - type: map_at_100 value: 56.03900000000001 - type: map_at_1000 value: 56.067 - type: map_at_3 value: 53.174 - type: map_at_5 value: 54.510999999999996 - type: mrr_at_1 value: 48.575 - type: mrr_at_10 value: 59.194 - type: mrr_at_100 value: 59.760999999999996 - type: mrr_at_1000 value: 59.784000000000006 - type: mrr_at_3 value: 56.896 - type: mrr_at_5 value: 58.282000000000004 - type: ndcg_at_1 value: 48.575 - type: ndcg_at_10 value: 61.096 - type: ndcg_at_100 value: 63.94800000000001 - type: ndcg_at_1000 value: 64.68199999999999 - type: ndcg_at_3 value: 56.58 - type: ndcg_at_5 value: 58.928000000000004 - type: precision_at_1 value: 48.575 - type: precision_at_10 value: 8.18 - type: precision_at_100 value: 0.968 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 22.662 - type: precision_at_5 value: 14.881 - type: recall_at_1 value: 45.078 - type: recall_at_10 value: 75.057 - type: recall_at_100 value: 88.05199999999999 - type: recall_at_1000 value: 93.58999999999999 - type: recall_at_3 value: 62.77700000000001 - type: recall_at_5 value: 68.50699999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 11.097999999999999 - type: map_at_10 value: 18.288 - type: map_at_100 value: 19.903000000000002 - 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type: map_at_5 value: 7.8100000000000005 - type: mrr_at_1 value: 37.461 - type: mrr_at_10 value: 45.839999999999996 - type: mrr_at_100 value: 46.513 - type: mrr_at_1000 value: 46.571 - type: mrr_at_3 value: 43.55 - type: mrr_at_5 value: 44.773 - type: ndcg_at_1 value: 35.913000000000004 - type: ndcg_at_10 value: 27.340999999999998 - type: ndcg_at_100 value: 25.197000000000003 - type: ndcg_at_1000 value: 34.632000000000005 - type: ndcg_at_3 value: 31.952 - type: ndcg_at_5 value: 30.244 - type: precision_at_1 value: 37.461 - type: precision_at_10 value: 20.495 - type: precision_at_100 value: 6.551 - type: precision_at_1000 value: 1.966 - type: precision_at_3 value: 30.753000000000004 - type: precision_at_5 value: 26.935 - type: recall_at_1 value: 3.7060000000000004 - type: recall_at_10 value: 12.958 - type: recall_at_100 value: 26.582 - type: recall_at_1000 value: 59.724 - type: recall_at_3 value: 7.503 - type: recall_at_5 value: 9.808 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 22.201999999999998 - type: map_at_10 value: 33.76 - type: map_at_100 value: 34.867 - type: map_at_1000 value: 34.92 - type: map_at_3 value: 30.233999999999998 - type: map_at_5 value: 32.291 - type: mrr_at_1 value: 25.232 - type: mrr_at_10 value: 36.239 - type: mrr_at_100 value: 37.119 - type: mrr_at_1000 value: 37.162 - type: mrr_at_3 value: 33.213 - type: mrr_at_5 value: 35.02 - type: ndcg_at_1 value: 25.232 - type: ndcg_at_10 value: 40.046 - type: ndcg_at_100 value: 45.025 - type: ndcg_at_1000 value: 46.459 - type: ndcg_at_3 value: 33.343 - type: ndcg_at_5 value: 36.801 - type: precision_at_1 value: 25.232 - type: precision_at_10 value: 6.796 - type: precision_at_100 value: 0.959 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 15.276 - type: precision_at_5 value: 11.17 - type: recall_at_1 value: 22.201999999999998 - type: recall_at_10 value: 56.733 - type: recall_at_100 value: 79.041 - type: recall_at_1000 value: 90.08500000000001 - type: recall_at_3 value: 39.412000000000006 - type: recall_at_5 value: 47.352 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 62.53383865728208 - type: cos_sim_ap value: 66.3197921045625 - type: cos_sim_f1 value: 69.3385214007782 - type: cos_sim_precision value: 54.89833641404805 - type: cos_sim_recall value: 94.08658922914466 - type: dot_accuracy value: 59.7184623714131 - type: dot_ap value: 61.53586693000539 - type: dot_f1 value: 68.26923076923077 - type: dot_precision value: 52.53272623790552 - type: dot_recall value: 97.46568109820485 - type: euclidean_accuracy value: 62.912831618841366 - type: euclidean_ap value: 67.15479155849464 - type: euclidean_f1 value: 70.64071370640713 - type: euclidean_precision value: 57.34035549703752 - type: euclidean_recall value: 91.97465681098205 - type: manhattan_accuracy value: 63.50839198700595 - type: manhattan_ap value: 67.55807251483273 - type: manhattan_f1 value: 70.58356490670901 - type: manhattan_precision value: 56.55216284987278 - type: manhattan_recall value: 93.8753959873284 - type: max_accuracy value: 63.50839198700595 - type: max_ap value: 67.55807251483273 - type: max_f1 value: 70.64071370640713 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 87.11 - type: ap value: 84.20351278644551 - type: f1 value: 87.10043002123766 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 13.050279647770473 - type: cos_sim_spearman value: 14.227909232579874 - type: euclidean_pearson value: 16.372629300358096 - type: euclidean_spearman value: 14.68140021547196 - type: manhattan_pearson value: 16.266960163157336 - type: manhattan_spearman value: 14.627750758965616 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 30.56036276943463 - type: cos_sim_spearman value: 32.918859292204 - type: euclidean_pearson value: 31.679745438037195 - type: euclidean_spearman value: 33.68461814972644 - type: manhattan_pearson value: 31.994557954084563 - type: manhattan_spearman value: 33.97758185204816 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 68.327 - type: map_at_10 value: 81.938 - type: map_at_100 value: 82.581 - type: map_at_1000 value: 82.60300000000001 - type: map_at_3 value: 78.89399999999999 - type: map_at_5 value: 80.816 - type: mrr_at_1 value: 78.75 - type: mrr_at_10 value: 85.302 - type: mrr_at_100 value: 85.432 - type: mrr_at_1000 value: 85.434 - type: mrr_at_3 value: 84.128 - type: mrr_at_5 value: 84.91199999999999 - type: ndcg_at_1 value: 78.74 - type: ndcg_at_10 value: 86.042 - type: ndcg_at_100 value: 87.468 - type: ndcg_at_1000 value: 87.641 - type: ndcg_at_3 value: 82.799 - type: ndcg_at_5 value: 84.603 - type: precision_at_1 value: 78.74 - type: precision_at_10 value: 13.071 - type: precision_at_100 value: 1.508 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.08 - type: precision_at_5 value: 23.87 - type: recall_at_1 value: 68.327 - type: recall_at_10 value: 93.962 - type: recall_at_100 value: 99.054 - type: recall_at_1000 value: 99.9 - type: recall_at_3 value: 84.788 - type: recall_at_5 value: 89.73 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 41.337989152483956 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 51.2046136625677 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - 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type: recall_at_1000 value: 58.318000000000005 - type: recall_at_3 value: 8.312999999999999 - type: recall_at_5 value: 11.238 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.33402689861924 - type: cos_sim_spearman value: 78.52738315932625 - type: euclidean_pearson value: 80.800678573052 - type: euclidean_spearman value: 77.86666946799137 - type: manhattan_pearson value: 81.03106755866989 - type: manhattan_spearman value: 78.0676393879487 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 81.86998503723257 - type: cos_sim_spearman value: 74.07437934108376 - type: euclidean_pearson value: 80.91626452869946 - type: euclidean_spearman value: 76.88419802521403 - type: manhattan_pearson value: 81.50196980117957 - type: manhattan_spearman value: 77.2456891009073 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.19616084290932 - type: cos_sim_spearman value: 81.80834431353927 - type: euclidean_pearson value: 81.25429737195789 - type: euclidean_spearman value: 82.00934127307355 - type: manhattan_pearson value: 81.67403556759655 - type: manhattan_spearman value: 82.42359818976753 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.50884725941148 - type: cos_sim_spearman value: 77.0493522248929 - type: euclidean_pearson value: 79.15856111178543 - type: euclidean_spearman value: 77.24292975474096 - type: manhattan_pearson value: 79.22641788874807 - type: manhattan_spearman value: 77.37101663798234 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - 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type: mrr_at_1000 value: 51.31099999999999 - type: mrr_at_3 value: 48.283 - type: mrr_at_5 value: 49.633 - type: ndcg_at_1 value: 41.8 - type: ndcg_at_10 value: 55.071999999999996 - type: ndcg_at_100 value: 58.604 - type: ndcg_at_1000 value: 59.679 - type: ndcg_at_3 value: 50.394000000000005 - type: ndcg_at_5 value: 52.825 - type: precision_at_1 value: 41.8 - type: precision_at_10 value: 6.93 - type: precision_at_100 value: 0.861 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 18.833 - type: precision_at_5 value: 12.479999999999999 - type: recall_at_1 value: 41.8 - type: recall_at_10 value: 69.3 - type: recall_at_100 value: 86.1 - type: recall_at_1000 value: 94.6 - type: recall_at_3 value: 56.49999999999999 - type: recall_at_5 value: 62.4 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 80.65 - type: ap value: 59.927241826012924 - type: f1 value: 78.72456184299979 --- # Model Card for udever-bloom <!-- Provide a quick summary of what the model is/does. --> `udever-bloom-560m` is finetuned from [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) via [BitFit](https://aclanthology.org/2022.acl-short.1/) on MS MARCO Passage Ranking, SNLI and MultiNLI data. It is a universal embedding model across tasks, natural and programming languages. (From the technical view, `udever` is merely with some minor improvements to `sgpt-bloom`) <img width="338" height="259" src="https://user-images.githubusercontent.com/26690193/277643721-cdb7f227-cae5-40e1-b6e1-a201bde00339.png" /> ## Model Details ### Model Description - **Developed by:** Alibaba Group - **Model type:** Transformer-based Language Model (decoder-only) - **Language(s) (NLP):** Multiple; see [bloom training data](https://huggingface.co/bigscience/bloom-560m#training-data) - **Finetuned from model :** [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) ### Model Sources <!-- Provide the basic links for the model. --> - **Repository:** [github.com/izhx/uni-rep](https://github.com/izhx/uni-rep) - **Paper :** [Language Models are Universal Embedders](https://arxiv.org/pdf/2310.08232.pdf) - **Training Date :** 2023-06 ### Checkpoints - [udever-bloom-560m](https://huggingface.co/izhx/udever-bloom-560m) - [udever-bloom-1b1](https://huggingface.co/izhx/udever-bloom-1b1) - [udever-bloom-3b](https://huggingface.co/izhx/udever-bloom-3b) - [udever-bloom-7b1](https://huggingface.co/izhx/udever-bloom-7b1) On ModelScope / 魔搭社区: [udever-bloom-560m](https://modelscope.cn/models/damo/udever-bloom-560m), [udever-bloom-1b1](https://modelscope.cn/models/damo/udever-bloom-1b1), [udever-bloom-3b](https://modelscope.cn/models/damo/udever-bloom-3b), [udever-bloom-7b1](https://modelscope.cn/models/damo/udever-bloom-7b1) ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch from transformers import AutoTokenizer, BloomModel tokenizer = AutoTokenizer.from_pretrained('izhx/udever-bloom-560m') model = BloomModel.from_pretrained('izhx/udever-bloom-560m') boq, eoq, bod, eod = '[BOQ]', '[EOQ]', '[BOD]', '[EOD]' eoq_id, eod_id = tokenizer.convert_tokens_to_ids([eoq, eod]) if tokenizer.padding_side != 'left': print('!!!', tokenizer.padding_side) tokenizer.padding_side = 'left' def encode(texts: list, is_query: bool = True, max_length=300): bos = boq if is_query else bod eos_id = eoq_id if is_query else eod_id texts = [bos + t for t in texts] encoding = tokenizer( texts, truncation=True, max_length=max_length - 1, padding=True ) for ids, mask in zip(encoding['input_ids'], encoding['attention_mask']): ids.append(eos_id) mask.append(1) inputs = tokenizer.pad(encoding, return_tensors='pt') with torch.inference_mode(): outputs = model(**inputs) embeds = outputs.last_hidden_state[:, -1] return embeds encode(['I am Bert', 'You are Elmo']) ``` ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> - MS MARCO Passage Ranking, retrieved by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86) - SNLI and MultiNLI (https://sbert.net/datasets/AllNLI.tsv.gz) ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing MS MARCO hard negatives provided by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86). Negatives for SNLI and MultiNLI are randomly sampled. #### Training Hyperparameters - **Training regime:** tf32, BitFit - **Batch size:** 1024 - **Epochs:** 3 - **Optimizer:** AdamW - **Learning rate:** 1e-4 - **Scheduler:** constant with warmup. - **Warmup:** 0.25 epoch ## Evaluation ### Table 1: Massive Text Embedding Benchmark [MTEB](https://huggingface.co/spaces/mteb/leaderboard) | MTEB | Avg. | Class. | Clust. | PairClass. | Rerank. | Retr. | STS | Summ. | |-----------------------------|--------------|--------------|--------------|--------------|--------------|--------------|--------------|--------| | #Datasets ➡️ | 56 | 12 | 11 | 3 | 4 | 15 | 10 | 1 | || | bge-large-en-v1.5 | **64.23** | **75.97** | 46.08| **87.12** | **60.03** | **54.29** | 83.11| 31.61 | | bge-base-en-v1.5 | 63.55| 75.53| 45.77| 86.55| 58.86| 53.25| 82.4| 31.07 | | gte-large | 63.13| 73.33| **46.84** | 85| 59.13| 52.22| **83.35** | 31.66 | | gte-base | 62.39| 73.01| 46.2| 84.57| 58.61| 51.14| 82.3| 31.17 | | e5-large-v2 | 62.25| 75.24| 44.49| 86.03| 56.61| 50.56| 82.05| 30.19 | | instructor-xl | 61.79| 73.12| 44.74| 86.62| 57.29| 49.26| 83.06| 32.32 | | instructor-large | 61.59| 73.86| 45.29| 85.89| 57.54| 47.57| 83.15| 31.84 | | e5-base-v2 | 61.5 | 73.84| 43.8| 85.73| 55.91| 50.29| 81.05| 30.28 | | e5-large | 61.42| 73.14| 43.33| 85.94| 56.53| 49.99| 82.06| 30.97 | | text-embedding-ada-002 (OpenAI API) | 60.99| 70.93| 45.9 | 84.89| 56.32| 49.25| 80.97| 30.8 | | e5-base | 60.44| 72.63| 42.11| 85.09| 55.7 | 48.75| 80.96| 31.01 | | SGPT-5.8B-msmarco | 58.93| 68.13| 40.34| 82 | 56.56| 50.25| 78.1 | 31.46 | | sgpt-bloom-7b1-msmarco | 57.59| 66.19| 38.93| 81.9 | 55.65| 48.22| 77.74| **33.6** | || | Udever-bloom-560m | 55.80| 68.04| 36.89| 81.05| 52.60| 41.19| 79.93| 32.06 | | Udever-bloom-1b1 | 58.28| 70.18| 39.11| 83.11| 54.28| 45.27| 81.52| 31.10 | | Udever-bloom-3b | 59.86| 71.91| 40.74| 84.06| 54.90| 47.67| 82.37| 30.62 | | Udever-bloom-7b1 | 60.63 | 72.13| 40.81| 85.40| 55.91| 49.34| 83.01| 30.97 | ### Table 2: [CodeSearchNet](https://github.com/github/CodeSearchNet) | CodeSearchNet | Go | Ruby | Python | Java | JS | PHP | Avg. | |-|-|-|-|-|-|-|-| | CodeBERT | 69.3 | 70.6 | 84.0 | 86.8 | 74.8 | 70.6 | 76.0 | | GraphCodeBERT | 84.1 | 73.2 | 87.9 | 75.7 | 71.1 | 72.5 | 77.4 | | cpt-code S | **97.7** | **86.3** | 99.8 | 94.0 | 86.0 | 96.7 | 93.4 | | cpt-code M | 97.5 | 85.5 | **99.9** | **94.4** | **86.5** | **97.2** | **93.5** | | sgpt-bloom-7b1-msmarco | 76.79 | 69.25 | 95.68 | 77.93 | 70.35 | 73.45 | 77.24 | || | Udever-bloom-560m | 75.38 | 66.67 | 96.23 | 78.99 | 69.39 | 73.69 | 76.73 | | Udever-bloom-1b1 | 78.76 | 72.85 | 97.67 | 82.77 | 74.38 | 78.97 | 80.90 | | Udever-bloom-3b | 80.63 | 75.40 | 98.02 | 83.88 | 76.18 | 79.67 | 82.29 | | Udever-bloom-7b1 | 79.37 | 76.59 | 98.38 | 84.68 | 77.49 | 80.03 | 82.76 | ### Table 3: Chinese multi-domain retrieval [Multi-cpr](https://dl.acm.org/doi/10.1145/3477495.3531736) | | | |E-commerce | | Entertainment video | | Medical | | |--|--|--|--|--|--|--|--|--| | Model | Train | Backbone | MRR@10 | Recall@1k | MRR@10 | Recall@1k | MRR@10 | Recall@1k | || | BM25 | - | - | 0.225 | 0.815 | 0.225 | 0.780 | 0.187 | 0.482 | | Doc2Query | - | - | 0.239 | 0.826 | 0.238 | 0.794 | 0.210 | 0.505 | | DPR-1 | In-Domain | BERT | 0.270 | 0.921 | 0.254 | 0.934 | 0.327 | 0.747 | | DPR-2 | In-Domain | BERT-CT | 0.289 | **0.926** | 0.263 | **0.935** | 0.339 | **0.769** | | text-embedding-ada-002 | General | GPT | 0.183 | 0.825 | 0.159 | 0.786 | 0.245 | 0.593 | | sgpt-bloom-7b1-msmarco | General | BLOOM | 0.242 | 0.840 | 0.227 | 0.829 | 0.311 | 0.675 | || | Udever-bloom-560m | General | BLOOM | 0.156 | 0.802 | 0.149 | 0.749 | 0.245 | 0.571 | | Udever-bloom-1b1 | General | BLOOM | 0.244 | 0.863 | 0.208 | 0.815 | 0.241 | 0.557 | | Udever-bloom-3b | General | BLOOM | 0.267 | 0.871 | 0.228 | 0.836 | 0.288 | 0.619 | | Udever-bloom-7b1 | General | BLOOM | **0.296** | 0.889 | **0.267** | 0.907 | **0.343** | 0.705 | #### More results refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 3. ## Technical Specifications ### Model Architecture and Objective - Model: [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m). - Objective: Constrastive loss with hard negatives (refer to [paper](https://arxiv.org/pdf/2310.08232.pdf) section 2.2). ### Compute Infrastructure - Nvidia A100 SXM4 80GB. - torch 2.0.0, transformers 4.29.2. ## Citation **BibTeX:** ```BibTeX @article{zhang2023language, title={Language Models are Universal Embedders}, author={Zhang, Xin and Li, Zehan and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Zhang, Min}, journal={arXiv preprint arXiv:2310.08232}, year={2023} } ```
SanjiWatsuki/Silicon-Maid-7B
SanjiWatsuki
"2024-01-10T09:27:33Z"
5,081
92
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "not-for-all-audiences", "nsfw", "en", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-12-27T02:27:53Z"
--- license: cc-by-4.0 language: - en tags: - merge - not-for-all-audiences - nsfw --- <div style="display: flex; justify-content: center; align-items: center"> <img src="https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B/resolve/main/assets/cybermaid.png"> </div > <p align="center"> <big><b>Top 1 RP Performer on MT-bench 🤪</b ></big> </p> <p align="center"> <strong>Next Gen Silicon-Based RP Maid</strong> </p> ## WTF is This? Silicon-Maid-7B is another model targeted at being both strong at RP **and** being a smart cookie that can follow character cards very well. As of right now, Silicon-Maid-7B outscores both of my previous 7B RP models in my RP benchmark and I have been impressed by this model's creativity. It is suitable for RP/ERP and general use. Quants can be found [here](https://huggingface.co/collections/SanjiWatsuki/silicon-maid-7b-658d1669292816fe4992daa4). It's built on [xDAN-AI/xDAN-L1-Chat-RL-v1](https://huggingface.co/xDAN-AI/xDAN-L1-Chat-RL-v1), a 7B model which scores unusually high on MT-Bench, and chargoddard/loyal-piano-m7, an Alpaca format 7B model with surprisingly creative outputs. I was excited to see this model for two main reasons: * MT-Bench normally correlates well with real world model quality * It was an Alpaca prompt model with high benches which meant I could try swapping out my Marcoroni frankenmerge used in my previous model. **MT-Bench Average Turn** | model | score | size |--------------------|-----------|-------- | gpt-4 | 8.99 | - | *xDAN-L1-Chat-RL-v1* | 8.24^1 | 7b | Starling-7B | 8.09 | 7b | Claude-2 | 8.06 | - | **Silicon-Maid** | **7.96** | **7b** | *Loyal-Macaroni-Maid*| 7.95 | 7b | gpt-3.5-turbo | 7.94 | 20b? | Claude-1 | 7.90 | - | OpenChat-3.5 | 7.81 | - | vicuna-33b-v1.3 | 7.12 | 33b | wizardlm-30b | 7.01 | 30b | Llama-2-70b-chat | 6.86 | 70b ^1 xDAN's testing placed it 8.35 - this number is from my independent MT-Bench run. <img src="https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B/resolve/main/assets/fig-silicon-loyal.png"> It's unclear to me if xDAN-L1-Chat-RL-v1 is overtly benchmaxxing but it seemed like a solid 7B from my limited testing (although nothing that screams 2nd best model behind GPT-4). Amusingly, the model lost a lot of Reasoning and Coding skills in the merger. This was a much greater MT-Bench dropoff than I expected, perhaps suggesting the Math/Reasoning ability in the original model was rather dense and susceptible to being lost to a DARE TIE merger? Besides that, the merger is almost identical to the Loyal-Macaroni-Maid merger with a new base "smart cookie" model. If you liked any of my previous RP models, give this one a shot and let me know in the Community tab what you think! ### The Sauce ``` models: # Top-Loyal-Bruins-Maid-DARE-7B - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: xDAN-AI/xDAN-L1-Chat-RL-v1 parameters: weight: 0.4 density: 0.8 - model: chargoddard/loyal-piano-m7 parameters: weight: 0.3 density: 0.8 - model: Undi95/Toppy-M-7B parameters: weight: 0.2 density: 0.4 - model: NeverSleep/Noromaid-7b-v0.2 parameters: weight: 0.2 density: 0.4 - model: athirdpath/NSFW_DPO_vmgb-7b parameters: weight: 0.2 density: 0.4 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: bfloat16 ``` For more information about why I use this merger, see the [Loyal-Macaroni-Maid repo](https://huggingface.co/SanjiWatsuki/Loyal-Macaroni-Maid-7B#the-sauce-all-you-need-is-dare) ### Prompt Template (Alpaca) I found the best SillyTavern results from using the Noromaid template but please try other templates! Let me know if you find anything good. SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json). Additionally, here is my highly recommended [Text Completion preset](https://huggingface.co/SanjiWatsuki/Loyal-Macaroni-Maid-7B/blob/main/Characters/MinP.json). You can tweak this by adjusting temperature up or dropping min p to boost creativity or raise min p to increase stability. You shouldn't need to touch anything else! ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` ### Other Benchmarks | Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench | |---|---:|---:|---:|---:|---:| | [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) [📄](https://gist.github.com/mlabonne/36c412889c4acfad7061f269a31f9055) | 56.85 | 44.74 | 75.6 | 59.89 | 47.17 | | [**Silicon-Maid-7B**](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B) [📄](https://gist.github.com/DHNishi/315ba1abba27af930f5f546af3515735) | **56.45**| 44.74| 74.26| 61.5| 45.32| | [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 | | [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 | | [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) [📄](https://gist.github.com/mlabonne/e23d7d8418619cf5b1ca10da391ac629) | 51.34 | 42.67 | 72.92 | 47.27 | 42.51 | | [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) [📄](https://gist.github.com/mlabonne/c31cc46169ef3004c0df250017d5cac9) | 51.16 | 42.06 | 72.72 | 47.33 | 42.53 | | [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) [📄](https://gist.github.com/mlabonne/32a36f448fd36a3100c325d51d01c0a1) | 50.99 | 37.33 | 71.83 | 55.1 | 39.7 |
1bitLLM/bitnet_b1_58-3B
1bitLLM
"2024-03-29T11:57:44Z"
5,080
186
transformers
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2402.17764", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-03-29T11:09:15Z"
--- license: mit --- This is a reproduction of the <a href="https://arxiv.org/abs/2402.17764"> BitNet b1.58</a> paper. The models are trained with <a href="https://github.com/togethercomputer/RedPajama-Data">RedPajama dataset</a> for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following <a href="https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf">paper</a>. All models are open-source in the <a href="https://huggingface.co/1bitLLM">repo</a>. We will train larger models and/or more tokens when resource is available. ## Results PPL and zero-shot accuracy: | Models | PPL| ARCe| ARCc| HS | BQ | OQ | PQ | WGe | Avg |-------|-------|-------|-------|-------|-------|-------|-------|-------|-------| | FP16 700M (reported) | 12.33 | 54.7 | 23.0 | 37.0 | 60.0 | 20.2 | 68.9 | 54.8 | 45.5 | | BitNet b1.58 700M (reported) | 12.87 | 51.8 | 21.4 | 35.1 | 58.2 | 20.0 | 68.1 | 55.2 | 44.3 | | BitNet b1.58 700M (reproduced) | 12.78 | 51.4 | 21.8 | 35.0 | 59.6 | 20.6 | 67.5 | 55.4 | 44.5 | | FP16 1.3B (reported) | 11.25 | 56.9 | 23.5 | 38.5 | 59.1 | 21.6 | 70.0 | 53.9 | 46.2 | BitNet b1.58 1.3B (reported) | 11.29 | 54.9 | 24.2 | 37.7 | 56.7 | 19.6 | 68.8 | 55.8 | 45.4 | | BitNet b1.58 1.3B (reproduced) | 11.19 | 55.8 | 23.7 | 37.6 | 59.0 | 20.2 | 69.2 | 56.0 | 45.9 | FP16 3B (reported) | 10.04 | 62.1 | 25.6 | 43.3 | 61.8 | 24.6 | 72.1 | 58.2 | 49.7 | BitNet b1.58 3B (reported) | 9.91 | 61.4 | 28.3 | 42.9 | 61.5 | 26.6 | 71.5 | 59.3 | 50.2 | BitNet b1.58 3B (reproduced) | 9.88 | 60.9 | 28.0 | 42.3 | 58.3 | 26.0 | 71.4 | 60.3 | 49.6 | The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors. ## Evaluation The evaluation pipelines are from the paper authors. Here is the commands to run the evaluation: ``` pip install lm-eval==0.3.0 ``` ``` python eval_ppl.py --hf_path 1bitLLM/bitnet_b1_58-3B --seqlen 2048 ``` ``` python eval_task.py --hf_path 1bitLLM/bitnet_b1_58-3B \ --batch_size 1 \ --tasks \ --output_path result.json \ --num_fewshot 0 \ --ctx_size 2048 ```
digiplay/AI-infinity-V1-fp16
digiplay
"2023-08-04T18:12:02Z"
5,077
6
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2023-08-03T13:31:17Z"
--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/121253/ai-infinity-realistic-better-hands DEMO image generated by huggingface's API : ![f7174472-7ca7-4a01-a4f9-b5dec059c81c.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/axDIpUTys70mLbH5hdfho.jpeg) Original Author's DEMO image : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/aab573b9-12a2-4e6a-a7fa-7c0fe6ae9098/width=1024/04444-AI-infinity-V1-fp16-343063047.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/4334e6b3-87df-4fe3-9c9d-8c79f5d2d4af/width=1024/04507-AI-infinity-V1-fp16-2049249013.jpeg)
qnguyen3/Master-Yi-9B
qnguyen3
"2024-05-20T11:21:22Z"
5,075
17
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-05-18T00:14:19Z"
--- license: apache-2.0 --- ## Model Description Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs. **Master-Yi-9B** is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions. **Quantized Version**: [Here](https://huggingface.co/qnguyen3/Master-Yi-9B-GGUF) **Communitiy Quantization** (Thanks to [@LoneStriker](https://huggingface.co/LoneStriker)) - exl2: [Master-Yi-9B-8.0bpw-h8-exl2](https://huggingface.co/LoneStriker/Master-Yi-9B-8.0bpw-h8-exl2), [Master-Yi-9B-6.0bpw-h6-exl2](https://huggingface.co/LoneStriker/Master-Yi-9B-6.0bpw-h6-exl2), [Master-Yi-9B-5.0bpw-h6-exl2](https://huggingface.co/LoneStriker/Master-Yi-9B-5.0bpw-h6-exl2), [Master-Yi-9B-4.0bpw-h6-exl2](https://huggingface.co/LoneStriker/Master-Yi-9B-4.0bpw-h6-exl2) - GGUFs: [Master-Yi-9B-GGUF](https://huggingface.co/LoneStriker/Master-Yi-9B-GGUF) **Master-Yi-9B-Vision**: **Coming Soon** ![img](https://huggingface.co/qnguyen3/Master-Yi-9B/resolve/main/Master-Yi-9B.webp) ## Prompt Template ``` <|im_start|>system You are a helpful AI assistant.<|im_end|> <|im_start|>user What is the meaning of life?<|im_end|> <|im_start|>assistant ``` ## Examples ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/E27JmdRAMrHQacM50-lBk.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630430583926de1f7ec62c6b/z0HS4bxHFQzPe0gZlvCzZ.png) ## Inference Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "qnguyen3/Master-Yi-9B", torch_dtype='auto', device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("qnguyen3/Master-Yi-9B") prompt = "What is the mearning of life?" messages = [ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id, temperature=0.25, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ## Benchmarks ### Nous Benchmark: | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |---------------------------------------------------|------:|------:|---------:|-------:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)| 43.55| 71.48| 48.54| 41.43| 51.25| ### AGIEval ``` | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |35.83|± | 3.01| | | |acc_norm|31.89|± | 2.93| |agieval_logiqa_en | 0|acc |38.25|± | 1.91| | | |acc_norm|37.79|± | 1.90| |agieval_lsat_ar | 0|acc |23.04|± | 2.78| | | |acc_norm|20.43|± | 2.66| |agieval_lsat_lr | 0|acc |48.04|± | 2.21| | | |acc_norm|42.75|± | 2.19| |agieval_lsat_rc | 0|acc |61.34|± | 2.97| | | |acc_norm|52.79|± | 3.05| |agieval_sat_en | 0|acc |79.13|± | 2.84| | | |acc_norm|72.33|± | 3.12| |agieval_sat_en_without_passage| 0|acc |44.17|± | 3.47| | | |acc_norm|42.72|± | 3.45| |agieval_sat_math | 0|acc |52.27|± | 3.38| | | |acc_norm|47.73|± | 3.38| Average: 43.55% ``` ### GPT4All ``` | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |54.95|± | 1.45| | | |acc_norm|58.70|± | 1.44| |arc_easy | 0|acc |82.28|± | 0.78| | | |acc_norm|81.10|± | 0.80| |boolq | 1|acc |86.15|± | 0.60| |hellaswag | 0|acc |59.16|± | 0.49| | | |acc_norm|77.53|± | 0.42| |openbookqa | 0|acc |37.40|± | 2.17| | | |acc_norm|44.00|± | 2.22| |piqa | 0|acc |79.00|± | 0.95| | | |acc_norm|80.25|± | 0.93| |winogrande | 0|acc |72.61|± | 1.25| Average: 71.48% ``` ### TruthfulQA ``` | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |33.05|± | 1.65| | | |mc2 |48.54|± | 1.54| Average: 48.54% ``` ### Bigbench ``` | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|54.74|± | 3.62| |bigbench_date_understanding | 0|multiple_choice_grade|68.02|± | 2.43| |bigbench_disambiguation_qa | 0|multiple_choice_grade|40.31|± | 3.06| |bigbench_geometric_shapes | 0|multiple_choice_grade|30.36|± | 2.43| | | |exact_str_match | 2.23|± | 0.78| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|26.00|± | 1.96| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.71|± | 1.53| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|44.00|± | 2.87| |bigbench_movie_recommendation | 0|multiple_choice_grade|35.00|± | 2.14| |bigbench_navigate | 0|multiple_choice_grade|58.40|± | 1.56| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|61.80|± | 1.09| |bigbench_ruin_names | 0|multiple_choice_grade|42.41|± | 2.34| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|31.56|± | 1.47| |bigbench_snarks | 0|multiple_choice_grade|55.25|± | 3.71| |bigbench_sports_understanding | 0|multiple_choice_grade|69.37|± | 1.47| |bigbench_temporal_sequences | 0|multiple_choice_grade|27.70|± | 1.42| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.36|± | 1.16| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|14.69|± | 0.85| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|44.00|± | 2.87| Average: 41.43% ``` **Average score**: 51.25% ### OpenLLM Benchmark: | Model |ARC |HellaSwag|MMLU |TruthfulQA|Winogrande|GSM8K|Average| |---------------------------------------------------|---:|--------:|----:|---------:|---------:|----:|------:| |[Master-Yi-9B](https://huggingface.co/qnguyen3/Master-Yi-9B)|61.6| 79.89|69.95| 48.59| 77.35|67.48| 67.48| ### ARC ``` | Task |Version| Metric | Value | |Stderr| |-------------|------:|--------------------|-------------|---|------| |arc_challenge| 1|acc,none | 0.59| | | | | |acc_stderr,none | 0.01| | | | | |acc_norm,none | 0.62| | | | | |acc_norm_stderr,none| 0.01| | | | | |alias |arc_challenge| | | Average: 61.6% ``` ### HellaSwag ``` | Task |Version| Metric | Value | |Stderr| |---------|------:|--------------------|---------|---|------| |hellaswag| 1|acc,none | 0.61| | | | | |acc_stderr,none | 0| | | | | |acc_norm,none | 0.80| | | | | |acc_norm_stderr,none| 0| | | | | |alias |hellaswag| | | Average: 79.89% ``` ### MMLU ``` | Task |Version| Metric | Value | |Stderr| |----------------------------------------|-------|---------------|---------------------------------------|---|------| |mmlu |N/A |acc,none | 0.7| | | | | |acc_stderr,none| 0| | | | | |alias |mmlu | | | |mmlu_abstract_algebra | 0|alias | - abstract_algebra | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_anatomy | 0|alias | - anatomy | | | | | |acc,none |0.64 | | | | | |acc_stderr,none|0.04 | | | |mmlu_astronomy | 0|alias | - astronomy | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.03 | | | |mmlu_business_ethics | 0|alias | - business_ethics | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_clinical_knowledge | 0|alias | - clinical_knowledge | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_biology | 0|alias | - college_biology | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_chemistry | 0|alias | - college_chemistry | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_computer_science | 0|alias | - college_computer_science | | | | | |acc,none |0.56 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_mathematics | 0|alias | - college_mathematics | | | | | |acc,none |0.44 | | | | | |acc_stderr,none|0.05 | | | |mmlu_college_medicine | 0|alias | - college_medicine | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.03 | | | |mmlu_college_physics | 0|alias | - college_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.05 | | | |mmlu_computer_security | 0|alias | - computer_security | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.04 | | | |mmlu_conceptual_physics | 0|alias | - conceptual_physics | | | | | |acc,none |0.74 | | | | | |acc_stderr,none|0.03 | | | |mmlu_econometrics | 0|alias | - econometrics | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.04 | | | |mmlu_electrical_engineering | 0|alias | - electrical_engineering | | | | | |acc,none |0.72 | | | | | |acc_stderr,none|0.04 | | | |mmlu_elementary_mathematics | 0|alias | - elementary_mathematics | | | | | |acc,none |0.62 | | | | | |acc_stderr,none|0.02 | | | |mmlu_formal_logic | 0|alias | - formal_logic | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.04 | | | |mmlu_global_facts | 0|alias | - global_facts | | | | | |acc,none |0.46 | | | | | |acc_stderr,none|0.05 | | | |mmlu_high_school_biology | 0|alias | - high_school_biology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_chemistry | 0|alias | - high_school_chemistry | | | | | |acc,none |0.67 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_computer_science | 0|alias | - high_school_computer_science | | | | | |acc,none |0.84 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_european_history | 0|alias | - high_school_european_history | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_geography | 0|alias | - high_school_geography | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_government_and_politics| 0|alias | - high_school_government_and_politics| | | | | |acc,none |0.90 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_macroeconomics | 0|alias | - high_school_macroeconomics | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_mathematics | 0|alias | - high_school_mathematics | | | | | |acc,none |0.43 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_microeconomics | 0|alias | - high_school_microeconomics | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_physics | 0|alias | - high_school_physics | | | | | |acc,none |0.45 | | | | | |acc_stderr,none|0.04 | | | |mmlu_high_school_psychology | 0|alias | - high_school_psychology | | | | | |acc,none |0.87 | | | | | |acc_stderr,none|0.01 | | | |mmlu_high_school_statistics | 0|alias | - high_school_statistics | | | | | |acc,none |0.68 | | | | | |acc_stderr,none|0.03 | | | |mmlu_high_school_us_history | 0|alias | - high_school_us_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_high_school_world_history | 0|alias | - high_school_world_history | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.02 | | | |mmlu_human_aging | 0|alias | - human_aging | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.03 | | | |mmlu_human_sexuality | 0|alias | - human_sexuality | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_humanities |N/A |alias | - humanities | | | | | |acc,none |0.63 | | | | | |acc_stderr,none|0.01 | | | |mmlu_international_law | 0|alias | - international_law | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_jurisprudence | 0|alias | - jurisprudence | | | | | |acc,none |0.79 | | | | | |acc_stderr,none|0.04 | | | |mmlu_logical_fallacies | 0|alias | - logical_fallacies | | | | | |acc,none |0.80 | | | | | |acc_stderr,none|0.03 | | | |mmlu_machine_learning | 0|alias | - machine_learning | | | | | |acc,none |0.52 | | | | | |acc_stderr,none|0.05 | | | |mmlu_management | 0|alias | - management | | | | | |acc,none |0.83 | | | | | |acc_stderr,none|0.04 | | | |mmlu_marketing | 0|alias | - marketing | | | | | |acc,none |0.89 | | | | | |acc_stderr,none|0.02 | | | |mmlu_medical_genetics | 0|alias | - medical_genetics | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.04 | | | |mmlu_miscellaneous | 0|alias | - miscellaneous | | | | | |acc,none |0.85 | | | | | |acc_stderr,none|0.01 | | | |mmlu_moral_disputes | 0|alias | - moral_disputes | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.02 | | | |mmlu_moral_scenarios | 0|alias | - moral_scenarios | | | | | |acc,none |0.48 | | | | | |acc_stderr,none|0.02 | | | |mmlu_nutrition | 0|alias | - nutrition | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_other |N/A |alias | - other | | | | | |acc,none |0.75 | | | | | |acc_stderr,none|0.01 | | | |mmlu_philosophy | 0|alias | - philosophy | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.02 | | | |mmlu_prehistory | 0|alias | - prehistory | | | | | |acc,none |0.77 | | | | | |acc_stderr,none|0.02 | | | |mmlu_professional_accounting | 0|alias | - professional_accounting | | | | | |acc,none |0.57 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_law | 0|alias | - professional_law | | | | | |acc,none |0.50 | | | | | |acc_stderr,none|0.01 | | | |mmlu_professional_medicine | 0|alias | - professional_medicine | | | | | |acc,none |0.71 | | | | | |acc_stderr,none|0.03 | | | |mmlu_professional_psychology | 0|alias | - professional_psychology | | | | | |acc,none |0.73 | | | | | |acc_stderr,none|0.02 | | | |mmlu_public_relations | 0|alias | - public_relations | | | | | |acc,none |0.76 | | | | | |acc_stderr,none|0.04 | | | |mmlu_security_studies | 0|alias | - security_studies | | | | | |acc,none |0.78 | | | | | |acc_stderr,none|0.03 | | | |mmlu_social_sciences |N/A |alias | - social_sciences | | | | | |acc,none |0.81 | | | | | |acc_stderr,none|0.01 | | | |mmlu_sociology | 0|alias | - sociology | | | | | |acc,none |0.86 | | | | | |acc_stderr,none|0.02 | | | |mmlu_stem |N/A |alias | - stem | | | | | |acc,none |0.65 | | | | | |acc_stderr,none|0.01 | | | |mmlu_us_foreign_policy | 0|alias | - us_foreign_policy | | | | | |acc,none |0.92 | | | | | |acc_stderr,none|0.03 | | | |mmlu_virology | 0|alias | - virology | | | | | |acc,none |0.58 | | | | | |acc_stderr,none|0.04 | | | |mmlu_world_religions | 0|alias | - world_religions | | | | | |acc,none |0.82 | | | | | |acc_stderr,none|0.03 | | | Average: 69.95% ``` ### TruthfulQA ``` | Task |Version| Metric | Value | |Stderr| |--------------|-------|-----------------------|-----------------|---|------| |truthfulqa |N/A |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |acc,none | 0.41| | | | | |acc_stderr,none | 0.01| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |alias |truthfulqa | | | |truthfulqa_gen| 3|bleu_max,none | 23.77| | | | | |bleu_max_stderr,none | 0.81| | | | | |bleu_acc,none | 0.45| | | | | |bleu_acc_stderr,none | 0.02| | | | | |bleu_diff,none | 1.38| | | | | |bleu_diff_stderr,none | 0.75| | | | | |rouge1_max,none | 48.29| | | | | |rouge1_max_stderr,none | 0.90| | | | | |rouge1_acc,none | 0.45| | | | | |rouge1_acc_stderr,none | 0.02| | | | | |rouge1_diff,none | 1.72| | | | | |rouge1_diff_stderr,none| 0.92| | | | | |rouge2_max,none | 33.01| | | | | |rouge2_max_stderr,none | 1.05| | | | | |rouge2_acc,none | 0.38| | | | | |rouge2_acc_stderr,none | 0.02| | | | | |rouge2_diff,none | 0.92| | | | | |rouge2_diff_stderr,none| 1.07| | | | | |rougeL_max,none | 45.25| | | | | |rougeL_max_stderr,none | 0.92| | | | | |rougeL_acc,none | 0.46| | | | | |rougeL_acc_stderr,none | 0.02| | | | | |rougeL_diff,none | 1.57| | | | | |rougeL_diff_stderr,none| 0.93| | | | | |alias | - truthfulqa_gen| | | |truthfulqa_mc1| 2|acc,none | 0.33| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc1| | | |truthfulqa_mc2| 2|acc,none | 0.49| | | | | |acc_stderr,none | 0.02| | | | | |alias | - truthfulqa_mc2| | | Average: 48.59% ``` ### Winogrande ``` | Task |Version| Metric | Value | |Stderr| |----------|------:|---------------|----------|---|------| |winogrande| 1|acc,none | 0.77| | | | | |acc_stderr,none| 0.01| | | | | |alias |winogrande| | | Average: 77.35% ``` ### GSM8K ``` |Task |Version| Metric |Value| |Stderr| |-----|------:|-----------------------------------|-----|---|------| |gsm8k| 3|exact_match,strict-match | 0.67| | | | | |exact_match_stderr,strict-match | 0.01| | | | | |exact_match,flexible-extract | 0.68| | | | | |exact_match_stderr,flexible-extract| 0.01| | | | | |alias |gsm8k| | | Average: 67.48% ``` **Average score**: 67.48%
mosaicml/mpt-30b
mosaicml
"2024-03-05T20:25:40Z"
5,073
340
transformers
[ "transformers", "pytorch", "mpt", "text-generation", "Composer", "MosaicML", "llm-foundry", "StreamingDatasets", "custom_code", "dataset:allenai/c4", "dataset:mc4", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:bigcode/the-stack-dedup", "dataset:allenai/s2orc", "arxiv:2108.12409", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2010.04245", "arxiv:1909.08053", "arxiv:2302.06675", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-06-20T16:29:39Z"
--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - allenai/c4 - mc4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack-dedup - allenai/s2orc inference: false --- # MPT-30B MPT-30B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-30B is part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. MPT-30B comes with special features that differentiate it from other LLMs, including an 8k token context window (which can be further extended via finetuning; see [MPT-7B-StoryWriter](https://huggingface.co/mosaicml/mpt-7b-storywriter)), support for context-length extrapolation via [ALiBi](https://arxiv.org/abs/2108.12409), and efficient inference + training via FlashAttention. It also has strong coding abilities thanks to its pretraining mix. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). The size of MPT-30B was also specifically chosen to make it easy to deploy on a single GPU—either 1xA100-80GB in 16-bit precision or 1xA100-40GB in 8-bit precision. This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-30B is: * **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409). * **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-30B: The following models are finetuned on MPT-30B: * [MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-instruct): a model for long-form instruction following (especially summarization and question-answering). Built by finetuning MPT-30B on several carefully curated datasets. * License: _CC-BY-SA-3.0_ * [MPT-30B-Chat](https://huggingface.co/mosaicml/mpt-30b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-30B on [ShareGPT-Vicuna](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered), [Camel-AI](https://huggingface.co/camel-ai), [GPTeacher](https://github.com/teknium1/GPTeacher), [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Baize](https://github.com/project-baize/baize-chatbot) and some generated datasets. * License: _CC-By-NC-SA-4.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-30b-chat) ## Model Date June 22, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: MPT-30B: Raising the bar for open-source foundation models](https://www.mosaicml.com/blog/mpt-30b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-30b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-30b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` The model was trained initially with a sequence length of 2048 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-30b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the MPT-30B tokenizer which is identical to the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline with torch.autocast('cuda', dtype=torch.bfloat16): inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda') outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # or using the HF pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 29.95B | |n_layers | 48 | | n_heads | 64 | | d_model | 7168 | | vocab size | 50432 | | sequence length | 8192 | ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens on the following data mix: | Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs | |-------------|----------------------------|------------|----------------------------|--------| | mC4 3.1.0 - English (200+ words) | 2417.99 B | 33.50% | 335 B | 0.14 | | c4 - English - SemDedup 80% | 100.42 B | 29.90% | 299 B | 2.98 | | RedPajama - CommonCrawl | 878.45 B | 8.50% | 85 B | 0.097 | | The Stack - Selected Languages | 463.78 B | 10.00% | 100 B | 0.22 | | RedPajama - Wikipedia | 4.87 B | 4.00% | 40 B | 8.21 | | The Stack - Markdown | 107.07 B | 4.50% | 45 B | 0.42 | | Semantic Scholar ORC | 48.95 B | 3.30% | 33 B | 0.67 | | RedPajama - Books | 26.02 B | 3.00% | 30 B | 1.15 | | RedPajama - arXiv | 28.10 B | 1.90% | 19 B | 0.68 | | RedPajama - StackExchange | 20.54 B | 1.40% | 14 B |0.68 | Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the sequence length. To build 8k support into MPT-30B efficiently, we first pre-trained on 1T tokens using sequences that were 2k tokens long, and then trained for an additional 50B tokens using sequences that were 8k tokens long. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)). ### Training Configuration The model was trained in three stages using the [MosaicML Platform](https://www.mosaicml.com/platform): (i) First it was trained on 440 A100-40GBs with a batch size of 1760. (ii) Then, on 216 A100-40GBs with a batch size of 1728. (iii) Training was completed on 256 H100-80GBs with a batch size of 512 with 8k context length and 50B tokens. The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-30B (Base) is **not** intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-30B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-30B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-30b). ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-30B: Raising the bar for open-source foundation models}, year = {2023}, url = {www.mosaicml.com/blog/mpt-30b}, note = {Accessed: 2023-06-22}, urldate = {2023-06-22} } ```
Salesforce/blip-itm-base-coco
Salesforce
"2023-08-01T14:49:10Z"
5,071
12
transformers
[ "transformers", "pytorch", "tf", "blip", "image-text-matching", "arxiv:2201.12086", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]
null
"2022-12-12T17:53:18Z"
--- pipeline_tags: 'other' tags: - image-text-matching languages: - en license: bsd-3-clause --- # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for BLIP trained on image-text matching - base architecture (with ViT base backbone) trained on COCO dataset. | ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) | |:--:| | <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>| ## TL;DR Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.* ## Usage You can use this model for conditional and un-conditional image captioning ### Using the Pytorch model #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForImageTextRetrieval processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "A woman and a dog sitting together in a beach." inputs = processor(raw_image, question, return_tensors="pt") itm_scores = model(**inputs)[0] cosine_score = model(**inputs, use_itm_head=False)[0] ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForImageTextRetrieval processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco").to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "A woman and a dog sitting together in a beach." inputs = processor(raw_image, question, return_tensors="pt").to("cuda") itm_scores = model(**inputs)[0] cosine_score = model(**inputs, use_itm_head=False)[0] ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForImageTextRetrieval processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco", torch_dtype=torch.float16).to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "A woman and a dog sitting together in a beach." inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16) itm_scores = model(**inputs)[0] cosine_score = model(**inputs, use_itm_head=False)[0] ``` </details> ## BibTex and citation info ``` @misc{https://doi.org/10.48550/arxiv.2201.12086, doi = {10.48550/ARXIV.2201.12086}, url = {https://arxiv.org/abs/2201.12086}, author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
hantian/layoutreader
hantian
"2024-04-11T15:23:23Z"
5,071
10
transformers
[ "transformers", "pytorch", "safetensors", "layoutlmv3", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
"2024-02-28T09:10:22Z"
--- library_name: transformers --- # LayoutReader A reading order prediction model. Turn bboxes extracted from PDF or detected by OCR into readable order. Please refer to [Github](https://github.com/ppaanngggg/layoutreader) for more details.
facebook/data2vec-audio-base-960h
facebook
"2022-05-24T10:41:22Z"
5,070
10
transformers
[ "transformers", "pytorch", "data2vec-audio", "automatic-speech-recognition", "speech", "hf-asr-leaderboard", "en", "dataset:librispeech_asr", "arxiv:2202.03555", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
"2022-03-02T23:29:05Z"
--- language: en datasets: - librispeech_asr tags: - speech - hf-asr-leaderboard license: apache-2.0 widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: data2vec-audio-base-960h results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 2.77 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 7.08 --- # Data2Vec-Audio-Base-960h [Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/) The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2202.03555) Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli **Abstract** While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches. The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec . # Pre-Training method ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/data2vec.png) For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555). # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Data2VecForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h") model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-960h") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate **facebook/data2vec-audio-base-960h** on LibriSpeech's "clean" and "other" test data. ```python from transformers import Wav2Vec2Processor, Data2VecForCTC from datasets import load_dataset import torch from jiwer import wer # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h").to("cuda") model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-960h") librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 2.77 | 7.08 |
monologg/koelectra-base-v3-finetuned-korquad
monologg
"2023-06-12T12:29:43Z"
5,066
4
transformers
[ "transformers", "pytorch", "safetensors", "electra", "question-answering", "endpoints_compatible", "region:us" ]
question-answering
"2022-03-02T23:29:05Z"
Entry not found
Helsinki-NLP/opus-mt-en-et
Helsinki-NLP
"2023-08-16T11:29:29Z"
5,064
0
transformers
[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "en", "et", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
"2022-03-02T23:29:04Z"
--- tags: - translation license: apache-2.0 --- ### opus-mt-en-et * source languages: en * target languages: et * OPUS readme: [en-et](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-et/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-et/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-et/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-et/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2018-enet.en.et | 21.8 | 0.540 | | newstest2018-enet.en.et | 23.3 | 0.556 | | Tatoeba.en.et | 54.0 | 0.717 |
latentcat/control_v1p_sd15_brightness
latentcat
"2023-05-25T10:35:20Z"
5,057
180
diffusers
[ "diffusers", "safetensors", "image-to-image", "controlnet", "en", "dataset:ioclab/grayscale_image_aesthetic_3M", "license:creativeml-openrail-m", "region:us" ]
image-to-image
"2023-04-19T06:14:12Z"
--- license: creativeml-openrail-m datasets: - ioclab/grayscale_image_aesthetic_3M language: - en library_name: diffusers tags: - image-to-image - controlnet --- # Model Card for ioclab/ioc-controlnet This model brings brightness control to Stable Diffusion, allowing users to colorize grayscale images or recolor generated images. ## Model Details - **Developed by:** [@ciaochaos](https://github.com/ciaochaos) - **Shared by [optional]:** [More Information Needed] - **Model type:** Stable Diffusion ControlNet model for [web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. ## Uses ### HuggingFace Space Demo [huggingface.co/spaces/ioclab/brightness-controlnet](https://huggingface.co/spaces/ioclab/brightness-controlnet) ### Direct Use [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ## More Info [Brightness ControlNet 训练流程](https://aigc.ioclab.com/sd-showcase/brightness-controlnet.html) (Chinese)
stablediffusionapi/sdxxxl
stablediffusionapi
"2023-12-12T06:35:53Z"
5,057
3
diffusers
[ "diffusers", "stablediffusionapi.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
"2023-12-12T06:33:17Z"
--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # sdxxxl API Inference ![generated from stablediffusionapi.com](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/380bc155-f23d-4587-9127-3eb7bdda0f28/width=768/00946-2910078626.jpeg) ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "sdxxxl" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/sdxxxl) Model link: [View model](https://stablediffusionapi.com/models/sdxxxl) Credits: [View credits](https://civitai.com/?query=sdxxxl) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "sdxxxl", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**
google/siglip-base-patch16-256-multilingual
google
"2024-03-28T17:30:52Z"
5,055
24
transformers
[ "transformers", "safetensors", "siglip", "zero-shot-image-classification", "vision", "arxiv:2303.15343", "arxiv:2209.06794", "license:apache-2.0", "endpoints_compatible", "region:us" ]
zero-shot-image-classification
"2024-01-08T13:24:51Z"
--- license: apache-2.0 tags: - vision widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog --- # SigLIP (base-sized model, multilingual) SigLIP model pre-trained on WebLi at resolution 256x256. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision). Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes. A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713). ## Intended uses & limitations You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for other versions on a task that interests you. ### How to use Here is how to use this model to perform zero-shot image classification: ```python from PIL import Image import requests from transformers import AutoProcessor, AutoModel import torch model = AutoModel.from_pretrained("google/siglip-base-patch16-256-multilingual") processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-256-multilingual") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) texts = ["a photo of 2 cats", "a photo of 2 dogs"] inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) logits_per_image = outputs.logits_per_image probs = torch.sigmoid(logits_per_image) # these are the probabilities print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") ``` Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user: ```python from transformers import pipeline from PIL import Image import requests # load pipe image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-256-multilingual") # load image url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) # inference outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"]) outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs] print(outputs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#). ## Training procedure ### Training data SigLIP is pre-trained on the WebLI dataset without language filter [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794). ### Preprocessing Images are resized/rescaled to the same resolution (256x256) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). Texts are tokenized and padded to the same length (64 tokens). ### Compute The model was trained on 16 TPU-v4 chips for three days. ## Evaluation results Evaluation of SigLIP compared to CLIP is shown below (taken from the paper). <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg" alt="drawing" width="600"/> ### BibTeX entry and citation info ```bibtex @misc{zhai2023sigmoid, title={Sigmoid Loss for Language Image Pre-Training}, author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer}, year={2023}, eprint={2303.15343}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF
mradermacher
"2024-06-04T05:49:26Z"
5,053
0
transformers
[ "transformers", "gguf", "en", "base_model:Hastagaras/Halu-8B-Llama3-v0.35", "license:llama3", "endpoints_compatible", "region:us" ]
null
"2024-06-03T16:14:54Z"
--- base_model: Hastagaras/Halu-8B-Llama3-v0.35 language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Hastagaras/Halu-8B-Llama3-v0.35 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Halu-8B-Llama3-v0.35-i1-GGUF/resolve/main/Halu-8B-Llama3-v0.35.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
huggyllama/llama-65b
huggyllama
"2023-04-07T15:51:00Z"
5,052
72
transformers
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-04-04T01:43:00Z"
--- license: other --- This contains the weights for the LLaMA-65b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.