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text2text-generation
|
transformers
|
# PLLaVA Model Card
## Model details
**Model type:**
PLLaVA-34B is an open-source video-language chatbot trained by fine-tuning Image-LLM on video instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: liuhaotian/llava-v1.6-34b
**Model date:**
PLLaVA-34B was trained in April 2024.
**Paper or resources for more information:**
- github repo: https://github.com/magic-research/PLLaVA
- project page: https://pllava.github.io/
- paper link: https://arxiv.org/abs/2404.16994
## License
NousResearch/Nous-Hermes-2-Yi-34B license.
**Where to send questions or comments about the model:**
https://github.com/magic-research/PLLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of PLLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
Video-Instruct-Tuning data of OpenGVLab/VideoChat2-IT
## Evaluation dataset
A collection of 6 benchmarks, including 5 Video QA benchmarks and 1 benchmarks specifically proposed for Video-LMMs.
|
{"license": "apache-2.0", "tags": ["video LLM"], "datasets": ["OpenGVLab/VideoChat2-IT"]}
|
ermu2001/pllava-34b
| null |
[
"transformers",
"safetensors",
"llava",
"text2text-generation",
"video LLM",
"dataset:OpenGVLab/VideoChat2-IT",
"arxiv:2404.16994",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null |
2024-04-25T04:16:03+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Nitral-AI/Poppy_Porpoise-v0.8-L3-8B
<!-- 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/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF/resolve/main/Poppy_Porpoise-v0.8-L3-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
{"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "Nitral-AI/Poppy_Porpoise-v0.8-L3-8B", "quantized_by": "mradermacher"}
|
mradermacher/Poppy_Porpoise-v0.8-L3-8B-GGUF
| null |
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nitral-AI/Poppy_Porpoise-v0.8-L3-8B",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:18:25+00:00
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
ripaaiii/fine-tune-C1-stage1_5epoch_besar
| null |
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:18:27+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# results
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/flan-t5-base", "model-index": [{"name": "results", "results": []}]}
|
UmarSk27/results
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:18:46+00:00
|
null | null |
# SkinXmed Erfahrungen Wo Kaufen - SkinXmed Creme Bewertungen Deutschland Preis
Skinxmed Creme Erfahrungen ist eine Feuchtigkeitscreme, die von der Marke Skinxmed angeboten wird. Sie ist speziell für die Bekämpfung von Hautalterung, Falten und anderen Hautproblemen entwickelt worden. Die Creme enthält Inhaltsstoffe wie Hyaluronsäure, Kollagen und Vitamin C, die dazu beitragen, die Haut zu hydratisieren, zu straffen und das Auftreten von Falten zu reduzieren.
## **[Klicken Sie hier, um jetzt auf der offiziellen Website von SkinXmed zu kaufen](https://deutschlandbuzz.de/skinxmed-de)**
## Hyaluronsäure Molekulargröße
Leider findet man auf den meisten Produkten keine Angabe über die Molekulargröße von Hyaluron.
Da jedoch die Herstellung von niedermolekularer Hyaluronsäure sehr teuer ist, kann man davon ausgehen, dass billige Hyaluroncremes nur hochmolekulare Hyaluronsäure enthalten.
Niedermolekulare Hyaluronsäure findet man überwiegend in Seren. Sie gelangt in die tiefen Hautschichten und kann die Feuchtigkeit nachhaltig speichern.
Die perfekte Hyaluroncreme beinhaltet sowohl hoch- als auch niedermolekulare Hyaluronsäure.
## Ubiquinone :
Ubiquinone ist besser bekannt als das Coenzym Q10.
Q10 ist eine Geheimwaffe gegen Falten, da es, wie Vitamin C, als Antioxidans wirkt und freie Radikale bekämpfen kann.
Q10 dient als Zellschutz und schützt die kollagenen Fasern vor dem Zerfall durch UV-Strahlung und oxidativem Stress.
## Retinol (Vitamin A) :
Retinol wird in der Haut zu Vitamin-A-Säure umgewandelt.
Retinol wird von Dermatologen als effizientester und wissenschaftlich erwiesener Wirkstoff gegen Falten bezeichnet, da es die Kollagenproduktion anregt und sogar sonnengeschädigte Haut reparieren kann.
## DMAE (Dimethylaminoethanol) :
DMAE ist ein natürlicher Nährstoff, der aus Fisch (u.a. Lachs, Sardinen) gewonnen wird und noch als Geheimtipp im Kampf gegen Falten gilt.
Dimethylaminoethanol verbessert die Festigkeit und Elastizität der Haut und sorgt durch einen Schutz der Zellmembran für eine längere Lebensdauer der Zellen.
DMAE ist auch dafür verantwortlich, dass mehr Acetylcholin ausgeschüttet wird, wodurch die Mikro-Muskelfasern (MYOFILAMENTE) mehr Spannung erhalten. Somit kann DMAE auch schlaffen Hautpartien entgegenwirken.
## Alteromonas Ferment Extract :
Peptid aus den Aminosäuren Lysin, Histidin und Glysin. Fördert die Wasserspeicherkapazität und Wundheilung. Regt die Kollagen- und Elastinbildung und erhöht das Feuchthaltevermögen der Haut.
## Pullulan :
Bei Pullulan handet es sich um ein Polysaccharid, welches durch einen natürlichen Fermentationsprozess aus Pflanzenextrakten gewonnen wird.
Pullulan hat einen Sofort-Lifting-Effekt, bildet einen feinen Film auf der Haut und lässt Falten somit innerhalb von Sekunden verschwinden.
Fermentative Wirkstoffe in der Kosmetik werden immer beliebter. Durch den Fermentationsprozess werden ohne Chemikalien hochwirksame Nährstoffe gebildet. Außerdem kann bei fermentativen Kosmetikprodukten auf Konservierungsstoffe verzichtet werden.
## **[Klicken Sie hier, um jetzt auf der offiziellen Website von SkinXmed zu kaufen](https://deutschlandbuzz.de/skinxmed-de)**
|
{}
|
VKapseln475/SkinXmed1555
| null |
[
"region:us"
] | null |
2024-04-25T04:19:37+00:00
|
null | null |
{"license": "openrail"}
|
YangPa/LeeChungAh
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-25T04:20:52+00:00
|
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_SOAPL_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SOAPL_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:22:59+00:00
|
text-generation
|
transformers
|
# Gemma 2B Translation v0.123
- Eval Loss: `0.94028`
- Train Loss: `0.85489`
- lr: `6e-05`
- optimizer: adamw
- lr_scheduler_type: cosine
## Prompt Template
```
<bos>##English##
Hamsters don't eat cats.
##Korean##
햄스터는 고양이를 먹지 않습니다.<eos>
```
```
<bos>##Korean##
햄스터는 고양이를 먹지 않습니다.
##English##
Hamsters do not eat cats.<eos>
```
## Model Description
- **Developed by:** `lemon-mint`
- **Model type:** Gemma
- **Language(s) (NLP):** English
- **License:** [gemma-terms-of-use](https://ai.google.dev/gemma/terms)
- **Finetuned from model:** [beomi/gemma-ko-2b](https://huggingface.co/beomi/gemma-ko-2b)
|
{"language": ["ko"], "license": "gemma", "library_name": "transformers", "tags": ["gemma", "pytorch", "instruct", "finetune", "translation"], "datasets": ["traintogpb/aihub-flores-koen-integrated-sparta-30k"], "widget": [{"messages": [{"role": "user", "content": "Hamsters don't eat cats."}]}], "base_model": "beomi/gemma-ko-2b", "pipeline_tag": "text-generation"}
|
lemon-mint/gemma-2b-translation-v0.123
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"pytorch",
"instruct",
"finetune",
"translation",
"conversational",
"ko",
"dataset:traintogpb/aihub-flores-koen-integrated-sparta-30k",
"base_model:beomi/gemma-ko-2b",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:23:03+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_OSAPL_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_OSAPL_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_OSAPL_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:23:49+00:00
|
text-to-image
|
diffusers
|
# AutoTrain SDXL LoRA DreamBooth - DileepPatruni/CarImagesLORAmodel
<Gallery />
## Model description
These are DileepPatruni/CarImagesLORAmodel LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use <enter your prompt here> to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](DileepPatruni/CarImagesLORAmodel/tree/main) them in the Files & versions tab.
|
{"license": "openrail++", "tags": ["autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "<enter your prompt here>"}
|
DileepPatruni/CarImagesLORAmodel
| null |
[
"diffusers",
"autotrain",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null |
2024-04-25T04:25:31+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_PSOAL_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_PSOAL_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_PSOAL_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:25:52+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# robust_llm_pythia-160m_mz-130_IMDB_n-its-10-seed-4
This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset.
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-160m", "model-index": [{"name": "robust_llm_pythia-160m_mz-130_IMDB_n-its-10-seed-4", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-160m_mz-130_IMDB_n-its-10-seed-4
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-160m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:26:31+00:00
|
null | null |
{}
|
andryxinson/25-1
| null |
[
"region:us"
] | null |
2024-04-25T04:27:21+00:00
|
|
null | null |
{"license": "mit", "tags": ["code"]}
|
sanyuan0704/hhh-8b-uugf
| null |
[
"gguf",
"code",
"license:mit",
"region:us"
] | null |
2024-04-25T04:27:28+00:00
|
|
null |
transformers
|
# Uploaded model
- **Developed by:** hanifsyarubany10
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-7b-bnb-4bit"}
|
hanifsyarubany10/gemma-7b-100epochs-Unsloth-FreedomIntelligence-indo-2e-4
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:27:28+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/NotAiLOL/Knight-Miqu-70B-MoE
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-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/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ1_S.gguf) | i1-IQ1_S | 14.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ1_M.gguf) | i1-IQ1_M | 15.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.1 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.2 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ2_S.gguf) | i1-IQ2_S | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ2_M.gguf) | i1-IQ2_M | 23.0 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q2_K.gguf) | i1-Q2_K | 25.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.0 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q3_K_S.gguf) | i1-Q3_K_S | 29.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ3_S.gguf) | i1-IQ3_S | 29.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ3_M.gguf) | i1-IQ3_M | 30.6 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q3_K_M.gguf) | i1-Q3_K_M | 32.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q3_K_L.gguf) | i1-Q3_K_L | 35.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.5 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q4_0.gguf) | i1-Q4_0 | 38.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q4_K_S.gguf) | i1-Q4_K_S | 38.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q4_K_M.gguf) | i1-Q4_K_M | 40.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.0 | |
| [GGUF](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.2 | |
| [PART 1](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Knight-Miqu-70B-MoE-i1-GGUF/resolve/main/Knight-Miqu-70B-MoE.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "NotAiLOL/Knight-Miqu-70B-MoE", "quantized_by": "mradermacher"}
|
mradermacher/Knight-Miqu-70B-MoE-i1-GGUF
| null |
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:NotAiLOL/Knight-Miqu-70B-MoE",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:27:33+00:00
|
text-to-image
|
diffusers
|
# NVIDIA RTX 4070Ti
<Gallery />
## Model description
寻医问药--基于大语言模型的智能诊断系统
## Download model
Weights for this model are available in Safetensors format.
[Download](/XinNuyoah/_/tree/main) them in the Files & versions tab.
|
{"license": "llama2", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "\u5bfb\u533b\u95ee\u836f", "parameters": {"negative_prompt": "\u57fa\u4e8e\u5927\u8bed\u8a00\u6a21\u578b\u7684\u667a\u80fd\u8bca\u65ad\u7cfb\u7edf"}, "output": {"url": "images/\u622a\u56fe 2024-04-25 12-27-04.png"}}], "base_model": "stabilityai/cosxl"}
|
XinNuyoah/_
| null |
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/cosxl",
"license:llama2",
"region:us"
] | null |
2024-04-25T04:28:01+00:00
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_Aleatoric_tiny_0.8_Seed105
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null |
2024-04-25T04:28:20+00:00
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_Aleatoric_tiny_0.8_Seed105
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null |
2024-04-25T04:28:24+00:00
|
null | null |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{}
|
Breezy9900/RVC_HFv2
| null |
[
"arxiv:1910.09700",
"doi:10.57967/hf/2116",
"region:us"
] | null |
2024-04-25T04:28:55+00:00
|
null | null |
{}
|
Tristan/pythia-410m-deduped-en
| null |
[
"tensorboard",
"safetensors",
"region:us"
] | null |
2024-04-25T04:29:29+00:00
|
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2017
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:30:04+00:00
|
|
null | null |
{}
|
LAKSHM11-G/pegasus-arxiv-pegasus_article_summarization3
| null |
[
"region:us"
] | null |
2024-04-25T04:30:06+00:00
|
|
text-classification
|
transformers
|
<!-- 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. -->
# robust_llm_pythia-14m_mz-131_IMDB
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_mz-131_IMDB", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-14m_mz-131_IMDB
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:30:53+00:00
|
text-generation
|
transformers
|
# [MaziyarPanahi/WizardLM-2-8x22B-GGUF](https://huggingface.co/MaziyarPanahi/WizardLM-2-8x22B-GGUF)
- Base model: [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1)
## Description
[MaziyarPanahi/WizardLM-2-8x22B-GGUF](https://huggingface.co/MaziyarPanahi/WizardLM-2-8x22B-GGUF) contains GGUF format model files for [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1).
## How to download
You can download only the quants you need instead of cloning the entire repository as follows:
```
huggingface-cli download MaziyarPanahi/WizardLM-2-8x22B-GGUF --local-dir . --include '*Q2_K*gguf'
```
On Windows:
```sh
huggingface-cli download MaziyarPanahi/WizardLM-2-8x22B-GGUF --local-dir . --include *Q4_K_S*gguf
```
## Load sharded model
`llama_load_model_from_file` will detect the number of files and will load additional tensors from the rest of files.
```sh
llama.cpp/main -m WizardLM-2-8x22B.Q2_K-00001-of-00005.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 1024 -e
```
## Prompt template
```
{system_prompt}
USER: {prompt}
ASSISTANT: </s>
```
or
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful,
detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
USER: {prompt} ASSISTANT: </s>......
```
|
{"tags": ["quantized", "2-bit", "GGUF", "transformers", "safetensors", "mistral", "text-generation", "arxiv:2304.12244", "arxiv:2306.08568", "arxiv:2308.09583", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "text-generation"], "model_name": "WizardLM-2-8x22B-GGUF", "inference": true, "base_model": "mistral-community/Mixtral-8x22B-v0.1", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"}
|
KingNish/WizardLM2-2bit
| null |
[
"transformers",
"safetensors",
"gguf",
"mixtral",
"text-generation",
"quantized",
"2-bit",
"GGUF",
"mistral",
"arxiv:2304.12244",
"arxiv:2306.08568",
"arxiv:2308.09583",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"conversational",
"base_model:mistral-community/Mixtral-8x22B-v0.1"
] | null |
2024-04-25T04:32:19+00:00
|
feature-extraction
|
transformers
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Last_BERT_V2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.40.0
- TensorFlow 2.15.0
- Tokenizers 0.19.1
|
{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "Last_BERT_V2", "results": []}]}
|
Gutema/Last_BERT_V2
| null |
[
"transformers",
"tf",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:33:11+00:00
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2018
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:33:16+00:00
|
|
null | null |
{}
|
ThuyNT/CS505_COQE_viT5_train_Instruction4_SOAPL_h1
| null |
[
"region:us"
] | null |
2024-04-25T04:34:40+00:00
|
|
text2text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
amaithi/pegasus-samsum-model
| null |
[
"transformers",
"safetensors",
"pegasus",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:34:52+00:00
|
token-classification
|
transformers
|
{"language": ["en"], "library_name": "transformers", "pipeline_tag": "token-classification"}
|
pchaudhari-ggc/finetunedBERT-pii
| null |
[
"transformers",
"safetensors",
"token-classification",
"en",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:35:30+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
HenryCai1129/adapter-toxic2nontoxic-100-50-0.004
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:35:43+00:00
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2019
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:36:30+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": ["trl", "sft"]}
|
dickdiss/phi-3_qlora_consumer
| null |
[
"transformers",
"safetensors",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:37:01+00:00
|
text-classification
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
Lakshit11/bert-15-categories
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:37:49+00:00
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2020
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:39:49+00:00
|
|
null |
transformers
|
# oceansweep/c4ai-command-r-v01-Q6_K-GGUF
This model was converted to GGUF format from [`CohereForAI/c4ai-command-r-v01`](https://huggingface.co/CohereForAI/c4ai-command-r-v01) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/CohereForAI/c4ai-command-r-v01) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo oceansweep/c4ai-command-r-v01-Q6_K-GGUF --model c4ai-command-r-v01.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo oceansweep/c4ai-command-r-v01-Q6_K-GGUF --model c4ai-command-r-v01.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m c4ai-command-r-v01.Q6_K.gguf -n 128
```
|
{"language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"]}
|
oceansweep/c4ai-command-r-v01-Q6_K-GGUF
| null |
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:39:58+00:00
|
null | null |
{"license": "llama3"}
|
Cheolman/llama3_lora_model_v1.0
| null |
[
"safetensors",
"license:llama3",
"region:us"
] | null |
2024-04-25T04:41:55+00:00
|
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2021
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:43:00+00:00
|
|
text-generation
|
transformers
|
{}
|
shauray/Mistral-DPO-Uncensored
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:43:20+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
JuniorThap/clip-wangchanroberta-lora
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:43:48+00:00
|
text-generation
|
transformers
|
<!-- 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. -->
# 0.0_ablation_4iters_bs128_nodpo_iter_3
This model is a fine-tuned version of [ShenaoZhang/0.0_ablation_4iters_bs128_nodpo_iter_2](https://huggingface.co/ShenaoZhang/0.0_ablation_4iters_bs128_nodpo_iter_2) on the updated and the original datasets.
## 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: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.0_ablation_4iters_bs128_nodpo_iter_2", "model-index": [{"name": "0.0_ablation_4iters_bs128_nodpo_iter_3", "results": []}]}
|
ShenaoZhang/0.0_ablation_4iters_bs128_nodpo_iter_3
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.0_ablation_4iters_bs128_nodpo_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:45:23+00:00
|
text-generation
|
transformers
|
{}
|
valudollar/llama-2-7b-capstone
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:45:47+00:00
|
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2022
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:46:15+00:00
|
|
text-classification
|
transformers
|
{}
|
nnngoc/ms-marco-MiniLM-L-6-v2-32-5M-1
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null |
2024-04-25T04:47:25+00:00
|
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2023
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:49:43+00:00
|
|
image-text-to-text
|
xtuner
|
<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
[](https://github.com/InternLM/xtuner)
</div>
## Model
llava-phi-3-mini is a LLaVA model fine-tuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner).
**Note: This model is in XTuner LLaVA format.**
Resources:
- GitHub: [xtuner](https://github.com/InternLM/xtuner)
- HuggingFace LLaVA format model: [xtuner/llava-phi-3-mini-hf](https://huggingface.co/xtuner/llava-phi-3-mini-hf)
- Official LLaVA format model: [xtuner/llava-phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini)
- GGUF LLaVA model: [xtuner/llava-phi-3-mini-gguf](https://huggingface.co/xtuner/llava-phi-3-mini-gguf)
## Details
| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset | Pretrain Epoch | Fine-tune Epoch |
| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: | -------------- | --------------- |
| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 |
| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 |
| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 1 |
| **LLaVA-Phi-3-mini** | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Full ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 2 |
## Results
<div align="center">
<img src="https://github.com/InternLM/xtuner/assets/36994684/78524f65-260d-4ae3-a687-03fc5a19dcbb" alt="Image" width=500" />
</div>
| Model | MMBench Test (EN) | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar |
| :-------------------- | :---------------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: |
| LLaVA-v1.5-7B | 66.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 |
| LLaVA-Llama-3-8B | 68.9 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 |
| LLaVA-Llama-3-8B-v1.1 | 72.3 | 37.1 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 |
| **LLaVA-Phi-3-mini** | 69.2 | 41.4 | 70.0 | 69.3 | 73.7 | 49.8 | 87.3 | 61.5 | 57.8 | 1477/313 | 43.7 |
## Quickstart
### Installation
```shell
pip install 'git+https://github.com/InternLM/xtuner.git#egg=xtuner[deepspeed]'
```
### Chat
```shell
xtuner chat xtuner/llava-phi-3-mini-xtuner \
--llava xtuner/llava-phi-3-mini-xtuner \
--prompt-template phi3_chat \
--image $IMAGE_PATH
```
### MMBench Evaluation
XTuner integrates the MMBench evaluation, and you can perform evaluations with the following command!
```bash
xtuner mmbench xtuner/llava-phi-3-mini-xtuner \
--llava xtuner/llava-phi-3-mini-xtuner \
--prompt-template phi3_chat \
--data-path $MMBENCH_DATA_PATH \
--work-dir $RESULT_PATH
```
After the evaluation is completed, if it's a development set, it will directly print out the results; If it's a test set, you need to submit `mmbench_result.xlsx` to the official MMBench for final evaluation to obtain precision results!
### Reproduce
Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme).
## Citation
```bibtex
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}
```
|
{"library_name": "xtuner", "datasets": ["Lin-Chen/ShareGPT4V"], "pipeline_tag": "image-text-to-text"}
|
xtuner/llava-phi-3-mini-xtuner
| null |
[
"xtuner",
"safetensors",
"llama",
"image-text-to-text",
"dataset:Lin-Chen/ShareGPT4V",
"region:us"
] | null |
2024-04-25T04:50:11+00:00
|
text-generation
|
transformers
|
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [kalytm/nous-0](https://huggingface.co/kalytm/nous-0)
* [kalytm/nous-2](https://huggingface.co/kalytm/nous-2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: kalytm/nous-2
layer_range: [0, 24]
- model: kalytm/nous-0
layer_range: [0, 24]
merge_method: slerp
base_model: kalytm/nous-2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["kalytm/nous-0", "kalytm/nous-2"]}
|
Sumail/Ame20
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:kalytm/nous-0",
"base_model:kalytm/nous-2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:51:16+00:00
|
text-generation
|
transformers
|
<!-- 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. -->
# 0.01_ablation_4iters_bs128_nodpo_iter_3
This model is a fine-tuned version of [ShenaoZhang/0.01_ablation_4iters_bs128_nodpo_iter_2](https://huggingface.co/ShenaoZhang/0.01_ablation_4iters_bs128_nodpo_iter_2) on the updated and the original datasets.
## 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: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.01_ablation_4iters_bs128_nodpo_iter_2", "model-index": [{"name": "0.01_ablation_4iters_bs128_nodpo_iter_3", "results": []}]}
|
ShenaoZhang/0.01_ablation_4iters_bs128_nodpo_iter_3
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.01_ablation_4iters_bs128_nodpo_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T04:52:32+00:00
|
null | null |
{}
|
miricalderonr/vit-skin-skan
| null |
[
"region:us"
] | null |
2024-04-25T04:52:39+00:00
|
|
text-classification
|
transformers
|
{}
|
kwoncho/gaincut_news_pre2024
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:53:01+00:00
|
|
null | null |
{"license": "other", "license_name": "carti", "license_link": "LICENSE"}
|
y0usly/DeepVoiceCarti_250_Epochs
| null |
[
"license:other",
"region:us"
] | null |
2024-04-25T04:54:54+00:00
|
|
null | null |
{}
|
tirkens/boom
| null |
[
"region:us"
] | null |
2024-04-25T04:58:38+00:00
|
|
null |
peft
|
<!-- 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. -->
# trainer
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0849
- eval_runtime: 37.5454
- eval_samples_per_second: 0.692
- eval_steps_per_second: 0.346
- epoch: 3.0
- step: 354
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "trainer", "results": []}]}
|
Surabhi-K/llama_3_3epochs
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null |
2024-04-25T04:58:40+00:00
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** Hinglish-Project
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-7b-bnb-4bit"}
|
Hinglish-Project/llama-3-8b-English-to-Hinglish
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:58:58+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- 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. -->
# Whisper Small Yo - Oyemade Oyemaja
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1731
- Wer Ortho: 70.4834
- Wer: 49.8612
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:-------:|
| 0.2595 | 3.8462 | 500 | 0.7546 | 71.3700 | 52.0488 |
| 0.0312 | 7.6923 | 1000 | 0.9057 | 74.6210 | 53.1174 |
| 0.0134 | 11.5385 | 1500 | 1.0199 | 72.2090 | 51.7711 |
| 0.0059 | 15.3846 | 2000 | 1.0713 | 71.2842 | 51.6281 |
| 0.0087 | 19.2308 | 2500 | 1.1007 | 70.5787 | 50.1136 |
| 0.006 | 23.0769 | 3000 | 1.1568 | 70.8552 | 50.6100 |
| 0.0059 | 26.9231 | 3500 | 1.1327 | 69.0438 | 48.4645 |
| 0.0043 | 30.7692 | 4000 | 1.1731 | 70.4834 | 49.8612 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"language": ["yo"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Yo - Oyemade Oyemaja", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16", "type": "mozilla-foundation/common_voice_16_1", "config": "yo", "split": "test", "args": "yo"}, "metrics": [{"type": "wer", "value": 49.86116954143879, "name": "Wer"}]}]}]}
|
oyemade/whisper-small-yo
| null |
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"yo",
"dataset:mozilla-foundation/common_voice_16_1",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T04:59:51+00:00
|
null |
peft
|
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
{"library_name": "peft"}
|
Rimyy/TentativeLlamaGsm5ep
| null |
[
"peft",
"region:us"
] | null |
2024-04-25T05:00:24+00:00
|
null | null |
# Mixtral 8x22B Instruct v0.1 - llamafile
This repository contains executable weights (which we call
[llamafiles](https://github.com/Mozilla-Ocho/llamafile)) that run on
Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64.
- Model creator: [Mistral AI](https://mistral.ai/)
- Original model: [mistralai/Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1)
## Quickstart
Assuming your system has at least 128GB of RAM, you can try running the
following command which download, concatenate, and execute the model.
```
( curl -L https://huggingface.co/jartine/Mixtral-8x22B-Instruct-v0.1-llamafile/resolve/main/Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile.cat0
curl -L https://huggingface.co/jartine/Mixtral-8x22B-Instruct-v0.1-llamafile/resolve/main/Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile.cat1
) > Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile
chmod +x Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile
./Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile --help # view manual
./Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile # launch web gui + oai api
./Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile -p ... # cli interface (scriptable)
```
Alternatively, you may download an official `llamafile` executable from
Mozilla Ocho on GitHub, in which case you can use the Mixtral llamafiles
as a simple weights data file.
```
llamafile -m Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile ...
```
For further information, please see the [llamafile
README](https://github.com/mozilla-ocho/llamafile/).
Having **trouble?** See the ["Gotchas"
section](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas)
of the README.
## Prompting
Prompt template:
```
[INST] {{prompt}} [/INST]
```
Command template:
```
./Mixtral-8x22B-Instruct-v0.1.Q4_0.llamafile -p "[INST]{{prompt}}[/INST]"
```
## About llamafile
llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023.
It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp
binaries that run on the stock installs of six OSes for both ARM64 and
AMD64.
In addition to being executables, llamafiles are also zip archives. Each
llamafile contains a GGUF file, which you can extract using the `unzip`
command. If you want to change or add files to your llamafiles, then the
`zipalign` command (distributed on the llamafile github) should be used
instead of the traditional `zip` command.
## About Upload Limits
Files which exceed the Hugging Face 50GB upload limit have a .cat𝑋
extension. You need to use the `cat` command locally to turn them back
into a single file, using the same order.
## About Quantization Formats (General Advice)
Your choice of quantization format depends on three things:
1. Will it fit in RAM or VRAM?
2. Is your use case reading (e.g. summarization) or writing (e.g. chatbot)?
3. llamafiles bigger than 4.30 GB are hard to run on Windows (see [gotchas](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas))
Good quants for writing (prediction speed) are Q5\_K\_M, and Q4\_0. Text
generation is bounded by memory speed, so smaller quants help, but they
cause the LLM to hallucinate more. However that doesn't mean they can't
think correctly. A highly degraded quant like `Q2_K` may not make a
great encyclopedia, but it's still capable of logical reasoning and
the emergent capabilities LLMs exhibit.
Good quants for reading (evaluation speed) are BF16, F16, Q8\_0, and
Q4\_0 (ordered from fastest to slowest). Prompt evaluation is bounded by
flop count, which means perf can be improved through software
engineering alone, e.g. BLAS algorithms, in which case quantization
starts hurting more than it helps, since it competes for CPU resources
and makes it harder for the compiler to parallelize instructions. You
want to ideally use the simplest smallest floating point format that's
natively implemented by your hardware. In most cases, that's BF16 or
FP16. However, llamafile is able to still offer respectable tinyBLAS
speedups for llama.cpp's simplest quants: Q8\_0 and Q4\_0.
## Hardware Choices (Mixtral 8x22B Specific)
This model is very large. Even at Q2 quantization, it's still well-over
twice as large the highest tier NVIDIA gaming GPUs. llamafile supports
splitting models over multiple GPUs (for NVIDIA only currently) if you
have such a system. The easiest way to have one, if you don't, is to pay
a few bucks an hour to rent a 4x RTX 4090 rig off vast.ai.
Mac Studio is a good option for running this model locally. An M2 Ultra
desktop from Apple is affordable and has 128GB of unified RAM+VRAM. If
you have one, then llamafile will use your Metal GPU. Try starting out
with the `Q4_0` quantization level.
Another good option for running large, large language models locally and
fully under your control is to just use CPU inference. We developed new
tensor multiplication kernels on the llamafile project specifically to
speed up "mixture of experts" LLMs like Mixtral. On a AMD Threadripper
Pro 7995WX with 256GB of 5200 MT/s RAM, llamafile v0.8 runs Mixtral
8x22B Q4\_0 on Linux at 98 tokens per second for evaluation, and it
predicts 9.44 tokens per second.
---
# Model Card for Mixtral-8x22B-Instruct-v0.1
The Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1).
## Run the model
```python
from transformers import AutoModelForCausalLM
from mistral_common.protocol.instruct.messages import (
AssistantMessage,
UserMessage,
)
from mistral_common.protocol.instruct.tool_calls import (
Tool,
Function,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
device = "cuda" # the device to load the model onto
tokenizer_v3 = MistralTokenizer.v3()
mistral_query = ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris"),
],
model="test",
)
encodeds = tokenizer_v3.encode_chat_completion(mistral_query).tokens
model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
sp_tokenizer = tokenizer_v3.instruct_tokenizer.tokenizer
decoded = sp_tokenizer.decode(generated_ids[0])
print(decoded)
```
# Instruct tokenizer
The HuggingFace tokenizer included in this release should match our own. To compare:
`pip install mistral-common`
```py
from mistral_common.protocol.instruct.messages import (
AssistantMessage,
UserMessage,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
from transformers import AutoTokenizer
tokenizer_v3 = MistralTokenizer.v3()
mistral_query = ChatCompletionRequest(
messages=[
UserMessage(content="How many experts ?"),
AssistantMessage(content="8"),
UserMessage(content="How big ?"),
AssistantMessage(content="22B"),
UserMessage(content="Noice 🎉 !"),
],
model="test",
)
hf_messages = mistral_query.model_dump()['messages']
tokenized_mistral = tokenizer_v3.encode_chat_completion(mistral_query).tokens
tokenizer_hf = AutoTokenizer.from_pretrained('mistralai/Mixtral-8x22B-Instruct-v0.1')
tokenized_hf = tokenizer_hf.apply_chat_template(hf_messages, tokenize=True)
assert tokenized_hf == tokenized_mistral
```
# Function calling and special tokens
This tokenizer includes more special tokens, related to function calling :
- [TOOL_CALLS]
- [AVAILABLE_TOOLS]
- [/AVAILABLE_TOOLS]
- [TOOL_RESULTS]
- [/TOOL_RESULTS]
If you want to use this model with function calling, please be sure to apply it similarly to what is done in our [SentencePieceTokenizerV3](https://github.com/mistralai/mistral-common/blob/main/src/mistral_common/tokens/tokenizers/sentencepiece.py#L299).
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux,
Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,
Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot,
Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger,
Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona,
Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon,
Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat,
Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen,
Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,
Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang,
Valera Nemychnikova, William El Sayed, William Marshall
|
{"language": ["en"], "license": "apache-2.0", "tags": ["llamafile"], "base_model": "mistralai/Mixtral-8x22B-Instruct-v0.1", "model_creator": "mistralai", "quantized_by": "jartine", "prompt_template": "[INST] {{prompt}} [/INST]\n"}
|
jartine/Mixtral-8x22B-Instruct-v0.1-llamafile
| null |
[
"llamafile",
"en",
"base_model:mistralai/Mixtral-8x22B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T05:00:51+00:00
|
null | null |
{"license": "mit"}
|
sureshsanghani/sn25
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-25T05:01:50+00:00
|
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** Hinglish-Project
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-7b-bnb-4bit"}
|
Hinglish-Project/gemma-7b-English-to-Hinglish
| null |
[
"transformers",
"safetensors",
"gguf",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:02:12+00:00
|
null | null |
{}
|
sajjadrahman56/gemma-chatbot
| null |
[
"region:us"
] | null |
2024-04-25T05:02:21+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
ivrit-ai/whisper-large-v2-150-2000-steps
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:02:43+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}
|
4-alokk/gemma-7b-English-to-Hinglish
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:03:27+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_OSPAL_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_OSPAL_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_OSPAL_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:06:46+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_SOPAL_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SOPAL_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_SOPAL_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:07:15+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_ASPOL_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_ASPOL_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:07:40+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_PSAOL_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_PSAOL_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_PSAOL_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:10:22+00:00
|
text-to-audio
|
transformers
|
{"license": "apache-2.0"}
|
awajai/speecht5_tts_nepali_slr43-143-swos
| null |
[
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:10:37+00:00
|
|
null | null |
{}
|
ZamiSanj/falcon-7b-sharded-fp16-finetuned-mental-health-conversational
| null |
[
"tensorboard",
"safetensors",
"region:us"
] | null |
2024-04-25T05:13:50+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
devkya/openai-whisper-large-ko-transcribe-self
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:15:48+00:00
|
null | null |
{}
|
Fabricioalan/tiranovoice
| null |
[
"region:us"
] | null |
2024-04-25T05:16:18+00:00
|
|
fill-mask
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
sally9805/bert-base-uncased-finetuned-news-1929-1932
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:16:41+00:00
|
null | null |
{"license": "openrail"}
|
YangPa/HanJiMin
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-25T05:17:09+00:00
|
|
text-generation
|
transformers
|
(Maybe i'll change the waifu picture later)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than the Mixtral 8x7B and it's finetunes in RP/ERP tasks.
[GGUF, Exl2](https://huggingface.co/collections/xxx777xxxASD/chaoticsoliloquy-4x8b-6628a759b5a60d8d3f51ed62)
### ChaoticSoliloquy-4x8B
```
base_model: jeiku_Chaos_RP_l3_8B
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: ChaoticNeutrals_Poppy_Porpoise-v0.6-L3-8B
- source_model: jeiku_Chaos_RP_l3_8B
- source_model: openlynn_Llama-3-Soliloquy-8B
- source_model: Sao10K_L3-Solana-8B-v1
```
## Models used
- [ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B)
- [jeiku/Chaos_RP_l3_8B](https://huggingface.co/jeiku/Chaos_RP_l3_8B)
- [openlynn/Llama-3-Soliloquy-8B](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B)
- [Sao10K/L3-Solana-8B-v1](https://huggingface.co/Sao10K/L3-Solana-8B-v1)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/Llava_1.5_Llama3_mmproj)
## Prompt format: Llama 3
|
{"language": ["en"], "license": "llama3", "tags": ["moe"]}
|
zaq-hack/ChaoticSoliloquy-4x8B-bpw800-h8-exl2-rpcal
| null |
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null |
2024-04-25T05:18:53+00:00
|
null | null |
{"license": "mit"}
|
zizi0123/Qwen1.5-0.5B-sft
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-25T05:19:01+00:00
|
|
video-classification
|
transformers
|
<!-- 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. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1489
- Accuracy: 0.9429
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7623 | 0.25 | 75 | 1.3281 | 0.7 |
| 0.9755 | 1.25 | 150 | 0.5068 | 0.8143 |
| 0.3218 | 2.25 | 225 | 0.2246 | 0.9714 |
| 0.1334 | 3.25 | 300 | 0.1489 | 0.9429 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-ucf101-subset", "results": []}]}
|
Yash2003/videomae-base-finetuned-ucf101-subset
| null |
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:19:08+00:00
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
rahul9699/wav2vec2-base-gig-demo-colab
| null |
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:19:11+00:00
|
null |
transformers
|
# oceansweep/c4ai-command-r-v01-Q8_0-GGUF
This model was converted to GGUF format from [`CohereForAI/c4ai-command-r-v01`](https://huggingface.co/CohereForAI/c4ai-command-r-v01) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/CohereForAI/c4ai-command-r-v01) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo oceansweep/c4ai-command-r-v01-Q8_0-GGUF --model c4ai-command-r-v01.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo oceansweep/c4ai-command-r-v01-Q8_0-GGUF --model c4ai-command-r-v01.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m c4ai-command-r-v01.Q8_0.gguf -n 128
```
|
{"language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"]}
|
oceansweep/c4ai-command-r-v01-Q8_0-GGUF
| null |
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:20:40+00:00
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": ["unsloth"]}
|
ravindrakinagi/abc
| null |
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:21:39+00:00
|
null | null |
{}
|
Laddoo/AnalyizedTraining
| null |
[
"region:us"
] | null |
2024-04-25T05:21:56+00:00
|
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
EpicJhon/llama_67
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:23:23+00:00
|
text-classification
|
transformers
|
{"license": "gemma"}
|
justshao/gemma-7b-with-confidence
| null |
[
"transformers",
"safetensors",
"gemma",
"text-classification",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:23:58+00:00
|
|
null | null |
{}
|
kyuhyun/final_4bit_DST_10per_4epochs_free
| null |
[
"region:us"
] | null |
2024-04-25T05:24:35+00:00
|
|
text-classification
|
transformers
|
<!-- 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. -->
# robust_llm_pythia-14m_mz-131_PasswordMatch
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_mz-131_PasswordMatch", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-14m_mz-131_PasswordMatch
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T05:24:45+00:00
|
text-generation
|
transformers
|
Quantizations of https://huggingface.co/lmsys/vicuna-7b-v1.5
# From original readme
...
|
{"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "lmsys", "vicuna-7b-v1.5"], "inference": false, "pipeline_tag": "text-generation"}
|
duyntnet/vicuna-7b-v1.5-imatrix-GGUF
| null |
[
"transformers",
"gguf",
"imatrix",
"lmsys",
"vicuna-7b-v1.5",
"text-generation",
"en",
"license:other",
"region:us"
] | null |
2024-04-25T05:24:59+00:00
|
null | null |
{}
|
KSGAI/ANYUJIN
| null |
[
"region:us"
] | null |
2024-04-25T05:24:59+00:00
|
|
null | null |
# sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview-Q4_K_M-GGUF
This model was converted to GGUF format from [`sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview`](https://huggingface.co/sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview-Q4_K_M-GGUF --model hansoldeco-beomi-llama-3-open-ko-8b-instruct-preview.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview-Q4_K_M-GGUF --model hansoldeco-beomi-llama-3-open-ko-8b-instruct-preview.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m hansoldeco-beomi-llama-3-open-ko-8b-instruct-preview.Q4_K_M.gguf -n 128
```
|
{"tags": ["llama-cpp", "gguf-my-repo"]}
|
sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview-Q4_K_M-GGUF
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null |
2024-04-25T05:25:40+00:00
|
null | null |
{"license": "creativeml-openrail-m"}
|
GregoRio123/ryc
| null |
[
"license:creativeml-openrail-m",
"region:us"
] | null |
2024-04-25T05:27:38+00:00
|
|
null | null |
{}
|
HenryCai1129/adapter-toxic2nontoxic-100-50-0.005
| null |
[
"region:us"
] | null |
2024-04-25T05:28:02+00:00
|
|
null | null |
# 0425
This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the alpaca_formatted_ift_eft_Justification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8213
## Model description
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:
* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B 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 the [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- total_eval_batch_size: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
| :-----------: | :----: | :--: | :-------------: |
| 1.0669 | 0.2018 | 100 | 0.8823 |
| 0.9156 | 0.4036 | 200 | 0.8593 |
| 0.9509 | 0.6054 | 300 | 0.8491 |
| 0.8287 | 0.8073 | 400 | 0.8423 |
| 0.8772 | 1.0091 | 500 | 0.8390 |
| 0.9101 | 1.2109 | 600 | 0.8385 |
| 0.8212 | 1.4127 | 700 | 0.8342 |
| 0.8721 | 1.6145 | 800 | 0.8327 |
| 1.0033 | 1.8163 | 900 | 0.8319 |
| 0.9879 | 2.0182 | 1000 | 0.8276 |
| 0.964 | 2.2200 | 1100 | 0.8276 |
| 0.8409 | 2.4218 | 1200 | 0.8264 |
| 0.8055 | 2.6236 | 1300 | 0.8262 |
| 1.0026 | 2.8254 | 1400 | 0.8240 |
| 0.881 | 3.0272 | 1500 | 0.8241 |
| 1.0058 | 3.2291 | 1600 | 0.8226 |
| 0.8747 | 3.4309 | 1700 | 0.8205 |
| 0.8686 | 3.6327 | 1800 | 0.8215 |
| 0.8838 | 3.8345 | 1900 | 0.8208 |
| 0.8246 | 4.0363 | 2000 | 0.8218 |
| 0.8727 | 4.2381 | 2100 | 0.8216 |
| 0.8737 | 4.4400 | 2200 | 0.8214 |
| 0.8955 | 4.6418 | 2300 | 0.8214 |
| 0.8909 | 4.8436 | 2400 | 0.8215 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
|
{"license": "mit"}
|
WDong/Qwen1.5-7B-SFT-0425
| null |
[
"safetensors",
"license:mit",
"region:us"
] | null |
2024-04-25T05:28:13+00:00
|
null |
peft
|
<!-- 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. -->
# mistral7binstruct_summarize
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5071
## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8045 | 0.2475 | 25 | 1.5872 |
| 1.6052 | 0.4950 | 50 | 1.5071 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral7binstruct_summarize", "results": []}]}
|
JerryKChiang/mistral7binstruct_summarize
| null |
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T05:28:46+00:00
|
null | null |
# CapHuman: CapHuman: Capture Your Moments in Parallel Universes
caphuman.ckpt: based on Stable Diffusion V1.5
pytorch3d-0.7.6-cp37-cp37m-linux_x86_64.whl: pytorch3d 0.7.6, Ubuntu 18.04, python 3.7.19, torch 1.13.1, CUDA 11.7
---
license: cc-by-nc-4.0
---
|
{}
|
VamosC/CapHuman
| null |
[
"region:us"
] | null |
2024-04-25T05:32:12+00:00
|
null | null |
{}
|
ishavverma/tinyllama-colorist-test
| null |
[
"tensorboard",
"safetensors",
"region:us"
] | null |
2024-04-25T05:33:06+00:00
|
|
null |
transformers
|
# Function Calling and Tool Use LLaMA Models
This repository contains two main versions of LLaMA models fine-tuned for function calling and tool use capabilities:
1. Fine-tuned version of the `LLama3-8b-instruct` model
2. `tinyllama` - a smaller model version
For each version, the following variants are available:
- 16-bit quantized model
- 4-bit quantized model
- GGFU format for use with llama.cpp
## Dataset
The models were fine-tuned using a modified version of the `ilacai/glaive-function-calling-v2-sharegpt` dataset, which can be found at [unclecode/glaive-function-calling-llama3](https://huggingface.co/datasets/unclecode/glaive-function-calling-llama3).
## Usage
To learn how to use these models, refer to the Colab notebook: [](https://tinyurl.com/ucfllm)
This is the first version of the models, and work is in progress to further train them with multi-tool detection and native tool binding support.
## Library and Tools Support
A library is being developed to manage tools and add tool support for major LLMs, regardless of their built-in capabilities. You can find examples and contribute to the library at the following repository:
[https://github.com/unclecode/fllm](https://github.com/unclecode/fllm)
Please open an issue in the repository for any bugs or collaboration requests.
## Other Models
Here are links to other related models:
- [unclecode/llama3-function-call-lora-adapter-240424](https://huggingface.co/unclecode/llama3-function-call-lora-adapter-240424)
- [unclecode/llama3-function-call-16bit-240424](https://huggingface.co/unclecode/llama3-function-call-16bit-240424)
- [unclecode/llama3-function-call-4bit-240424](https://huggingface.co/unclecode/llama3-function-call-4bit-240424)
- [unclecode/llama3-function-call-Q4_K_M_GGFU-240424](https://huggingface.co/unclecode/llama3-function-call-Q4_K_M_GGFU-240424)
- [unclecode/tinyllama-function-call-lora-adapter-250424](https://huggingface.co/unclecode/tinyllama-function-call-lora-adapter-250424)
- [unclecode/tinyllama-function-call-16bit-250424](https://huggingface.co/unclecode/tinyllama-function-call-16bit-250424)
- [unclecode/tinyllama-function-call-Q4_K_M_GGFU-250424](https://huggingface.co/unclecode/tinyllama-function-call-Q4_K_M_GGFU-250424)
## License
These models are released under the Apache 2.0 license.
# Uploaded model
- **Developed by:** unclecode
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-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)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["function calling", "tool use", "llama", "llama3", "tinyllama", "instruct-tuned", "4-bit quantization", "ggfu", "text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"}
|
unclecode/tinyllama-function-call-lora-adapter-250424
| null |
[
"transformers",
"safetensors",
"function calling",
"tool use",
"llama",
"llama3",
"tinyllama",
"instruct-tuned",
"4-bit quantization",
"ggfu",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:35:58+00:00
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## 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. -->
[More Information Needed]
### 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 [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
abhayesian/MistralBobzillaTest
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T05:36:05+00:00
|
null |
diffusers
|
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/sayakpaul/diffusion-orpo-lora-sdxl/runs/ufwurjxt).
|
{}
|
diffusion-orpo/sdxl-orpo-large-beta_orpo-0.005-beta_inner-100-lr-1e-8-scaled-ema
| null |
[
"diffusers",
"safetensors",
"region:us"
] | null |
2024-04-25T05:37:01+00:00
|
null | null |
```
e88 88e d8
d888 888b 8888 8888 ,"Y88b 888 8e d88
C8888 8888D 8888 8888 "8" 888 888 88b d88888
Y888 888P Y888 888P ,ee 888 888 888 888
"88 88" "88 88" "88 888 888 888 888
b
8b,
e88'Y88 d8 888
d888 'Y ,"Y88b 888,8, d88 ,e e, 888
C8888 "8" 888 888 " d88888 d88 88b 888
Y888 ,d ,ee 888 888 888 888 , 888
"88,d88 "88 888 888 888 "YeeP" 888
PROUDLY PRESENTS
```
## Llama-3-8B-EGO-iMat-GGUF
Quantized from fp32 with love.
* Weighted quantizations were calculated using groups_merged.txt with 105 chunks (recommended amount for this file) and n_ctx=512. Special thanks to jukofyork for sharing [this process](https://huggingface.co/jukofyork/WizardLM-2-8x22B-imatrix)
<b>**Note - Please use SillyTavern as well as the following prompt format:**</b>
```
[EGO]Name: Character name and then Everything that forms the personality and speech patterns.(i.e. scenario, sample dialogue, character definitions, etc)[/EGO]
[SEEN]User message.[/SEEN]
Character Name:
```
For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747)
<b>All quants are verified working prior to uploading to repo for your safety and convenience. </b>
It's highly recommended to stick to higher quants of this model due to the unique nature of its pseudotokens
Original model card [here](https://huggingface.co/Envoid/Llama-3-8B-EGO) and below
---
# This model isn't particularly great. It's just an undercooked experiment.
Releasing it anyways just in case it accidentally makes good merge meat.
# It also has a tendency to produce mature content without warning.
This model is tuned off of the base Llama-3-8B model.
I adapted the leaked Undi dataset into training samples for custom formatting. This model pretty much only functions properly in SillyTavern.
The formatting has two pairs of pseudotokens
```
[EGO]Name: Character name and then Everything that forms the personality and speech patterns.(i.e. scenario, sample dialogue, character definitions, etc)[/EGO]
[SEEN]User message.[/SEEN]
Character Name:
```
The self attention modules were fine tuned separately on this dataset and the pseudotokens were chosen because they made logical sense with respect to the character giving a reply without allowing the model to 'connect the dots' during training and figure out that it is indeed an AI language model.
After this was done all modules were then finetuned together on the dendrite dataset in order to connect the changes made to the attention modules.
So with regards to building a SillyTavern prompt template you basically want the entire story string and any additional stylistic instructions enclosed in the [EGO] tags and then the user messages enclosed in [SEEN] tags.
It doesn't give particularly verbose replies unless you're continueing a roleplay with verbose messages. Otherwise it's pretty bad.
|
{"license": "cc-by-nc-4.0", "tags": ["GGUF", "iMat", "llama3"]}
|
Quant-Cartel/Llama-3-8B-EGO-iMat-GGUF
| null |
[
"gguf",
"GGUF",
"iMat",
"llama3",
"license:cc-by-nc-4.0",
"region:us"
] | null |
2024-04-25T05:37:21+00:00
|
null | null |
{}
|
Neha13/distilbert-base-uncased
| null |
[
"region:us"
] | null |
2024-04-25T05:37:26+00:00
|
|
unconditional-image-generation
|
diffusers
|
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('wsincos/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}
|
wsincos/sd-class-butterflies-32
| null |
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | null |
2024-04-25T05:39:06+00:00
|
null | null |
{"license": "apache-2.0"}
|
opensporks/stocknewseventssentiment-snes-10
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T05:39:10+00:00
|
|
null | null |
{}
|
mbookamer/project-pickup-tuned
| null |
[
"region:us"
] | null |
2024-04-25T05:39:33+00:00
|
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