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QuantFactory/deepseek-coder-6.7b-instruct-GGUF | QuantFactory | "2024-06-09T13:19:24Z" | 2,915 | 0 | null | [
"gguf",
"text-generation",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"license:other",
"region:us"
] | text-generation | "2024-06-09T05:35:48Z" | ---
license: other
license_name: deepseek
license_link: LICENSE
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
pipeline_tag: text-generation
---
# QuantFactory/deepseek-coder-6.7b-instruct-GGUF
This is quantized version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) created using llama.cpp
# Model Description
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[๐ Homepage]</a> | <a href="https://coder.deepseek.com/">[๐ค Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(ๅพฎไฟก)]</a> </p>
<hr>
### 1. Introduction of Deepseek Coder
Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
- **Massive Training Data**: Trained from scratch fon 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
### 2. Model Summary
deepseek-coder-6.7b-instruct is a 6.7B parameter model initialized from deepseek-coder-6.7b-base and fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]). |
ielabgroup/vec2text_gtr-base-st_inversion | ielabgroup | "2024-01-28T23:35:41Z" | 2,914 | 0 | transformers | [
"transformers",
"safetensors",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-01-28T03:46:53Z" | ---
license: apache-2.0
---
To use the model, check vec2text repo [https://github.com/jxmorris12/vec2text](https://github.com/jxmorris12/vec2text)
# Example:
```python
from sentence_transformers import SentenceTransformer
import vec2text
import transformers
inversion_model = vec2text.models.InversionModel.from_pretrained(
"ielabgroup/vec2text_gtr-base-st_inversion"
)
model = vec2text.models.CorrectorEncoderModel.from_pretrained(
"ielabgroup/vec2text_gtr-base-st_corrector"
)
inversion_trainer = vec2text.trainers.InversionTrainer(
model=inversion_model,
train_dataset=None,
eval_dataset=None,
data_collator=transformers.DataCollatorForSeq2Seq(
inversion_model.tokenizer,
label_pad_token_id=-100,
),
)
model.config.dispatch_batches = None
corrector = vec2text.trainers.Corrector(
model=model,
inversion_trainer=inversion_trainer,
args=None,
data_collator=vec2text.collator.DataCollatorForCorrection(
tokenizer=inversion_trainer.model.tokenizer
),
)
model = SentenceTransformer('sentence-transformers/gtr-t5-base')
embeddings = model.encode([
"Jack Morris is a PhD student at Cornell Tech in New York City",
"It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity"
], convert_to_tensor=True,).to('mps')
vec2text.invert_embeddings(
embeddings=embeddings,
corrector=corrector,
num_steps=20,
)
[' Jack Morris is a PhD student at Cornell Tech in New York', 'It was the best of times, it was the worst of times, it was the epoch of incredulity, it was age of']
``` |
mradermacher/Falcon2-8B-Czech-GGUF | mradermacher | "2024-06-05T17:10:34Z" | 2,913 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"cs",
"base_model:ssmits/Falcon2-8B-Czech",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-05T16:41:15Z" | ---
base_model: ssmits/Falcon2-8B-Czech
language:
- cs
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ssmits/Falcon2-8B-Czech
<!-- 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/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.IQ3_XS.gguf) | IQ3_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q5_K_M.gguf) | Q5_K_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.Q8_0.gguf) | Q8_0 | 8.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Czech-GGUF/resolve/main/Falcon2-8B-Czech.f16.gguf) | f16 | 16.3 | 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 -->
|
oh-yeontaek/llama-2-13B-LoRA-assemble | oh-yeontaek | "2023-09-13T21:39:41Z" | 2,912 | 8 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-13T20:53:27Z" | Entry not found |
mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF | mradermacher | "2024-06-11T06:17:24Z" | 2,912 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TroyDoesAI/Mermaid-Flow-MoE-Expert2",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T20:27:38Z" | ---
base_model: TroyDoesAI/Mermaid-Flow-MoE-Expert2
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/TroyDoesAI/Mermaid-Flow-MoE-Expert2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-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/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Flow-MoE-Expert2-i1-GGUF/resolve/main/Mermaid-Flow-MoE-Expert2.i1-Q6_K.gguf) | i1-Q6_K | 8.9 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf | RichardErkhov | "2024-06-29T14:25:42Z" | 2,912 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"region:us"
] | null | "2024-06-29T13:57:10Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Cinder-1.3B-Test - GGUF
- Model creator: https://huggingface.co/Josephgflowers/
- Original model: https://huggingface.co/Josephgflowers/Cinder-1.3B-Test/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Cinder-1.3B-Test.Q2_K.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q2_K.gguf) | Q2_K | 0.46GB |
| [Cinder-1.3B-Test.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.IQ3_XS.gguf) | IQ3_XS | 0.51GB |
| [Cinder-1.3B-Test.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.IQ3_S.gguf) | IQ3_S | 0.54GB |
| [Cinder-1.3B-Test.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q3_K_S.gguf) | Q3_K_S | 0.54GB |
| [Cinder-1.3B-Test.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.IQ3_M.gguf) | IQ3_M | 0.56GB |
| [Cinder-1.3B-Test.Q3_K.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q3_K.gguf) | Q3_K | 0.59GB |
| [Cinder-1.3B-Test.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q3_K_M.gguf) | Q3_K_M | 0.59GB |
| [Cinder-1.3B-Test.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q3_K_L.gguf) | Q3_K_L | 0.64GB |
| [Cinder-1.3B-Test.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.IQ4_XS.gguf) | IQ4_XS | 0.66GB |
| [Cinder-1.3B-Test.Q4_0.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q4_0.gguf) | Q4_0 | 0.69GB |
| [Cinder-1.3B-Test.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.IQ4_NL.gguf) | IQ4_NL | 0.69GB |
| [Cinder-1.3B-Test.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q4_K_S.gguf) | Q4_K_S | 0.69GB |
| [Cinder-1.3B-Test.Q4_K.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q4_K.gguf) | Q4_K | 0.72GB |
| [Cinder-1.3B-Test.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q4_K_M.gguf) | Q4_K_M | 0.72GB |
| [Cinder-1.3B-Test.Q4_1.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q4_1.gguf) | Q4_1 | 0.76GB |
| [Cinder-1.3B-Test.Q5_0.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q5_0.gguf) | Q5_0 | 0.83GB |
| [Cinder-1.3B-Test.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q5_K_S.gguf) | Q5_K_S | 0.83GB |
| [Cinder-1.3B-Test.Q5_K.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q5_K.gguf) | Q5_K | 0.85GB |
| [Cinder-1.3B-Test.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q5_K_M.gguf) | Q5_K_M | 0.85GB |
| [Cinder-1.3B-Test.Q5_1.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q5_1.gguf) | Q5_1 | 0.9GB |
| [Cinder-1.3B-Test.Q6_K.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q6_K.gguf) | Q6_K | 0.98GB |
| [Cinder-1.3B-Test.Q8_0.gguf](https://huggingface.co/RichardErkhov/Josephgflowers_-_Cinder-1.3B-Test-gguf/blob/main/Cinder-1.3B-Test.Q8_0.gguf) | Q8_0 | 1.26GB |
Original model description:
---
license: mit
library_name: transformers
base_model: Josephgflowers/TinyLlama-3T-Cinder-v1.2
pipeline_tag: text-generation
widget:
- text: '<|system|>
You are a helpful assistant</s>
<|user|>
Tell me about yourself, what is your name?.</s>
<|assistant|>'
model-index:
- name: Cinder-1.3B-Test
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 33.19
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 55.48
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 26.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 36.62
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 58.96
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 2.81
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/Cinder-1.3B-Test
name: Open LLM Leaderboard
---
# 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]
- **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]
### Framework versions
- PEFT 0.7.1
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__Cinder-1.3B-Test)
| Metric |Value|
|---------------------------------|----:|
|Avg. |35.57|
|AI2 Reasoning Challenge (25-Shot)|33.19|
|HellaSwag (10-Shot) |55.48|
|MMLU (5-Shot) |26.37|
|TruthfulQA (0-shot) |36.62|
|Winogrande (5-shot) |58.96|
|GSM8k (5-shot) | 2.81|
|
yeen214/test_llama2_7b | yeen214 | "2023-10-01T02:38:15Z" | 2,911 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-30T08:45:19Z" | Entry not found |
TheBloke/orca_mini_v3_7B-GPTQ | TheBloke | "2023-09-27T12:45:35Z" | 2,910 | 11 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:psmathur/orca_mini_v1_dataset",
"dataset:ehartford/dolphin",
"arxiv:2306.02707",
"base_model:psmathur/orca_mini_v3_7b",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | "2023-08-10T10:12:19Z" | ---
language:
- en
license: other
library_name: transformers
datasets:
- psmathur/orca_mini_v1_dataset
- ehartford/dolphin
model_name: Orca Mini v3 7B
base_model: psmathur/orca_mini_v3_7b
inference: false
model_creator: Pankaj Mathur
model_type: llama
pipeline_tag: text-generation
prompt_template: '### System:
You are an AI assistant that follows instruction extremely well. Help as much as
you can.
### User:
{prompt}
### Input:
{input}
### Response:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Orca Mini v3 7B - GPTQ
- Model creator: [Pankaj Mathur](https://huggingface.co/psmathur)
- Original model: [Orca Mini v3 7B](https://huggingface.co/psmathur/orca_mini_v3_7b)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Pankaj Mathur's Orca Mini v3 7B](https://huggingface.co/psmathur/orca_mini_v3_7b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/orca_mini_v3_7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_v3_7B-GGUF)
* [Pankaj Mathur's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_v3_7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: orca_mini
```
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
{prompt}
### Input:
{input}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Pankaj Mathur's Orca Mini v3 7B](https://huggingface.co/psmathur/orca_mini_v3_7b).
<!-- licensing end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 GB | Yes | 4-bit, without Act Order and group size 128g. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.02 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.31 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/orca_mini_v3_7B-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/orca_mini_v3_7B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/orca_mini_v3_7B-GPTQ:main`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `orca_mini_v3_7B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
### For CodeLlama models only: you must use Transformers 4.33.0 or later.
If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/orca_mini_v3_7B-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
{prompt}
### Input:
{input}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjรคreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, ์ค๊ต ๊น, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, ้ฟๆ, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Pankaj Mathur's Orca Mini v3 7B
# orca_mini_v3_7b
A LLama2-7b model trained on Orca Style datasets.
<br>
<br>
๐ค How good is orca-mini-v3-7b? Do the evaluation results from HuggingFace Open LLM leaderboard translate to real-world use cases?
๐ Now you can figure it out for yourself!
Introducing the orca-mini chatbot powered by the orca-mini-v3-7b model. Dive in and see how the open source 7b model stacks up in the world of massive language models. ๐
โฐ Hurry up before I run out of GPU credits! ๐
Check it out here ๐
[https://huggingface.co/spaces/psmathur/psmathur-orca_mini_v3_7b](https://huggingface.co/spaces/psmathur/psmathur-orca_mini_v3_7b)
<br>
**P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.**
<br>
### quantized versions
Big thanks to [@TheBloke](https://huggingface.co/TheBloke)
1) https://huggingface.co/TheBloke/orca_mini_v3_7B-GGML
2) https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ
<br>
#### license disclaimer:
This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.
<br>
## evaluation
We evaluated orca_mini_v3_7b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|||||
|:------:|:--------:|:-------:|:--------:|
|**Task**|**Metric**|**Value**|**Stderr**|
|*arc_challenge*|acc_norm|0.5717|0.0145|
|*hellaswag*|acc_norm|0.7966|0.0043|
|*mmlu*|acc_norm|0.5234|0.035|
|*truthfulqa_mc*|mc2|0.5029|0.0156|
|**Total Average**|-|**0.59865**||
<br>
## example esage
Here is prompt format
```
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
Tell me about Orcas.
### Assistant:
```
Below shows a code example on how to use this model
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_7b", use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
"psmathur/orca_mini_v3_7b",
torch_dtype=torch.float16,
load_in_8bit=True,
low_cpu_mem_usage=True,
device_map="auto"
)
system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"
#generate text steps
instruction = "Tell me about Orcas."
prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=4096)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
<br>
#### limitations & biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary.
<br>
### citiation:
Please kindly cite using the following BibTeX:
```
@misc{orca_mini_v3_7b,
author = {Pankaj Mathur},
title = {orca_mini_v3_7b: An explain tuned Llama2-7b model},
year = {2023},
publisher = {GitHub, HuggingFace},
journal = {GitHub repository, HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_7b},
}
```
```
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@software{touvron2023llama,
title={LLaMA2: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
|
yeen214/llama2_7b_small_tuning_v1 | yeen214 | "2023-10-03T06:53:33Z" | 2,910 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-02T06:48:50Z" | Entry not found |
yeen214/test_llama2_ko_7b | yeen214 | "2023-10-02T20:05:55Z" | 2,909 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-02T03:55:19Z" | Entry not found |
RichardErkhov/allenai_-_OLMo-1B-hf-gguf | RichardErkhov | "2024-06-22T19:03:55Z" | 2,909 | 0 | null | [
"gguf",
"arxiv:2402.00838",
"arxiv:2302.13971",
"region:us"
] | null | "2024-06-22T18:47:45Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
OLMo-1B-hf - GGUF
- Model creator: https://huggingface.co/allenai/
- Original model: https://huggingface.co/allenai/OLMo-1B-hf/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [OLMo-1B-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q2_K.gguf) | Q2_K | 0.45GB |
| [OLMo-1B-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.IQ3_XS.gguf) | IQ3_XS | 0.49GB |
| [OLMo-1B-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.IQ3_S.gguf) | IQ3_S | 0.51GB |
| [OLMo-1B-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q3_K_S.gguf) | Q3_K_S | 0.51GB |
| [OLMo-1B-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.IQ3_M.gguf) | IQ3_M | 0.53GB |
| [OLMo-1B-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q3_K.gguf) | Q3_K | 0.56GB |
| [OLMo-1B-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q3_K_M.gguf) | Q3_K_M | 0.56GB |
| [OLMo-1B-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q3_K_L.gguf) | Q3_K_L | 0.61GB |
| [OLMo-1B-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.IQ4_XS.gguf) | IQ4_XS | 0.62GB |
| [OLMo-1B-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q4_0.gguf) | Q4_0 | 0.64GB |
| [OLMo-1B-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.IQ4_NL.gguf) | IQ4_NL | 0.65GB |
| [OLMo-1B-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q4_K_S.gguf) | Q4_K_S | 0.65GB |
| [OLMo-1B-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q4_K.gguf) | Q4_K | 0.68GB |
| [OLMo-1B-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q4_K_M.gguf) | Q4_K_M | 0.68GB |
| [OLMo-1B-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q4_1.gguf) | Q4_1 | 0.71GB |
| [OLMo-1B-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q5_0.gguf) | Q5_0 | 0.77GB |
| [OLMo-1B-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q5_K_S.gguf) | Q5_K_S | 0.77GB |
| [OLMo-1B-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q5_K.gguf) | Q5_K | 0.79GB |
| [OLMo-1B-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q5_K_M.gguf) | Q5_K_M | 0.79GB |
| [OLMo-1B-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q5_1.gguf) | Q5_1 | 0.83GB |
| [OLMo-1B-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q6_K.gguf) | Q6_K | 0.9GB |
| [OLMo-1B-hf.Q8_0.gguf](https://huggingface.co/RichardErkhov/allenai_-_OLMo-1B-hf-gguf/blob/main/OLMo-1B-hf.Q8_0.gguf) | Q8_0 | 1.17GB |
Original model description:
---
license: apache-2.0
datasets:
- allenai/dolma
language:
- en
---
<img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for OLMo 1B
<!-- Provide a quick summary of what the model is/does. -->
OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models.
The OLMo models are trained on the [Dolma](https://huggingface.co/datasets/allenai/dolma) dataset.
We release all code, checkpoints, logs (coming soon), and details involved in training these models.
This model has been converted from [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) for the
Hugging Face Transformers format.
## Model Details
The core models released in this batch are the following:
| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
|------|--------|---------|-------------|-----------------|----------------|
| [OLMo 1B](https://huggingface.co/allenai/OLMo-1B-hf) | 3 Trillion |16 | 2048 | 16 | 2048 |
| [OLMo 7B](https://huggingface.co/allenai/OLMo-7B-hf) | 2.5 Trillion | 32 | 4096 | 32 | 2048 |
| [OLMo 7B Twin 2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T-hf) | 2 Trillion | 32 | 4096 | 32 | 2048 |
We are releasing many checkpoints for these models, for every 1000 training steps. These have not
yet been converted into Hugging Face Transformers format, but are available in [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B).
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Allen Institute for AI (AI2)
- **Supported by:** Databricks, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, AMD, CSC (Lumi Supercomputer), UW
- **Model type:** a Transformer style autoregressive language model.
- **Language(s) (NLP):** English
- **License:** The code and model are released under Apache 2.0.
- **Contact:** Technical inquiries: `olmo at allenai dot org`. Press: `press at allenai dot org`
- **Date cutoff:** Feb./March 2023 based on Dolma dataset version.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Project Page:** https://allenai.org/olmo
- **Repositories:**
- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
- Evaluation code: https://github.com/allenai/OLMo-Eval
- Further fine-tuning code: https://github.com/allenai/open-instruct
- **Paper:** [Link](https://arxiv.org/abs/2402.00838)
- **Technical blog post:** https://blog.allenai.org/olmo-open-language-model-87ccfc95f580
- **W&B Logs:** https://wandb.ai/ai2-llm/OLMo-1B/reports/OLMo-1B--Vmlldzo2NzY1Njk1
<!-- - **Press release:** TODO -->
## 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. -->
### Inference
Quickly get inference running with the following:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-hf")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf")
message = ["Language modeling is "]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> 'Language modeling is the first step to build natural language generation...'
```
Alternatively, with the pipeline abstraction:
```python
from transformers import pipeline
olmo_pipe = pipeline("text-generation", model="allenai/OLMo-1B-hf")
print(olmo_pipe("Language modeling is "))
>> 'Language modeling is a branch of natural language processing that aims to...'
```
Or, you can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-hf", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`).
The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as `inputs.input_ids.to('cuda')` to avoid potential issues.
### Fine-tuning
This model does not directly support our fine-tuning processes. Model fine-tuning can be done
from the final checkpoint or many intermediate checkpoints of
[allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B).
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Core model results for the 7B model are found below.
| | [Llama 7B](https://arxiv.org/abs/2302.13971) | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | [MPT 7B](https://huggingface.co/mosaicml/mpt-7b) | **OLMo 7B** (ours) |
| --------------------------------- | -------- | ---------- | --------- | ------ | ------- |
| arc_challenge | 44.5 | 39.8 | 47.5 | 46.5 | 48.5 |
| arc_easy | 57.0 | 57.7 | 70.4 | 70.5 | 65.4 |
| boolq | 73.1 | 73.5 | 74.6 | 74.2 | 73.4 |
| copa | 85.0 | 87.0 | 86.0 | 85.0 | 90 |
| hellaswag | 74.5 | 74.5 | 75.9 | 77.6 | 76.4 |
| openbookqa | 49.8 | 48.4 | 53.0 | 48.6 | 50.2 |
| piqa | 76.3 | 76.4 | 78.5 | 77.3 | 78.4 |
| sciq | 89.5 | 90.8 | 93.9 | 93.7 | 93.8 |
| winogrande | 68.2 | 67.3 | 68.9 | 69.9 | 67.9 |
| **Core tasks average** | 68.7 | 68.4 | 72.1 | 71.5 | 71.6 |
| truthfulQA (MC2) | 33.9 | 38.5 | 34.0 | 33 | 36.0 |
| MMLU (5 shot MC) | 31.5 | 45.0 | 24.0 | 30.8 | 28.3 |
| GSM8k (mixed eval.) | 10.0 (8shot CoT) | 12.0 (8shot CoT) | 4.0 (5 shot) | 4.5 (5 shot) | 8.5 (8shot CoT) |
| **Full average** | 57.8 | 59.3 | 59.2 | 59.3 | 59.8 |
And for the 1B model:
| task | random | [StableLM 2 1.6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)\* | [Pythia 1B](https://huggingface.co/EleutherAI/pythia-1b) | [TinyLlama 1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) | **OLMo 1B** (ours) |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------ | ----------------- | --------- | -------------------------------------- | ------- |
| arc_challenge | 25 | 43.81 | 33.11 | 34.78 | 34.45 |
| arc_easy | 25 | 63.68 | 50.18 | 53.16 | 58.07 |
| boolq | 50 | 76.6 | 61.8 | 64.6 | 60.7 |
| copa | 50 | 84 | 72 | 78 | 79 |
| hellaswag | 25 | 68.2 | 44.7 | 58.7 | 62.5 |
| openbookqa | 25 | 45.8 | 37.8 | 43.6 | 46.4 |
| piqa | 50 | 74 | 69.1 | 71.1 | 73.7 |
| sciq | 25 | 94.7 | 86 | 90.5 | 88.1 |
| winogrande | 50 | 64.9 | 53.3 | 58.9 | 58.9 |
| Average | 36.11 | 68.41 | 56.44 | 61.48 | 62.42 |
\*Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging.
## Model Details
### Data
For training data details, please see the [Dolma](https://huggingface.co/datasets/allenai/dolma) documentation.
### Architecture
OLMo 7B architecture with peer models for comparison.
| | **OLMo 7B** | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | PaLM 8B |
|------------------------|-------------------|---------------------|--------------------|--------------------|------------------|
| d_model | 4096 | 4096 | 4096 | 4544 | 4096 |
| num heads | 32 | 32 | 32 | 71 | 16 |
| num layers | 32 | 32 | 32 | 32 | 32 |
| MLP ratio | ~8/3 | ~8/3 | ~8/3 | 4 | 4 |
| LayerNorm type | non-parametric LN | RMSNorm | parametric LN | parametric LN | parametric LN |
| pos embeddings | RoPE | RoPE | RoPE | RoPE | RoPE |
| attention variant | full | GQA | full | MQA | MQA |
| biases | none | none | in LN only | in LN only | none |
| block type | sequential | sequential | sequential | parallel | parallel |
| activation | SwiGLU | SwiGLU | SwiGLU | GeLU | SwiGLU |
| sequence length | 2048 | 4096 | 2048 | 2048 | 2048 |
| batch size (instances) | 2160 | 1024 | 2048 | 2304 | 512 |
| batch size (tokens) | ~4M | ~4M | ~4M | ~4M | ~1M |
| weight tying | no | no | no | no | yes |
### Hyperparameters
AdamW optimizer parameters are shown below.
| Size | Peak LR | Betas | Epsilon | Weight Decay |
|------|------------|-----------------|-------------|--------------|
| 1B | 4.0E-4 | (0.9, 0.95) | 1.0E-5 | 0.1 |
| 7B | 3.0E-4 | (0.9, 0.99) | 1.0E-5 | 0.1 |
Optimizer settings comparison with peer models.
| | **OLMo 7B** | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) |
|-----------------------|------------------|---------------------|--------------------|--------------------|
| warmup steps | 5000 | 2000 | 2000 | 1000 |
| peak LR | 3.0E-04 | 3.0E-04 | 3.0E-04 | 6.0E-04 |
| minimum LR | 3.0E-05 | 3.0E-05 | 3.0E-05 | 1.2E-05 |
| weight decay | 0.1 | 0.1 | 0.1 | 0.1 |
| beta1 | 0.9 | 0.9 | 0.9 | 0.99 |
| beta2 | 0.95 | 0.95 | 0.95 | 0.999 |
| epsilon | 1.0E-05 | 1.0E-05 | 1.0E-05 | 1.0E-05 |
| LR schedule | linear | cosine | cosine | cosine |
| gradient clipping | global 1.0 | global 1.0 | global 1.0 | global 1.0 |
| gradient reduce dtype | FP32 | FP32 | FP32 | BF16 |
| optimizer state dtype | FP32 | most likely FP32 | FP32 | FP32 |
## Environmental Impact
OLMo 7B variants were either trained on MI250X GPUs at the LUMI supercomputer, or A100-40GB GPUs provided by MosaicML.
A summary of the environmental impact. Further details are available in the paper.
| | GPU Type | Power Consumption From GPUs | Carbon Intensity (kg COโe/KWh) | Carbon Emissions (tCOโeq) |
|-----------|------------|-----------------------------|--------------------------------|---------------------------|
| OLMo 7B Twin | MI250X ([LUMI supercomputer](https://www.lumi-supercomputer.eu)) | 135 MWh | 0* | 0* |
| OLMo 7B | A100-40GB ([MosaicML](https://www.mosaicml.com)) | 104 MWh | 0.656 | 75.05 |
## Bias, Risks, and Limitations
Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content.
Such content can also be produced unintentionally, especially in the case of bias, so we recommend users consider the risks of applications of this technology.
Otherwise, many facts from OLMo or any LLM will often not be true, so they should be checked.
## Citation
**BibTeX:**
```
@article{Groeneveld2023OLMo,
title={OLMo: Accelerating the Science of Language Models},
author={Groeneveld, Dirk and Beltagy, Iz and Walsh, Pete and Bhagia, Akshita and Kinney, Rodney and Tafjord, Oyvind and Jha, Ananya Harsh and Ivison, Hamish and Magnusson, Ian and Wang, Yizhong and Arora, Shane and Atkinson, David and Authur, Russell and Chandu, Khyathi and Cohan, Arman and Dumas, Jennifer and Elazar, Yanai and Gu, Yuling and Hessel, Jack and Khot, Tushar and Merrill, William and Morrison, Jacob and Muennighoff, Niklas and Naik, Aakanksha and Nam, Crystal and Peters, Matthew E. and Pyatkin, Valentina and Ravichander, Abhilasha and Schwenk, Dustin and Shah, Saurabh and Smith, Will and Subramani, Nishant and Wortsman, Mitchell and Dasigi, Pradeep and Lambert, Nathan and Richardson, Kyle and Dodge, Jesse and Lo, Kyle and Soldaini, Luca and Smith, Noah A. and Hajishirzi, Hannaneh},
journal={Preprint},
year={2024}
}
```
**APA:**
Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Dodge, J., Lo, K., Soldaini, L., Smith, N., & Hajishirzi, H. (2024). OLMo: Accelerating the Science of Language Models. Preprint.
## Model Card Contact
For errors in this model card, contact Nathan, Akshita or Shane, `{nathanl, akshitab, shanea} at allenai dot org`.
|
Yntec/GimmeDatDing | Yntec | "2023-12-09T19:01:46Z" | 2,908 | 5 | diffusers | [
"diffusers",
"safetensors",
"Anime",
"Cartoons",
"Cute",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-12-08T07:31:09Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
- Cartoons
- Cute
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# GimmeDatDing
Samples and prompt:
highquality, masterpiece, 1girl, Chi-Chi, :D, close up, smile, arms up, pink helmet, black hair, black eyes, blush, white teeth, bikini armor, aqua cape, pink gloves, pink boots, cleavage. cave, rock, mountain. blue collar |
TheBloke/Llama-2-13B-GGUF | TheBloke | "2023-09-27T12:47:13Z" | 2,907 | 60 | transformers | [
"transformers",
"gguf",
"llama",
"facebook",
"meta",
"pytorch",
"llama-2",
"text-generation",
"en",
"arxiv:2307.09288",
"base_model:meta-llama/Llama-2-13b-hf",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-04T17:32:17Z" | ---
language:
- en
license: llama2
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
model_name: Llama 2 13B
base_model: meta-llama/Llama-2-13b-hf
inference: false
model_creator: Meta
model_type: llama
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama 2 13B - GGUF
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Meta's Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-GGUF)
* [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13b-hf)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-2-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-2-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [llama-2-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [llama-2-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [llama-2-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-2-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [llama-2-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [llama-2-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-2-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [llama-2-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [llama-2-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [llama-2-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/blob/main/llama-2-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Llama-2-13B-GGUF and below it, a specific filename to download, such as: llama-2-13b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Llama-2-13B-GGUF llama-2-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Llama-2-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Llama-2-13B-GGUF llama-2-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m llama-2-13b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-13B-GGUF", model_file="llama-2-13b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
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## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjรคreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, ์ค๊ต ๊น, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, ้ฟๆ, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
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<!-- original-model-card start -->
# Original model card: Meta's Llama 2 13B
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes โ 7B, 13B, and 70B โ as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Metaโs sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2โs potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software โbug,โ or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
<!-- original-model-card end -->
|
TioPanda/pandev-complete | TioPanda | "2024-06-26T07:02:12Z" | 2,906 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-26T06:33:47Z" | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** TioPanda
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AIARTCHAN/MIX-Pro-V4 | AIARTCHAN | "2023-04-06T02:07:28Z" | 2,905 | 39 | diffusers | [
"diffusers",
"stable-diffusion",
"aiartchan",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-04-06T01:49:01Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- aiartchan
---
# MIX-Pro-V4
[์๋ณธ๊ธ](https://arca.live/b/aiart/73277342)
[huggingface](https://huggingface.co/GIMG/AIChan_Model/tree/main/Blend/MIX-Pro/V4)
[civitai](https://civitai.com/models/7241)
# Download
- [original 4.27GB](https://huggingface.co/GIMG/AIChan_Model/resolve/main/Blend/MIX-Pro/V4/MIX-Pro-V4.safetensors)
- [fp16](https://huggingface.co/AIARTCHAN/MIX-Pro-V4/resolve/main/MIX-Pro-V4-fp16.safetensors)
## License
creativeml-openrail-m
+
- No selling images
- No generation services
- No selling models
## Parameters
https://huggingface.co/GIMG/AIChan_Model/tree/main/Blend/MIX-Pro/V4/Parameters
## Source
https://huggingface.co/andite/mikapikazo-diffusion/blob/main/mikapikazo-40000.ckpt
https://huggingface.co/andite/cutesexyrobutts-diffusion/blob/main/csrb-diffusion.ckpt
https://huggingface.co/andite/piromizu-diffusion/blob/main/piromizu-20000.ckpt
https://huggingface.co/andite/yohan-diffusion/blob/main/yohan-diffusion.safetensors
https://huggingface.co/nuigurumi/basil_mix/blob/main/Basil%20mix.safetensors
https://civitai.com/models/22607/loconlora-airconditioner-style
https://civitai.com/models/14393/thick-coat-cg-style
https://huggingface.co/closertodeath/mouseymix/blob/main/mouseymix.safetensors
https://huggingface.co/andite/pastel-mix/blob/main/pastelmix-fp16.safetensors
|
TheBloke/Mistral-7B-Claude-Chat-GGUF | TheBloke | "2023-10-28T23:02:14Z" | 2,905 | 18 | transformers | [
"transformers",
"gguf",
"mistral",
"dataset:Norquinal/claude_multiround_chat_1k",
"base_model:Norquinal/Mistral-7B-claude-chat",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | null | "2023-10-28T22:57:56Z" | ---
base_model: Norquinal/Mistral-7B-claude-chat
datasets:
- Norquinal/claude_multiround_chat_1k
inference: false
license: cc-by-nc-4.0
model_creator: Andy B. Norquinal
model_name: Mistral 7B Claude Chat
model_type: mistral
prompt_template: '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: {prompt} ASSISTANT:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mistral 7B Claude Chat - GGUF
- Model creator: [Andy B. Norquinal](https://huggingface.co/Norquinal)
- Original model: [Mistral 7B Claude Chat](https://huggingface.co/Norquinal/Mistral-7B-claude-chat)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Andy B. Norquinal's Mistral 7B Claude Chat](https://huggingface.co/Norquinal/Mistral-7B-claude-chat).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF)
* [Andy B. Norquinal's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Norquinal/Mistral-7B-claude-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Vicuna
```
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: {prompt} ASSISTANT:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-7b-claude-chat.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral-7b-claude-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistral-7b-claude-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral-7b-claude-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral-7b-claude-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistral-7b-claude-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral-7b-claude-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral-7b-claude-chat.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral-7b-claude-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral-7b-claude-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral-7b-claude-chat.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral-7b-claude-chat.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Claude-Chat-GGUF/blob/main/mistral-7b-claude-chat.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-Claude-Chat-GGUF and below it, a specific filename to download, such as: mistral-7b-claude-chat.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Mistral-7B-Claude-Chat-GGUF mistral-7b-claude-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Mistral-7B-Claude-Chat-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Claude-Chat-GGUF mistral-7b-claude-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m mistral-7b-claude-chat.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "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: {prompt} ASSISTANT:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Claude-Chat-GGUF", model_file="mistral-7b-claude-chat.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, ้ฟๆ, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjรคreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Andy B. Norquinal's Mistral 7B Claude Chat
This is the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) model fine-tuned using QLoRA (4-bit precision) on my [claude_multiround_chat_1k](https://huggingface.co/datasets/Norquinal/claude_multiround_chat_1k) dataset, which is a randomized subset of ~1000 samples from my [claude_multiround_chat_30k](https://huggingface.co/datasets/Norquinal/claude_multiround_chat_30k) dataset.
## Prompt Format
The model was finetuned with the Vicuna 1.1 prompt format:
```
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: {instruction}
ASSISTANT:
```
## Use in Text Generation Web UI
Install the bleeding-edge version of `transformers` from source:
```
pip install git+https://github.com/huggingface/transformers
```
Or, alternatively, change `model_type` in `config.json` from `mistral` to `llama`.
## Example Usage
>USER: First, give a brief overview of Chinese cuisine, including its regional diversity. next, describe the importance of the five flavors in Chinese cooking. Then, mention three popular Chinese dishes and their main ingredients. After that, provide a popular Chinese cooking technique and an example of a dish prepared using that technique. Finally, discuss the role of tea in traditional Chinese culture.
>ASSISTANT: Chinese cuisine is incredibly diverse due to its large geographic area with different climates and resources available. Regional specialties include Sichuan spicy hot pot, Cantonese dim sum and seafood dishes, Beijing roast duck, and Hunan stir-fries.
>
>The five basic tastes that guide Chinese cookery are sweetness, sourness, bitterness, saltiness, and pungency (spiciness). These contrasting flavors are used together to create balance in each dish.
>
>Some classic Chinese dishes include dumplings filled with meat or vegetables, spring rolls stuffed with vegetables and meat wrapped in thin pastry, and noodles served in soups or stir fried with sauces and vegetables.
>
>A common Chinese cooking technique is braising, where food is slowly cooked in a small amount of liquid until tender and flavorful. An example would be beef brisket braised in soy sauce, ginger and star anise for hours until falling apart.
>
>Tea plays a central role in Chinese culture as it represents hospitality, relaxation and socializing. Green teas are most commonly consumed but black, oolong and white varieties also exist. Tea drinking rituals involve preparing and serving tea in specific ways depending on context and occasion.
>
>In summary, Chinese cuisine offers a rich culinary tradition influenced by regional differences, an emphasis on balancing the five basic flavors, and a focus on simple yet flavorful preparations using fresh seasonal produce and high quality meats. Tea acts as both a refreshment and symbol of cultural refinement within Chinese society. I hope this overview provides an interesting starting point for further exploration into the fascinating world of Chinese gastronomy!
<!-- original-model-card end -->
|
mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF | mradermacher | "2024-06-06T21:47:57Z" | 2,905 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"mistral",
"text-generation",
"roleplay",
"en",
"base_model:FallenMerick/Chewy-Lemon-Cookie-11B",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-06T15:50:32Z" | ---
base_model: FallenMerick/Chewy-Lemon-Cookie-11B
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- mistral
- text-generation
- roleplay
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/FallenMerick/Chewy-Lemon-Cookie-11B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-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/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF/resolve/main/Chewy-Lemon-Cookie-11B.i1-Q6_K.gguf) | i1-Q6_K | 8.9 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
cyberagent/calm2-7b | cyberagent | "2023-11-02T05:46:18Z" | 2,904 | 27 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"japanese",
"causal-lm",
"ja",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-01T07:24:59Z" | ---
license: apache-2.0
language:
- ja
- en
tags:
- japanese
- causal-lm
inference: false
---
# CyberAgentLM2-7B (CALM2-7B)
## Model Description
CyberAgentLM2 is a decoder-only language model pre-trained on the 1.3T tokens of publicly available Japanese and English datasets.
Variant: [CyberAgentLM2-Chat](https://huggingface.co/cyberagent/calm2-7b-chat)
## Requirements
- transformers >= 4.34.1
- accelerate
## Usage
```python
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
assert transformers.__version__ >= "4.34.1"
model = AutoModelForCausalLM.from_pretrained("cyberagent/calm2-7b", device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("cyberagent/calm2-7b")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "AIใซใใฃใฆ็ง้ใฎๆฎใใใฏใ"
token_ids = tokenizer.encode(prompt, return_tensors="pt")
output_ids = model.generate(
input_ids=token_ids.to(model.device),
max_new_tokens=100,
do_sample=True,
temperature=0.9,
streamer=streamer,
)
```
## Model Details
* **Model size**: 7B
* **Trained tokens**: 1.3T tokens
* **Context length**: 4096
* **Model type**: Transformer-based Language Model
* **Language(s)**: Japanese, English
* **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/)
* **License**: Apache-2.0
## Author
[Ryosuke Ishigami](https://huggingface.co/rishigami)
## Citations
```tex
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` |
sapienzanlp/Minerva-3B-base-v1.0 | sapienzanlp | "2024-06-11T16:03:01Z" | 2,904 | 48 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gguf",
"mistral",
"text-generation",
"pretrained",
"it",
"en",
"dataset:uonlp/CulturaX",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-04-19T13:30:21Z" | ---
license: apache-2.0
pipeline_tag: text-generation
language:
- it
- en
tags:
- pretrained
datasets:
- uonlp/CulturaX
inference:
parameters:
temperature: 0.5
do_sample: True
widget:
- text: 'La capitale dell''Italia รจ '
example_title: Example 1
- text: 'Nel mezzo del cammin di nostra vita '
example_title: Example 2
- text: 'Una cena senza vino รจ come '
example_title: Example 3
---
<div style="text-align: center; display: flex; flex-direction: column; align-items: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/5f0b462819cb630495b814d7/DVA4MnFUs3UHBnTrX9jG6.png" style="max-width: 550px; height: auto;">
</div>
# Model Card for Minerva-3B-base-v1.0
Minerva is the first family of **LLMs pretrained from scratch on Italian** developed by [Sapienza NLP](https://nlp.uniroma1.it)
in collaboration with [Future Artificial Intelligence Research (FAIR)](https://fondazione-fair.it/) and [CINECA](https://www.cineca.it/).
Notably, the Minerva models are truly-open (data and model) Italian-English LLMs, with approximately half of the pretraining data
including Italian text.
* [Minerva LLMs - website](https://nlp.uniroma1.it/minerva/)
## Description
This is the model card for **Minerva-3B-base-v1.0**, a 3 billion parameter model trained on 660 billion tokens (330 billion in Italian, 330 billion in English).
This model is part of the Minerva LLM family:
* [Minerva-350M-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-350M-base-v1.0)
* [Minerva-1B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-1B-base-v1.0)
* [Minerva-3B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0)
## ๐จโ ๏ธ๐จ Bias, Risks, and Limitations ๐จโ ๏ธ๐จ
*This section identifies foreseeable harms and misunderstandings.*
This is a foundation model, not subject to alignment. Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Racist and sexist content
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information or historical facts as if it were factual
- Generate irrelevant or repetitive outputs
We are aware of the biases and potential problematic/toxic content that current pretrained large language models exhibit: more specifically, as probabilistic models of (Italian and English) languages, they reflect and amplify the biases of their training data.
For more information about this issue, please refer to our survey:
* [Biases in Large Language Models: Origins, Inventory, and Discussion](https://dl.acm.org/doi/full/10.1145/3597307)
## How to use Minerva with Hugging Face transformers
```python
import transformers
import torch
model_id = "sapienzanlp/Minerva-3B-base-v1.0"
# Initialize the pipeline.
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
# Input text for the model.
input_text = "La capitale dell'Italia รจ"
# Compute the outputs.
output = pipeline(
input_text,
max_new_tokens=128,
)
# Output:
# [{'generated_text': "La capitale dell'Italia รจ la cittร di Roma, che si trova a [...]"}]
```
## Model Architecture
Minerva-3B-base-v1.0 is a Transformer model based on the Mistral architecture, where the number of layers, number of heads, and the hidden states dimension are modified to reach 3B parameters.
Please, take a look at the configuration file for a detailed breakdown of the hyperparameters we chose for this model.
The Minerva LLM family is composed of:
| Model Name | Tokens | Layers | Hidden Size | Attention Heads | KV Heads | Sliding Window | Max Context Length |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Minerva-350M-base-v1.0 | 70B (35B it + 35B en) | 16 | 1152 | 16 | 4 | 2048 | 16384 |
| Minerva-1B-base-v1.0 | 200B (100B it + 100B en) | 16 | 2048 | 16 | 4 | 2048 | 16384 |
| **Minerva-3B-base-v1.0** | 660B (330B it + 330B en) | 32 | 2560 | 32 | 8 | 2048 | 16384 |
## Model Training
Minerva-3B-base-v1.0 was trained using [llm-foundry 0.6.0](https://github.com/mosaicml/llm-foundry) from [MosaicML](https://mosaicml.com/). The hyperparameters used are the following:
| Model Name | Optimizer | lr | betas | eps | weight decay | Scheduler | Warmup Steps | Batch Size (Tokens) | Total Steps |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Minerva-350M-base-v1.0 | Decoupled AdamW | 2e-4 | (0.9, 0.95) | 1e-8 | 0.0 | Cosine | 2% | 4M | 16,690 |
| Minerva-1B-base-v1.0 | Decoupled AdamW | 2e-4 | (0.9, 0.95) | 1e-8 | 0.0 | Cosine | 2% | 4M | 47,684 |
| **Minerva-3B-base-v1.0** | Decoupled AdamW | 2e-4 | (0.9, 0.95) | 1e-8 | 0.0 | Cosine | 2% | 4M | 157,357 |
## Model Evaluation
We assessed our model using the [LM-Evaluation-Harness](https://github.com/EleutherAI/lm-evaluation-harness) library, which serves as a comprehensive framework for testing generative language models across a wide range of evaluation tasks.
All the reported benchmark data was already present in the LM-Evaluation-Harness suite.
**Italian** Data:
| Task | Accuracy |
| --- | --- |
| [xcopa](https://huggingface.co/datasets/xcopa) (0-shot) | 0.694 |
| [Hellaswag](https://huggingface.co/datasets/alexandrainst/m_hellaswag) (5-shot) | 0.5293 |
| [Belebele](https://huggingface.co/datasets/facebook/belebele) (5-shot) | 0.2333 |
| [TruthfulQA MC 1](https://huggingface.co/datasets/alexandrainst/m_truthfulqa) (0-shot) | 0.2363 |
| [TruthfulQA MC 2](https://huggingface.co/datasets/alexandrainst/m_truthfulqa) (0-shot) | 0.3731 |
| [M MMLU](https://huggingface.co/datasets/alexandrainst/m_mmlu) (5-shot) | 0.2612 |
| [arc challenge](https://huggingface.co/datasets/alexandrainst/m_arc) (5-shot) | 0.3268 |
**English** Data:
| Task | Accuracy |
| --- | --- |
| [Hellaswag](https://huggingface.co/datasets/Rowan/hellaswag) (5-shot) | 0.6168 |
| [piqa](https://huggingface.co/datasets/piqa) (5-shot) | 0.7535 |
| [sciq](https://huggingface.co/datasets/sciq) (5-shot) | 0.925 |
| [Belebele](https://huggingface.co/datasets/facebook/belebele) (5-shot) | 0.2278 |
| [TruthfulQA MC 1](https://huggingface.co/datasets/truthful_qa) (0-shot) | 0.2142 |
| [TruthfulQA MC 2](https://huggingface.co/datasets/truthful_qa) (0-shot) | 0.3643 |
| [M MMLU](https://huggingface.co/datasets/alexandrainst/m_mmlu) (5-shot) | 0.263 |
| [arc challenge](allenai/ai2_arc) (5-shot) | 0.3319 |
| [arc easy](allenai/ai2_arc) (5-shot) | 0.6540 |
## Training Data
Minerva-3B-base-v1.0 was trained on 330B Italian tokens and 330B English tokens sampled from CulturaX.
We have extracted some statistics on Italian (115B tokens) and English (210B tokens) documents from CulturaX on the selected sources:
*Proportion of number of tokens per domain (Italian)*
<img src="https://github.com/Andrew-Wyn/images/blob/master/minerva/top_25_url_tokens_proportion_culturax_it.png?raw=true" alt="italian-tok-counts" border="0" width="1800px">
*Proportion of number of tokens per domain (English)*
<img src="https://github.com/Andrew-Wyn/images/blob/master/minerva/top_25_url_tokens_proportion_culturax_en.png?raw=true" alt="english-tok-counts" border="0" width="1800px">
## Tokenizer Fertility
The tokenizer fertility measures the average amount of tokens produced per tokenized word.
A tokenizer displaying high fertility values in a particular language typically indicates that it segments words in that language extensively.
The tokenizer fertility is strictly correlated with the inference speed of the model with respect to a specific language,
as higher values mean longer sequences of tokens to generate and thus lower inference speed.
**Fertility computed over a sample of Cultura X (CX) data and Wikipedia (Wp):**
| Model | Voc. Size | Fertility IT (CX) | Fertility EN (CX) | Fertility IT (Wp) | Fertility EN (Wp) |
| --- | --- | --- |--- | --- |--- |
| Mistral-7B-v0.1 | 32000 | 1.87 | 1.32 | 2.05 | 1.57 |
| gemma-7b | 256000 | 1.42 | 1.18 | 1.56 | 1.34 |
| Minerva-3B-base-v1.0 | 32768 | 1.39 | 1.32 | 1.66 | 1.59 |
## Notice
Minerva-3B-base-v1.0 is a pretrained base model and, therefore, has no moderation mechanisms.
## The Sapienza NLP Team
* **Riccardo Orlando:** data preprocessing, model training
* **Pere-Lluis Huguet Cabot:** data preprocessing, vocabulary, evaluation
* **Luca Moroni:** data curation, data analysis, downstream tasks, evaluation
* **Simone Conia:** data curation, evaluation, project supervision
* **Edoardo Barba:** data preprocessing, downstream tasks, project supervision
* **Roberto Navigli:** project coordinator
### Special thanks for their support
* Giuseppe Fiameni, Nvidia
* Sergio Orlandini, CINECA
## Acknowledgments
This work was funded by the PNRR MUR project [PE0000013-FAIR](https://fondazione-fair.it).
We acknowledge the [CINECA](https://www.cineca.it) award "IscB_medit" under the ISCRA initiative, for the availability of high performance computing resources and support. |
scrapegraphai/scrapegraphcompanion_finetuning_GGUF | scrapegraphai | "2024-06-27T14:57:29Z" | 2,903 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-26T15:49:59Z" | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** vinci00
- **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)
|
FinancialSupport/saiga-7b | FinancialSupport | "2024-03-25T21:31:58Z" | 2,902 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"it",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-28T16:31:58Z" | ---
language:
- it
license: apache-2.0
model-index:
- name: saiga-7b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 63.14
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FinancialSupport/saiga-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 83.14
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FinancialSupport/saiga-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 61.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FinancialSupport/saiga-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 54.99
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FinancialSupport/saiga-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.01
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FinancialSupport/saiga-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 45.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FinancialSupport/saiga-7b
name: Open LLM Leaderboard
---
il saiga รจ uno strano incrocio di antilopi che vive nelle steppe siberiane.
Il nome deriva dal fatto che รจ un parente di fauno/camoscio e un lontano cugino di cerbero (altri modelli open source ita).
E' un progetto portato avanti nei weekend con pochi soldi/tempo a disposizione
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_FinancialSupport__saiga-7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |64.51|
|AI2 Reasoning Challenge (25-Shot)|63.14|
|HellaSwag (10-Shot) |83.14|
|MMLU (5-Shot) |61.66|
|TruthfulQA (0-shot) |54.99|
|Winogrande (5-shot) |79.01|
|GSM8k (5-shot) |45.11|
|
mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF | mradermacher | "2024-06-23T12:05:53Z" | 2,901 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:grimjim/Llama-3-Oasis-v1-OAS-8B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-05T05:35:44Z" | ---
base_model: grimjim/Llama-3-Oasis-v1-OAS-8B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
license_link: LICENSE
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/grimjim/Llama-3-Oasis-v1-OAS-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-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/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF/resolve/main/Llama-3-Oasis-v1-OAS-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 -->
|
Helsinki-NLP/opus-mt-ine-en | Helsinki-NLP | "2023-08-16T11:58:27Z" | 2,900 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ca",
"es",
"os",
"ro",
"fy",
"cy",
"sc",
"is",
"yi",
"lb",
"an",
"sq",
"fr",
"ht",
"rm",
"ps",
"af",
"uk",
"sl",
"lt",
"bg",
"be",
"gd",
"si",
"en",
"br",
"mk",
"or",
"mr",
"ru",
"fo",
"co",
"oc",
"pl",
"gl",
"nb",
"bn",
"id",
"hy",
"da",
"gv",
"nl",
"pt",
"hi",
"as",
"kw",
"ga",
"sv",
"gu",
"wa",
"lv",
"el",
"it",
"hr",
"ur",
"nn",
"de",
"cs",
"ine",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
language:
- ca
- es
- os
- ro
- fy
- cy
- sc
- is
- yi
- lb
- an
- sq
- fr
- ht
- rm
- ps
- af
- uk
- sl
- lt
- bg
- be
- gd
- si
- en
- br
- mk
- or
- mr
- ru
- fo
- co
- oc
- pl
- gl
- nb
- bn
- id
- hy
- da
- gv
- nl
- pt
- hi
- as
- kw
- ga
- sv
- gu
- wa
- lv
- el
- it
- hr
- ur
- nn
- de
- cs
- ine
tags:
- translation
license: apache-2.0
---
### ine-eng
* source group: Indo-European languages
* target group: English
* OPUS readme: [ine-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-eng/README.md)
* model: transformer
* source language(s): afr aln ang_Latn arg asm ast awa bel bel_Latn ben bho bos_Latn bre bul bul_Latn cat ces cor cos csb_Latn cym dan deu dsb egl ell enm_Latn ext fao fra frm_Latn frr fry gcf_Latn gla gle glg glv gom gos got_Goth grc_Grek gsw guj hat hif_Latn hin hrv hsb hye ind isl ita jdt_Cyrl ksh kur_Arab kur_Latn lad lad_Latn lat_Latn lav lij lit lld_Latn lmo ltg ltz mai mar max_Latn mfe min mkd mwl nds nld nno nob nob_Hebr non_Latn npi oci ori orv_Cyrl oss pan_Guru pap pdc pes pes_Latn pes_Thaa pms pnb pol por prg_Latn pus roh rom ron rue rus san_Deva scn sco sgs sin slv snd_Arab spa sqi srp_Cyrl srp_Latn stq swe swg tgk_Cyrl tly_Latn tmw_Latn ukr urd vec wln yid zlm_Latn zsm_Latn zza
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdev2014-hineng.hin.eng | 11.2 | 0.375 |
| newsdev2016-enro-roneng.ron.eng | 35.5 | 0.614 |
| newsdev2017-enlv-laveng.lav.eng | 25.1 | 0.542 |
| newsdev2019-engu-gujeng.guj.eng | 16.0 | 0.420 |
| newsdev2019-enlt-liteng.lit.eng | 24.0 | 0.522 |
| newsdiscussdev2015-enfr-fraeng.fra.eng | 30.1 | 0.550 |
| newsdiscusstest2015-enfr-fraeng.fra.eng | 33.4 | 0.572 |
| newssyscomb2009-ceseng.ces.eng | 24.0 | 0.520 |
| newssyscomb2009-deueng.deu.eng | 25.7 | 0.526 |
| newssyscomb2009-fraeng.fra.eng | 27.9 | 0.550 |
| newssyscomb2009-itaeng.ita.eng | 31.4 | 0.574 |
| newssyscomb2009-spaeng.spa.eng | 28.3 | 0.555 |
| news-test2008-deueng.deu.eng | 24.0 | 0.515 |
| news-test2008-fraeng.fra.eng | 24.5 | 0.524 |
| news-test2008-spaeng.spa.eng | 25.5 | 0.533 |
| newstest2009-ceseng.ces.eng | 23.3 | 0.516 |
| newstest2009-deueng.deu.eng | 23.2 | 0.512 |
| newstest2009-fraeng.fra.eng | 27.3 | 0.545 |
| newstest2009-itaeng.ita.eng | 30.3 | 0.567 |
| newstest2009-spaeng.spa.eng | 27.9 | 0.549 |
| newstest2010-ceseng.ces.eng | 23.8 | 0.523 |
| newstest2010-deueng.deu.eng | 26.2 | 0.545 |
| newstest2010-fraeng.fra.eng | 28.6 | 0.562 |
| newstest2010-spaeng.spa.eng | 31.4 | 0.581 |
| newstest2011-ceseng.ces.eng | 24.2 | 0.521 |
| newstest2011-deueng.deu.eng | 23.9 | 0.522 |
| newstest2011-fraeng.fra.eng | 29.5 | 0.570 |
| newstest2011-spaeng.spa.eng | 30.3 | 0.570 |
| newstest2012-ceseng.ces.eng | 23.5 | 0.516 |
| newstest2012-deueng.deu.eng | 24.9 | 0.529 |
| newstest2012-fraeng.fra.eng | 30.0 | 0.568 |
| newstest2012-ruseng.rus.eng | 29.9 | 0.565 |
| newstest2012-spaeng.spa.eng | 33.3 | 0.593 |
| newstest2013-ceseng.ces.eng | 25.6 | 0.531 |
| newstest2013-deueng.deu.eng | 27.7 | 0.545 |
| newstest2013-fraeng.fra.eng | 30.0 | 0.561 |
| newstest2013-ruseng.rus.eng | 24.4 | 0.514 |
| newstest2013-spaeng.spa.eng | 30.8 | 0.577 |
| newstest2014-csen-ceseng.ces.eng | 27.7 | 0.558 |
| newstest2014-deen-deueng.deu.eng | 27.7 | 0.545 |
| newstest2014-fren-fraeng.fra.eng | 32.2 | 0.592 |
| newstest2014-hien-hineng.hin.eng | 16.7 | 0.450 |
| newstest2014-ruen-ruseng.rus.eng | 27.2 | 0.552 |
| newstest2015-encs-ceseng.ces.eng | 25.4 | 0.518 |
| newstest2015-ende-deueng.deu.eng | 28.8 | 0.552 |
| newstest2015-enru-ruseng.rus.eng | 25.6 | 0.527 |
| newstest2016-encs-ceseng.ces.eng | 27.0 | 0.540 |
| newstest2016-ende-deueng.deu.eng | 33.5 | 0.592 |
| newstest2016-enro-roneng.ron.eng | 32.8 | 0.591 |
| newstest2016-enru-ruseng.rus.eng | 24.8 | 0.523 |
| newstest2017-encs-ceseng.ces.eng | 23.7 | 0.510 |
| newstest2017-ende-deueng.deu.eng | 29.3 | 0.556 |
| newstest2017-enlv-laveng.lav.eng | 18.9 | 0.486 |
| newstest2017-enru-ruseng.rus.eng | 28.0 | 0.546 |
| newstest2018-encs-ceseng.ces.eng | 24.9 | 0.521 |
| newstest2018-ende-deueng.deu.eng | 36.0 | 0.604 |
| newstest2018-enru-ruseng.rus.eng | 23.8 | 0.517 |
| newstest2019-deen-deueng.deu.eng | 31.5 | 0.570 |
| newstest2019-guen-gujeng.guj.eng | 12.1 | 0.377 |
| newstest2019-lten-liteng.lit.eng | 26.6 | 0.555 |
| newstest2019-ruen-ruseng.rus.eng | 27.5 | 0.541 |
| Tatoeba-test.afr-eng.afr.eng | 59.0 | 0.724 |
| Tatoeba-test.ang-eng.ang.eng | 9.9 | 0.254 |
| Tatoeba-test.arg-eng.arg.eng | 41.6 | 0.487 |
| Tatoeba-test.asm-eng.asm.eng | 22.8 | 0.392 |
| Tatoeba-test.ast-eng.ast.eng | 36.1 | 0.521 |
| Tatoeba-test.awa-eng.awa.eng | 11.6 | 0.280 |
| Tatoeba-test.bel-eng.bel.eng | 42.2 | 0.597 |
| Tatoeba-test.ben-eng.ben.eng | 45.8 | 0.598 |
| Tatoeba-test.bho-eng.bho.eng | 34.4 | 0.518 |
| Tatoeba-test.bre-eng.bre.eng | 24.4 | 0.405 |
| Tatoeba-test.bul-eng.bul.eng | 50.8 | 0.660 |
| Tatoeba-test.cat-eng.cat.eng | 51.2 | 0.677 |
| Tatoeba-test.ces-eng.ces.eng | 47.6 | 0.641 |
| Tatoeba-test.cor-eng.cor.eng | 5.4 | 0.214 |
| Tatoeba-test.cos-eng.cos.eng | 61.0 | 0.675 |
| Tatoeba-test.csb-eng.csb.eng | 22.5 | 0.394 |
| Tatoeba-test.cym-eng.cym.eng | 34.7 | 0.522 |
| Tatoeba-test.dan-eng.dan.eng | 56.2 | 0.708 |
| Tatoeba-test.deu-eng.deu.eng | 44.9 | 0.625 |
| Tatoeba-test.dsb-eng.dsb.eng | 21.0 | 0.383 |
| Tatoeba-test.egl-eng.egl.eng | 6.9 | 0.221 |
| Tatoeba-test.ell-eng.ell.eng | 62.1 | 0.741 |
| Tatoeba-test.enm-eng.enm.eng | 22.6 | 0.466 |
| Tatoeba-test.ext-eng.ext.eng | 33.2 | 0.496 |
| Tatoeba-test.fao-eng.fao.eng | 28.1 | 0.460 |
| Tatoeba-test.fas-eng.fas.eng | 9.6 | 0.306 |
| Tatoeba-test.fra-eng.fra.eng | 50.3 | 0.661 |
| Tatoeba-test.frm-eng.frm.eng | 30.0 | 0.457 |
| Tatoeba-test.frr-eng.frr.eng | 15.2 | 0.301 |
| Tatoeba-test.fry-eng.fry.eng | 34.4 | 0.525 |
| Tatoeba-test.gcf-eng.gcf.eng | 18.4 | 0.317 |
| Tatoeba-test.gla-eng.gla.eng | 24.1 | 0.400 |
| Tatoeba-test.gle-eng.gle.eng | 52.2 | 0.671 |
| Tatoeba-test.glg-eng.glg.eng | 50.5 | 0.669 |
| Tatoeba-test.glv-eng.glv.eng | 5.7 | 0.189 |
| Tatoeba-test.gos-eng.gos.eng | 19.2 | 0.378 |
| Tatoeba-test.got-eng.got.eng | 0.1 | 0.022 |
| Tatoeba-test.grc-eng.grc.eng | 0.9 | 0.095 |
| Tatoeba-test.gsw-eng.gsw.eng | 23.9 | 0.390 |
| Tatoeba-test.guj-eng.guj.eng | 28.0 | 0.428 |
| Tatoeba-test.hat-eng.hat.eng | 44.2 | 0.567 |
| Tatoeba-test.hbs-eng.hbs.eng | 51.6 | 0.666 |
| Tatoeba-test.hif-eng.hif.eng | 22.3 | 0.451 |
| Tatoeba-test.hin-eng.hin.eng | 41.7 | 0.585 |
| Tatoeba-test.hsb-eng.hsb.eng | 46.4 | 0.590 |
| Tatoeba-test.hye-eng.hye.eng | 40.4 | 0.564 |
| Tatoeba-test.isl-eng.isl.eng | 43.8 | 0.605 |
| Tatoeba-test.ita-eng.ita.eng | 60.7 | 0.735 |
| Tatoeba-test.jdt-eng.jdt.eng | 5.5 | 0.091 |
| Tatoeba-test.kok-eng.kok.eng | 7.8 | 0.205 |
| Tatoeba-test.ksh-eng.ksh.eng | 15.8 | 0.284 |
| Tatoeba-test.kur-eng.kur.eng | 11.6 | 0.232 |
| Tatoeba-test.lad-eng.lad.eng | 30.7 | 0.484 |
| Tatoeba-test.lah-eng.lah.eng | 11.0 | 0.286 |
| Tatoeba-test.lat-eng.lat.eng | 24.4 | 0.432 |
| Tatoeba-test.lav-eng.lav.eng | 47.2 | 0.646 |
| Tatoeba-test.lij-eng.lij.eng | 9.0 | 0.287 |
| Tatoeba-test.lit-eng.lit.eng | 51.7 | 0.670 |
| Tatoeba-test.lld-eng.lld.eng | 22.4 | 0.369 |
| Tatoeba-test.lmo-eng.lmo.eng | 26.1 | 0.381 |
| Tatoeba-test.ltz-eng.ltz.eng | 39.8 | 0.536 |
| Tatoeba-test.mai-eng.mai.eng | 72.3 | 0.758 |
| Tatoeba-test.mar-eng.mar.eng | 32.0 | 0.554 |
| Tatoeba-test.mfe-eng.mfe.eng | 63.1 | 0.822 |
| Tatoeba-test.mkd-eng.mkd.eng | 49.5 | 0.638 |
| Tatoeba-test.msa-eng.msa.eng | 38.6 | 0.566 |
| Tatoeba-test.multi.eng | 45.6 | 0.615 |
| Tatoeba-test.mwl-eng.mwl.eng | 40.4 | 0.767 |
| Tatoeba-test.nds-eng.nds.eng | 35.5 | 0.538 |
| Tatoeba-test.nep-eng.nep.eng | 4.9 | 0.209 |
| Tatoeba-test.nld-eng.nld.eng | 54.2 | 0.694 |
| Tatoeba-test.non-eng.non.eng | 39.3 | 0.573 |
| Tatoeba-test.nor-eng.nor.eng | 50.9 | 0.663 |
| Tatoeba-test.oci-eng.oci.eng | 19.6 | 0.386 |
| Tatoeba-test.ori-eng.ori.eng | 16.2 | 0.364 |
| Tatoeba-test.orv-eng.orv.eng | 13.6 | 0.288 |
| Tatoeba-test.oss-eng.oss.eng | 9.4 | 0.301 |
| Tatoeba-test.pan-eng.pan.eng | 17.1 | 0.389 |
| Tatoeba-test.pap-eng.pap.eng | 57.0 | 0.680 |
| Tatoeba-test.pdc-eng.pdc.eng | 41.6 | 0.526 |
| Tatoeba-test.pms-eng.pms.eng | 13.7 | 0.333 |
| Tatoeba-test.pol-eng.pol.eng | 46.5 | 0.632 |
| Tatoeba-test.por-eng.por.eng | 56.4 | 0.710 |
| Tatoeba-test.prg-eng.prg.eng | 2.3 | 0.193 |
| Tatoeba-test.pus-eng.pus.eng | 3.2 | 0.194 |
| Tatoeba-test.roh-eng.roh.eng | 17.5 | 0.420 |
| Tatoeba-test.rom-eng.rom.eng | 5.0 | 0.237 |
| Tatoeba-test.ron-eng.ron.eng | 51.4 | 0.670 |
| Tatoeba-test.rue-eng.rue.eng | 26.0 | 0.447 |
| Tatoeba-test.rus-eng.rus.eng | 47.8 | 0.634 |
| Tatoeba-test.san-eng.san.eng | 4.0 | 0.195 |
| Tatoeba-test.scn-eng.scn.eng | 45.1 | 0.440 |
| Tatoeba-test.sco-eng.sco.eng | 41.9 | 0.582 |
| Tatoeba-test.sgs-eng.sgs.eng | 38.7 | 0.498 |
| Tatoeba-test.sin-eng.sin.eng | 29.7 | 0.499 |
| Tatoeba-test.slv-eng.slv.eng | 38.2 | 0.564 |
| Tatoeba-test.snd-eng.snd.eng | 12.7 | 0.342 |
| Tatoeba-test.spa-eng.spa.eng | 53.2 | 0.687 |
| Tatoeba-test.sqi-eng.sqi.eng | 51.9 | 0.679 |
| Tatoeba-test.stq-eng.stq.eng | 9.0 | 0.391 |
| Tatoeba-test.swe-eng.swe.eng | 57.4 | 0.705 |
| Tatoeba-test.swg-eng.swg.eng | 18.0 | 0.338 |
| Tatoeba-test.tgk-eng.tgk.eng | 24.3 | 0.413 |
| Tatoeba-test.tly-eng.tly.eng | 1.1 | 0.094 |
| Tatoeba-test.ukr-eng.ukr.eng | 48.0 | 0.639 |
| Tatoeba-test.urd-eng.urd.eng | 27.2 | 0.471 |
| Tatoeba-test.vec-eng.vec.eng | 28.0 | 0.398 |
| Tatoeba-test.wln-eng.wln.eng | 17.5 | 0.320 |
| Tatoeba-test.yid-eng.yid.eng | 26.9 | 0.457 |
| Tatoeba-test.zza-eng.zza.eng | 1.7 | 0.131 |
### System Info:
- hf_name: ine-eng
- source_languages: ine
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ine-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ca', 'es', 'os', 'ro', 'fy', 'cy', 'sc', 'is', 'yi', 'lb', 'an', 'sq', 'fr', 'ht', 'rm', 'ps', 'af', 'uk', 'sl', 'lt', 'bg', 'be', 'gd', 'si', 'en', 'br', 'mk', 'or', 'mr', 'ru', 'fo', 'co', 'oc', 'pl', 'gl', 'nb', 'bn', 'id', 'hy', 'da', 'gv', 'nl', 'pt', 'hi', 'as', 'kw', 'ga', 'sv', 'gu', 'wa', 'lv', 'el', 'it', 'hr', 'ur', 'nn', 'de', 'cs', 'ine']
- src_constituents: {'cat', 'spa', 'pap', 'mwl', 'lij', 'bos_Latn', 'lad_Latn', 'lat_Latn', 'pcd', 'oss', 'ron', 'fry', 'cym', 'awa', 'swg', 'zsm_Latn', 'srd', 'gcf_Latn', 'isl', 'yid', 'bho', 'ltz', 'kur_Latn', 'arg', 'pes_Thaa', 'sqi', 'csb_Latn', 'fra', 'hat', 'non_Latn', 'sco', 'pnb', 'roh', 'bul_Latn', 'pus', 'afr', 'ukr', 'slv', 'lit', 'tmw_Latn', 'hsb', 'tly_Latn', 'bul', 'bel', 'got_Goth', 'lat_Grek', 'ext', 'gla', 'mai', 'sin', 'hif_Latn', 'eng', 'bre', 'nob_Hebr', 'prg_Latn', 'ang_Latn', 'aln', 'mkd', 'ori', 'mar', 'afr_Arab', 'san_Deva', 'gos', 'rus', 'fao', 'orv_Cyrl', 'bel_Latn', 'cos', 'zza', 'grc_Grek', 'oci', 'mfe', 'gom', 'bjn', 'sgs', 'tgk_Cyrl', 'hye_Latn', 'pdc', 'srp_Cyrl', 'pol', 'ast', 'glg', 'pms', 'nob', 'ben', 'min', 'srp_Latn', 'zlm_Latn', 'ind', 'rom', 'hye', 'scn', 'enm_Latn', 'lmo', 'npi', 'pes', 'dan', 'rus_Latn', 'jdt_Cyrl', 'gsw', 'glv', 'nld', 'snd_Arab', 'kur_Arab', 'por', 'hin', 'dsb', 'asm', 'lad', 'frm_Latn', 'ksh', 'pan_Guru', 'cor', 'gle', 'swe', 'guj', 'wln', 'lav', 'ell', 'frr', 'rue', 'ita', 'hrv', 'urd', 'stq', 'nno', 'deu', 'lld_Latn', 'ces', 'egl', 'vec', 'max_Latn', 'pes_Latn', 'ltg', 'nds'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ine-eng/opus2m-2020-08-01.test.txt
- src_alpha3: ine
- tgt_alpha3: eng
- short_pair: ine-en
- chrF2_score: 0.615
- bleu: 45.6
- brevity_penalty: 0.997
- ref_len: 71872.0
- src_name: Indo-European languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: ine
- tgt_alpha2: en
- prefer_old: False
- long_pair: ine-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
keremberke/yolov8n-pcb-defect-segmentation | keremberke | "2023-02-22T13:02:17Z" | 2,900 | 3 | ultralytics | [
"ultralytics",
"tensorboard",
"v8",
"ultralyticsplus",
"yolov8",
"yolo",
"vision",
"image-segmentation",
"pytorch",
"awesome-yolov8-models",
"dataset:keremberke/pcb-defect-segmentation",
"model-index",
"region:us"
] | image-segmentation | "2023-01-28T06:32:15Z" |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-segmentation
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/pcb-defect-segmentation
model-index:
- name: keremberke/yolov8n-pcb-defect-segmentation
results:
- task:
type: image-segmentation
dataset:
type: keremberke/pcb-defect-segmentation
name: pcb-defect-segmentation
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.51186 # min: 0.0 - max: 1.0
name: [email protected](box)
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.51667 # min: 0.0 - max: 1.0
name: [email protected](mask)
---
<div align="center">
<img width="640" alt="keremberke/yolov8n-pcb-defect-segmentation" src="https://huggingface.co/keremberke/yolov8n-pcb-defect-segmentation/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8n-pcb-defect-segmentation')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
print(results[0].masks)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
timm/convnextv2_nano.fcmae_ft_in22k_in1k_384 | timm | "2024-02-10T23:29:33Z" | 2,899 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2301.00808",
"license:cc-by-nc-4.0",
"region:us"
] | image-classification | "2023-01-05T01:55:50Z" | ---
license: cc-by-nc-4.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-1k
---
# Model card for convnextv2_nano.fcmae_ft_in22k_in1k_384
A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 15.6
- GMACs: 7.2
- Activations (M): 24.6
- Image size: 384 x 384
- **Papers:**
- ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808
- **Original:** https://github.com/facebookresearch/ConvNeXt-V2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnextv2_nano.fcmae_ft_in22k_in1k_384', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnextv2_nano.fcmae_ft_in22k_in1k_384',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 80, 96, 96])
# torch.Size([1, 160, 48, 48])
# torch.Size([1, 320, 24, 24])
# torch.Size([1, 640, 12, 12])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnextv2_nano.fcmae_ft_in22k_in1k_384',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 640, 12, 12) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@article{Woo2023ConvNeXtV2,
title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
year={2023},
journal={arXiv preprint arXiv:2301.00808},
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
unsloth/zephyr-sft-bnb-4bit | unsloth | "2024-03-22T15:15:34Z" | 2,899 | 4 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"unsloth",
"mistral-7b",
"sft",
"zephyr",
"bnb",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2023-12-31T06:51:02Z" | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- mistral
- mistral-7b
- sft
- zephyr
- bnb
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
Follows https://github.com/huggingface/alignment-handbook/issues/45#issuecomment-1845598205
From `HuggingFaceH4/mistral-7b-sft-beta`
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## โจ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Gemma 7b** | [โถ๏ธ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [โถ๏ธ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [โถ๏ธ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [โถ๏ธ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [โถ๏ธ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [โถ๏ธ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [โถ๏ธ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. |
mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF | mradermacher | "2024-06-06T21:49:50Z" | 2,899 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Sao10K/SOLAR-10.7B-NahIdWin",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-05T09:32:10Z" | ---
base_model: Sao10K/SOLAR-10.7B-NahIdWin
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Sao10K/SOLAR-10.7B-NahIdWin
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-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/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/SOLAR-10.7B-NahIdWin-i1-GGUF/resolve/main/SOLAR-10.7B-NahIdWin.i1-Q6_K.gguf) | i1-Q6_K | 8.9 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
microsoft/xtremedistil-l6-h256-uncased | microsoft | "2021-08-05T17:49:53Z" | 2,898 | 29 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"text-classification",
"en",
"arxiv:2106.04563",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-02T23:29:05Z" | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation](https://arxiv.org/abs/2106.04563).
We leverage task transfer combined with multi-task distillation techniques from the papers [XtremeDistil: Multi-stage Distillation for Massive Multilingual Models](https://www.aclweb.org/anthology/2020.acl-main.202.pdf) and [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://proceedings.neurips.cc/paper/2020/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) with the following [Github code](https://github.com/microsoft/xtreme-distil-transformers).
This l6-h384 checkpoint with **6** layers, **384** hidden size, **12** attention heads corresponds to **22 million** parameters with **5.3x** speedup over BERT-base.
Other available checkpoints: [xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) and [xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased)
The following table shows the results on GLUE dev set and SQuAD-v2.
| Models | #Params | Speedup | MNLI | QNLI | QQP | RTE | SST | MRPC | SQUAD2 | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 1x | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 76.8 | 84.8 |
| DistilBERT | 66 | 2x | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 70.7 | 81.3 |
| TinyBERT | 66 | 2x | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 73.1 | 84.3 |
| MiniLM | 66 | 2x | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 76.4 | 84.9 |
| MiniLM | 22 | 5.3x | 82.8 | 90.3 | 90.6 | 68.9 | 91.3 | 86.6 | 72.9 | 83.3 |
| XtremeDistil-l6-h256 | 13 | 8.7x | 83.9 | 89.5 | 90.6 | 80.1 | 91.2 | 90.0 | 74.1 | 85.6 |
| XtremeDistil-l6-h384 | 22 | 5.3x | 85.4 | 90.3 | 91.0 | 80.9 | 92.3 | 90.0 | 76.6 | 86.6 |
| XtremeDistil-l12-h384 | 33 | 2.7x | 87.2 | 91.9 | 91.3 | 85.6 | 93.1 | 90.4 | 80.2 | 88.5 |
Tested with `tensorflow 2.3.1, transformers 4.1.1, torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@misc{mukherjee2021xtremedistiltransformers,
title={XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation},
author={Subhabrata Mukherjee and Ahmed Hassan Awadallah and Jianfeng Gao},
year={2021},
eprint={2106.04563},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF | mradermacher | "2024-06-13T00:21:17Z" | 2,898 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama",
"not-for-all-audiences",
"en",
"base_model:v000000/L3-8B-Poppy-Moonfall-OG",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T22:07:13Z" | ---
base_model: v000000/L3-8B-Poppy-Moonfall-OG
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama
- not-for-all-audiences
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/v000000/L3-8B-Poppy-Moonfall-OG
<!-- 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/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3-8B-Poppy-Moonfall-OG-GGUF/resolve/main/L3-8B-Poppy-Moonfall-OG.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 -->
|
bartowski/Starling-LM-7B-beta-GGUF | bartowski | "2024-03-20T22:23:14Z" | 2,897 | 22 | transformers | [
"transformers",
"gguf",
"reward model",
"RLHF",
"RLAIF",
"text-generation",
"en",
"dataset:berkeley-nest/Nectar",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-20T22:09:49Z" | ---
license: apache-2.0
datasets:
- berkeley-nest/Nectar
language:
- en
library_name: transformers
tags:
- reward model
- RLHF
- RLAIF
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp Quantizations of Starling-LM-7B-beta
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2440">b2440</a> for quantization.
Original model: https://huggingface.co/Nexusflow/Starling-LM-7B-beta
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Starling-LM-7B-beta-Q8_0.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q8_0.gguf) | Q8_0 | 7.69GB | Extremely high quality, generally unneeded but max available quant. |
| [Starling-LM-7B-beta-Q6_K.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q6_K.gguf) | Q6_K | 5.94GB | Very high quality, near perfect, *recommended*. |
| [Starling-LM-7B-beta-Q5_K_M.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q5_K_M.gguf) | Q5_K_M | 5.13GB | High quality, very usable. |
| [Starling-LM-7B-beta-Q5_K_S.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q5_K_S.gguf) | Q5_K_S | 4.99GB | High quality, very usable. |
| [Starling-LM-7B-beta-Q5_0.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q5_0.gguf) | Q5_0 | 4.99GB | High quality, older format, generally not recommended. |
| [Starling-LM-7B-beta-Q4_K_M.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q4_K_M.gguf) | Q4_K_M | 4.36GB | Good quality, similar to 4.25 bpw. |
| [Starling-LM-7B-beta-Q4_K_S.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q4_K_S.gguf) | Q4_K_S | 4.14GB | Slightly lower quality with small space savings. |
| [Starling-LM-7B-beta-IQ4_NL.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-IQ4_NL.gguf) | IQ4_NL | 4.15GB | Good quality, similar to Q4_K_S, new method of quanting, |
| [Starling-LM-7B-beta-IQ4_XS.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-IQ4_XS.gguf) | IQ4_XS | 3.94GB | Decent quality, new method with similar performance to Q4. |
| [Starling-LM-7B-beta-Q4_0.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q4_0.gguf) | Q4_0 | 4.10GB | Decent quality, older format, generally not recommended. |
| [Starling-LM-7B-beta-IQ3_M.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-IQ3_M.gguf) | IQ3_M | 3.28GB | Medium-low quality, new method with decent performance. |
| [Starling-LM-7B-beta-IQ3_S.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-IQ3_S.gguf) | IQ3_S | 3.18GB | Lower quality, new method with decent performance, recommended over Q3 quants. |
| [Starling-LM-7B-beta-Q3_K_L.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q3_K_L.gguf) | Q3_K_L | 3.82GB | Lower quality but usable, good for low RAM availability. |
| [Starling-LM-7B-beta-Q3_K_M.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q3_K_M.gguf) | Q3_K_M | 3.51GB | Even lower quality. |
| [Starling-LM-7B-beta-Q3_K_S.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q3_K_S.gguf) | Q3_K_S | 3.16GB | Low quality, not recommended. |
| [Starling-LM-7B-beta-Q2_K.gguf](https://huggingface.co/bartowski/Starling-LM-7B-beta-GGUF/blob/main/Starling-LM-7B-beta-Q2_K.gguf) | Q2_K | 2.71GB | Extremely low quality, *not* recommended.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
hallisky/sarcasm-classifier-gpt4-data | hallisky | "2024-05-17T00:11:04Z" | 2,897 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-05-16T19:41:58Z" | ---
license: apache-2.0
widget:
- text: "Oh really, what a great idea! Let's just ignore all the facts and trot right ahead!"
example_title: "Sarcastic Dialogue"
output:
- label: sarcasm_more
score: 1.0
- label: sarcasm_less
score: 0.0
- text: "What a great idea - let's continue!"
example_title: "Sincere Dialogue"
---
|
neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV | neuralmagic | "2024-06-19T16:10:20Z" | 2,897 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"fp8",
"vllm",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-20T18:44:16Z" | ---
tags:
- fp8
- vllm
---
# Meta-Llama-3-8B-Instruct-FP8-KV
## Model Overview
Meta-Llama-3-8B-Instruct quantized to FP8 weights and activations using per-tensor quantization, ready for inference with vLLM >= 0.5.0.
This model checkpoint also includes per-tensor scales for FP8 quantized KV Cache, accessed through the `--kv-cache-dtype fp8` argument in vLLM.
```python
from vllm import LLM
model = LLM(model="neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV", kv_cache_dtype="fp8")
result = model.generate("Hello, my name is")
```
## Usage and Creation
Produced using [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py).
```python
from datasets import load_dataset
from transformers import AutoTokenizer
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
pretrained_model_dir = "meta-llama/Meta-Llama-3-8B-Instruct"
quantized_model_dir = "Meta-Llama-3-8B-Instruct-FP8-KV"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
ds = load_dataset("mgoin/ultrachat_2k", split="train_sft")
examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds]
examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt").to("cuda")
quantize_config = BaseQuantizeConfig(
quant_method="fp8",
activation_scheme="static",
ignore_patterns=["re:.*lm_head"],
kv_cache_quant_targets=("k_proj", "v_proj"),
)
model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
model.quantize(examples)
model.save_quantized(quantized_model_dir)
```
## Evaluation
### Open LLM Leaderboard evaluation scores
| | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct-FP8 | Meta-Llama-3-8B-Instruct-FP8-KV<br>(this model) |
| :------------------: | :----------------------: | :--------------------------: | :---------------------------------------------: |
| gsm8k<br>5-shot | 75.44 | 74.37 | 74.98 |
|
PygmalionAI/pygmalion-2-13b | PygmalionAI | "2023-09-15T20:29:04Z" | 2,896 | 72 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text generation",
"instruct",
"en",
"dataset:PygmalionAI/PIPPA",
"dataset:Open-Orca/OpenOrca",
"dataset:Norquinal/claude_multiround_chat_30k",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"dataset:databricks/databricks-dolly-15k",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-04T22:05:31Z" | ---
language:
- en
thumbnail: null
tags:
- text generation
- instruct
pipeline_tag: text-generation
inference: false
license: llama2
datasets:
- PygmalionAI/PIPPA
- Open-Orca/OpenOrca
- Norquinal/claude_multiround_chat_30k
- jondurbin/airoboros-gpt4-1.4.1
- databricks/databricks-dolly-15k
---
<h1 style="text-align: center">Pygmalion-2 13B</h1>
<h2 style="text-align: center">An instruction-tuned Llama-2 biased towards fiction writing and conversation.</h2>
## Model Details
The long-awaited release of our new models based on Llama-2 is finally here. Pygmalion-2 13B (formerly known as Metharme) is based on
[Llama-2 13B](https://huggingface.co/meta-llama/llama-2-13b-hf) released by Meta AI.
The Metharme models were an experiment to try and get a model that is usable for conversation, roleplaying and storywriting,
but which can be guided using natural language like other instruct models. After much deliberation, we reached the conclusion
that the Metharme prompting format is superior (and easier to use) compared to the classic Pygmalion.
This model was trained by doing supervised fine-tuning over a mixture of regular instruction data alongside roleplay, fictional stories
and conversations with synthetically generated instructions attached.
This model is freely available for both commercial and non-commercial use, as per the Llama-2 license.
## Prompting
The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input.
The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to
form a conversation history.
### Prompting example
The system prompt has been designed to allow the model to "enter" various modes and dictate the reply length. Here's an example:
```
<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows:
{{persona}}
You shall reply to the user while staying in character, and generate long responses.
```
## Dataset
The dataset used to fine-tune this model includes our own [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA), along with several other instruction
datasets, and datasets acquired from various RP forums.
## Limitations and biases
The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
## Acknowledgements
We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for this model.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
|
MaziyarPanahi/mergekit-slerp-mpovshq-GGUF | MaziyarPanahi | "2024-06-17T12:36:56Z" | 2,896 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:WizardLM/WizardMath-7B-V1.1",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-mpovshq"
] | text-generation | "2024-06-17T12:09:28Z" | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- conversational
- base_model:WizardLM/WizardMath-7B-V1.1
- base_model:NousResearch/Hermes-2-Pro-Mistral-7B
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-mpovshq-GGUF
base_model: mergekit-community/mergekit-slerp-mpovshq
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-mpovshq-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-mpovshq-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-mpovshq](https://huggingface.co/mergekit-community/mergekit-slerp-mpovshq)
## Description
[MaziyarPanahi/mergekit-slerp-mpovshq-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-mpovshq-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-mpovshq](https://huggingface.co/mergekit-community/mergekit-slerp-mpovshq).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
๐ Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
codesage/codesage-small | codesage | "2024-06-26T03:37:57Z" | 2,895 | 4 | transformers | [
"transformers",
"pytorch",
"custom_code",
"code",
"dataset:bigcode/the-stack-dedup",
"arxiv:2402.01935",
"arxiv:2305.06161",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-02-06T18:35:41Z" | ---
license: apache-2.0
datasets:
- bigcode/the-stack-dedup
library_name: transformers
language:
- code
---
## CodeSage-Small
### Model description
CodeSage is a new family of open code embedding models with an encoder architecture that support a wide range of source code understanding tasks. It is introduced in the paper:
[Code Representation Learning At Scale by
Dejiao Zhang*, Wasi Uddin Ahmad*, Ming Tan, Hantian Ding, Ramesh Nallapati, Dan Roth, Xiaofei Ma, Bing Xiang](https://arxiv.org/abs/2402.01935) (* indicates equal contribution).
### Pretraining data
This checkpoint is trained on the Stack data (https://huggingface.co/datasets/bigcode/the-stack-dedup). Supported languages (9 in total) are as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.
### Training procedure
This checkpoint is first trained on code data via masked language modeling (MLM) and then on bimodal text-code pair data. Please refer to the paper for more details.
### How to use
This checkpoint consists of an encoder (130M model), which can be used to extract code embeddings of 1024 dimension. It can be easily loaded using the AutoModel functionality and employs the Starcoder tokenizer (https://arxiv.org/pdf/2305.06161.pdf).
```
from transformers import AutoModel, AutoTokenizer
checkpoint = "codesage/codesage-small"
device = "cuda" # for GPU usage or "cpu" for CPU usage
# Note: CodeSage requires adding eos token at the end of
# each tokenized sequence to ensure good performance
tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True, add_eos_token=True)
model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)
inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", return_tensors="pt").to(device)
embedding = model(inputs)[0]
print(f'Dimension of the embedding: {embedding[0].size()}')
# Dimension of the embedding: torch.Size([14, 1024])
```
### BibTeX entry and citation info
```
@inproceedings{
zhang2024code,
title={{CODE} {REPRESENTATION} {LEARNING} {AT} {SCALE}},
author={Dejiao Zhang and Wasi Uddin Ahmad and Ming Tan and Hantian Ding and Ramesh Nallapati and Dan Roth and Xiaofei Ma and Bing Xiang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=vfzRRjumpX}
}
``` |
predibase/Meta-Llama-3-70B-Instruct-dequantized | predibase | "2024-05-03T19:29:36Z" | 2,895 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"conversational",
"en",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-03T19:10:06Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- llama
base_model: meta-llama/Meta-Llama-3-70B-Instruct
--- |
TencentARC/t2iadapter_depth_sd15v2 | TencentARC | "2023-07-31T11:10:44Z" | 2,892 | 2 | diffusers | [
"diffusers",
"art",
"t2i-adapter",
"controlnet",
"stable-diffusion",
"image-to-image",
"arxiv:2302.08453",
"base_model:runwayml/stable-diffusion-v1-5",
"license:apache-2.0",
"region:us"
] | image-to-image | "2023-07-14T19:01:05Z" | ---
license: apache-2.0
base_model: runwayml/stable-diffusion-v1-5
tags:
- art
- t2i-adapter
- controlnet
- stable-diffusion
- image-to-image
---
# T2I Adapter - Depth
T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint.
This checkpoint provides conditioning on depth for the stable diffusion 1.5 checkpoint.
## Model Details
- **Developed by:** T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** Apache 2.0
- **Resources for more information:** [GitHub Repository](https://github.com/TencentARC/T2I-Adapter), [Paper](https://arxiv.org/abs/2302.08453).
- **Cite as:**
@misc{
title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models},
author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie},
year={2023},
eprint={2302.08453},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
### Checkpoints
| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
|---|---|---|---|
|[TencentARC/t2iadapter_color_sd14v1](https://huggingface.co/TencentARC/t2iadapter_color_sd14v1)<br/> *Trained with spatial color palette* | A image with 8x8 color palette.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_sample_output.png"/></a>|
|[TencentARC/t2iadapter_canny_sd14v1](https://huggingface.co/TencentARC/t2iadapter_canny_sd14v1)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/canny_sample_output.png"/></a>|
|[TencentARC/t2iadapter_sketch_sd14v1](https://huggingface.co/TencentARC/t2iadapter_sketch_sd14v1)<br/> *Trained with [PidiNet](https://github.com/zhuoinoulu/pidinet) edge detection* | A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/sketch_sample_output.png"/></a>|
|[TencentARC/t2iadapter_depth_sd14v1](https://huggingface.co/TencentARC/t2iadapter_depth_sd14v1)<br/> *Trained with Midas depth estimation* | A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_output.png"/></a>|
|[TencentARC/t2iadapter_openpose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_openpose_sd14v1)<br/> *Trained with OpenPose bone image* | A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/openpose_sample_output.png"/></a>|
|[TencentARC/t2iadapter_keypose_sd14v1](https://huggingface.co/TencentARC/t2iadapter_keypose_sd14v1)<br/> *Trained with mmpose skeleton image* | A [mmpose skeleton](https://github.com/open-mmlab/mmpose) image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_output.png"/></a>|
|[TencentARC/t2iadapter_seg_sd14v1](https://huggingface.co/TencentARC/t2iadapter_seg_sd14v1)<br/>*Trained with semantic segmentation* | An [custom](https://github.com/TencentARC/T2I-Adapter/discussions/25) segmentation protocol image.|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_input.png"/></a>|<a href="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"><img width="64" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/seg_sample_output.png"/></a> |
|[TencentARC/t2iadapter_canny_sd15v2](https://huggingface.co/TencentARC/t2iadapter_canny_sd15v2)||
|[TencentARC/t2iadapter_depth_sd15v2](https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2)||
|[TencentARC/t2iadapter_sketch_sd15v2](https://huggingface.co/TencentARC/t2iadapter_sketch_sd15v2)||
|[TencentARC/t2iadapter_zoedepth_sd15v1](https://huggingface.co/TencentARC/t2iadapter_zoedepth_sd15v1)||
## Example
1. Dependencies
```sh
pip install diffusers transformers controlnet_aux
```
2. Run code:
```python
from controlnet_aux import MidasDetector
from PIL import Image
from diffusers import T2IAdapter, StableDiffusionAdapterPipeline
import torch
midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
image = Image.open('./images/depth_input.png')
image = midas(image)
image.save('./images/depth.png')
adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_depth_sd15v2", torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16"
)
pipe.to('cuda')
generator = torch.Generator().manual_seed(1)
depth_out = pipe(prompt="storm trooper giving a speech", image=image, generator=generator).images[0]
depth_out.save('./images/depth_output.png')
```
 |
TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF | TheBloke | "2023-09-27T12:47:09Z" | 2,892 | 49 | transformers | [
"transformers",
"gguf",
"llama",
"facebook",
"meta",
"pytorch",
"llama-2",
"text-generation",
"en",
"dataset:garage-bAInd/Open-Platypus",
"arxiv:2307.09288",
"base_model:uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-02T09:17:16Z" | ---
language:
- en
license: llama2
library_name: transformers
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
datasets:
- garage-bAInd/Open-Platypus
model_name: Speechess Lllama2 Hermes Orca-Platypus WizardLM 13B
base_model: uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b
inference: false
model_creator: Jiangwen Su
model_type: llama
pipeline_tag: text-generation
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Speechess Lllama2 Hermes Orca-Platypus WizardLM 13B - GGUF
- Model creator: [Jiangwen Su](https://huggingface.co/uukuguy)
- Original model: [Speechess Lllama2 Hermes Orca-Platypus WizardLM 13B](https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jiangwen Su's Speechess Lllama2 Hermes Orca-Platypus WizardLM 13B](https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF)
* [Jiangwen Su's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF/blob/main/speechless-llama2-hermes-orca-platypus-wizardlm-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF and below it, a specific filename to download, such as: speechless-llama2-hermes-orca-platypus-wizardlm-13b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF speechless-llama2-hermes-orca-platypus-wizardlm-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF speechless-llama2-hermes-orca-platypus-wizardlm-13b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m speechless-llama2-hermes-orca-platypus-wizardlm-13b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Speechless-Llama2-Hermes-Orca-Platypus-WizardLM-13B-GGUF", model_file="speechless-llama2-hermes-orca-platypus-wizardlm-13b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjรคreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, ์ค๊ต ๊น, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, ้ฟๆ, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Jiangwen Su's Speechess Lllama2 Hermes Orca-Platypus WizardLM 13B
<p><h1> speechless-llama2-hermes-orca-platypus-wizardlm-13b </h1></p>
speechless-llama2-hermes-orca-platypus-wizardlm-13b is a merge of NousResearch/Nous-Hermes-Llama2-13b, Open-Orca/OpenOrca-Platypus2-13B and WizardLM/WizardLM-13B-V1.2.
| Metric | Value |
| --- | --- |
| ARC | |
| HellaSwag | |
| MMLU | |
| TruthfulQA | |
| Average | |
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes โ 7B, 13B, and 70B โ as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Metaโs sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2โs potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software โbug,โ or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
<!-- original-model-card end -->
|
microsoft/cvt-21-384-22k | microsoft | "2022-10-11T13:47:26Z" | 2,889 | 3 | transformers | [
"transformers",
"pytorch",
"tf",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-04-04T11:33:08Z" | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT).
Disclaimer: The team releasing CvT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Usage
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, CvtForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/cvt-21-384-22k')
model = CvtForImageClassification.from_pretrained('microsoft/cvt-21-384-22k')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
``` |
timm/vit_base_patch8_224.dino | timm | "2024-02-09T18:13:19Z" | 2,889 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"arxiv:2104.14294",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-feature-extraction | "2022-12-22T07:23:57Z" | ---
license: apache-2.0
library_name: timm
tags:
- image-feature-extraction
- timm
---
# Model card for vit_base_patch8_224.dino
A Vision Transformer (ViT) image feature model. Trained with Self-Supervised DINO method.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 85.8
- GMACs: 66.9
- Activations (M): 65.7
- Image size: 224 x 224
- **Papers:**
- Emerging Properties in Self-Supervised Vision Transformers: https://arxiv.org/abs/2104.14294
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Pretrain Dataset:** ImageNet-1k
- **Original:** https://github.com/facebookresearch/dino
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch8_224.dino', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch8_224.dino',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 785, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{caron2021emerging,
title={Emerging properties in self-supervised vision transformers},
author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J{'e}gou, Herv{'e} and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
pages={9650--9660},
year={2021}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Yntec/Gacha | Yntec | "2023-12-07T21:01:37Z" | 2,889 | 3 | diffusers | [
"diffusers",
"safetensors",
"Game Art",
"General purpose",
"Logos",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-12-07T20:24:29Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Game Art
- General purpose
- Logos
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
inference: true
---
# Gacha
A model with the gacha mechanic implemented to get you addicted to generating images.
Samples and prompts:
(Click for larger)
Top left: a lemon themed hamburger, high quality
Top right: a basket full of cherries, grass by wess anderson, high quality
Bottom left: A mushroom themed cake, high quality pie
Bottom right: highly detailed chibi koala in kitchen, hammer chef, fish, dungeons and dragons
|
mradermacher/1min-v2-luxia-8b-GGUF | mradermacher | "2024-06-05T15:28:58Z" | 2,888 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:esunn/1min-v2-luxia-8b",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | "2024-06-05T14:59:51Z" | ---
base_model: esunn/1min-v2-luxia-8b
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/esunn/1min-v2-luxia-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/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.IQ3_XS.gguf) | IQ3_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.IQ3_S.gguf) | IQ3_S | 3.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.IQ3_M.gguf) | IQ3_M | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q5_K_M.gguf) | Q5_K_M | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/1min-v2-luxia-8b-GGUF/resolve/main/1min-v2-luxia-8b.f16.gguf) | f16 | 16.5 | 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 -->
|
paust/pko-t5-small | paust | "2022-09-14T04:41:53Z" | 2,887 | 4 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2022-05-16T10:26:56Z" | ---
language: ko
license: cc-by-4.0
---
# pko-t5-small
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 ๋ ํ๊ตญ์ด ์ ์ฉ ๋ฐ์ดํฐ๋ก ํ์ตํ [t5 v1.1 ๋ชจ๋ธ](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)์
๋๋ค.
ํ๊ตญ์ด๋ฅผ tokenize ํ๊ธฐ ์ํด์ sentencepiece ๋์ OOV ๊ฐ ์๋ BBPE ๋ฅผ ์ฌ์ฉํ์ผ๋ฉฐ ํ๊ตญ์ด ๋ฐ์ดํฐ (๋๋ฌด์ํค, ์ํคํผ๋์, ๋ชจ๋์๋ง๋ญ์น ๋ฑ..) ๋ฅผ T5 ์ span corruption task ๋ฅผ ์ฌ์ฉํด์ unsupervised learning ๋ง ์ ์ฉํ์ฌ ํ์ต์ ์งํํ์ต๋๋ค.
pko-t5 ๋ฅผ ์ฌ์ฉํ์ค ๋๋ ๋์ task ์ ํ์ธํ๋ํ์ฌ ์ฌ์ฉํ์๊ธฐ ๋ฐ๋๋๋ค.
## Usage
transformers ์ API ๋ฅผ ์ฌ์ฉํ์ฌ ์ ๊ทผ ๊ฐ๋ฅํฉ๋๋ค. tokenizer ๋ฅผ ์ฌ์ฉํ ๋๋ `T5Tokenizer` ๊ฐ ์๋๋ผ `T5TokenizerFast` ๋ฅผ ์ฌ์ฉํด์ฃผ์ญ์์ค. model ์ T5ForConditionalGeneration ๋ฅผ ๊ทธ๋๋ก ํ์ฉํ์๋ฉด ๋ฉ๋๋ค.
### Example
```python
from transformers import T5TokenizerFast, T5ForConditionalGeneration
tokenizer = T5TokenizerFast.from_pretrained('paust/pko-t5-small')
model = T5ForConditionalGeneration.from_pretrained('paust/pko-t5-small')
input_ids = tokenizer(["qa question: ๋น์ ์ ์ด๋ฆ์ ๋ฌด์์ธ๊ฐ์?"]).input_ids
labels = tokenizer(["T5 ์
๋๋ค."]).input_ids
outputs = model(input_ids, labels)
print(f"loss={outputs.loss} logits={outputs.logits}")
```
## Klue ํ๊ฐ (dev)
| | Model | ynat (macro F1) | sts (pearsonr/F1) | nli (acc) | ner (entity-level F1) | re (micro F1) | dp (LAS) | mrc (EM/F1) |
|-----|------------------------------------------------------------------|-----------------|-------------------|-----------|-----------------------|---------------|-----------|-------------|
| | Baseline | **87.30** | **93.20/86.13** | **89.50** | 86.06 | 71.06 | 87.93 | **75.26/-** |
| FT | [pko-t5-small](https://huggingface.co/paust/pko-t5-small) (77M) | 86.21 | 77.99/77.01 | 69.20 | 82.60 | 66.46 | 93.15 | 43.81/46.58 |
| FT | [pko-t5-base](https://huggingface.co/paust/pko-t5-base) (250M) | 87.29 | 90.25/83.43 | 79.73 | 87.80 | 67.23 | 97.28 | 61.53/64.74 |
| FT | [pko-t5-large](https://huggingface.co/paust/pko-t5-large) (800M) | 87.12 | 92.05/85.24 | 84.96 | **88.18** | **75.17** | **97.60** | 68.01/71.44 |
| MT | pko-t5-small | 84.54 | 68.50/72/02 | 51.16 | 74.69 | 66.11 | 80.40 | 43.60/46.28 |
| MT | pko-t5-base | 86.89 | 83.96/80.30 | 72.03 | 85.27 | 66.59 | 95.05 | 61.11/63.94 |
| MT | pko-t5-large | 87.57 | 91.93/86.29 | 83.63 | 87.41 | 71.34 | 96.99 | 70.70/73.72 |
- FT: ์ฑ๊ธํ์คํฌ ํ์ธํ๋ / MT: ๋ฉํฐํ์คํฌ ํ์ธํ๋
- [Baseline](https://arxiv.org/abs/2105.09680): KLUE ๋
ผ๋ฌธ์์ ์๊ฐ๋ dev set ์ ๋ํ SOTA ์ ์
## License
[PAUST](https://paust.io)์์ ๋ง๋ pko-t5๋ [MIT license](https://github.com/paust-team/pko-t5/blob/main/LICENSE) ํ์ ๊ณต๊ฐ๋์ด ์์ต๋๋ค. |
Yntec/ResidentCNZCartoon3D | Yntec | "2024-01-07T20:17:34Z" | 2,887 | 1 | diffusers | [
"diffusers",
"safetensors",
"Style",
"Cartoon",
"Disney",
"Pixar",
"Dreamworks",
"residentchiefnz",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-01-07T19:25:13Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Style
- Cartoon
- Disney
- Pixar
- Dreamworks
- residentchiefnz
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
---
# RCNZ Cartoon 3D v2
Original page: https://civitai.com/models/66347?modelVersionId=82547
Samples and prompts (in no particular order):
(Click for larger)
highquality, masterpiece, 1girl, Chi-Chi, close up, arms up, pink helmet, black hair, black eyes, blush, bikini armor, enjoying, aqua cape, pink gloves, pink boots, cleavage. cave, rock, mountain. blue collar
ultra high res, a cute girl, detailed, cg, zelda princess, AS Younger, cute pose
cute sister playing chess, DETAILED CHIBI, Magazine ad, iconic, 1940, from the movie, Cartoon, sharp focus. acrylic art on canvas by Disney and Clay Mann and ROSSDRAWS.
Highly detailed, High Quality, Masterpiece, beautiful, cute girl as toon plumber, blue cap, red overalls, Peach
|
Menouar/saqr-7b-merged | Menouar | "2024-02-16T09:20:31Z" | 2,887 | 1 | transformers | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"saqr-7b-instrcut",
"Pytorch",
"conversational",
"custom_code",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:openbmb/UltraFeedback",
"dataset:gsm8k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-02-16T09:10:53Z" | ---
library_name: transformers
tags:
- saqr-7b-instrcut
- Pytorch
license: apache-2.0
datasets:
- HuggingFaceH4/ultrachat_200k
- openbmb/UltraFeedback
- gsm8k
language:
- en
pipeline_tag: text-generation
---
# saqr-7b-merged
This model is a merged version of [**saqr-7b-instruct**](https://huggingface.co/Menouar/saqr-7b-instruct) with LoRA Adapters.
<img src="https://huggingface.co/Menouar/saqr-7b-instruct/resolve/main/saqr.jpg" alt="Saqr Logo" width="800" style="margin-left:auto; margin-right:auto; display:block;"/>
|
TIGER-Lab/Mantis-8B-Idefics2 | TIGER-Lab | "2024-05-23T03:50:29Z" | 2,887 | 8 | transformers | [
"transformers",
"safetensors",
"idefics2",
"pretraining",
"multimodal",
"lmm",
"vlm",
"llava",
"siglip",
"llama3",
"mantis",
"en",
"dataset:TIGER-Lab/Mantis-Instruct",
"arxiv:2405.01483",
"base_model:HuggingFaceM4/idefics2-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-05-23T03:02:47Z" | ---
license: apache-2.0
base_model: HuggingFaceM4/idefics2-8b
tags:
- multimodal
- lmm
- vlm
- llava
- siglip
- llama3
- mantis
model-index:
- name: mantis-8b-idefics2_8192
results: []
datasets:
- TIGER-Lab/Mantis-Instruct
language:
- en
---
# ๐ฅ Mantis
[Paper](https://arxiv.org/abs/2405.01483) | [Website](https://tiger-ai-lab.github.io/Mantis/) | [Github](https://github.com/TIGER-AI-Lab/Mantis) | [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) | [Demo](https://huggingface.co/spaces/TIGER-Lab/Mantis)
**Excited to announce Mantis-Idefics2, with enhanced ability in multi-image scenarios!**
It's fine-tuned on [Mantis-Instruct](https://huggingface.co/datasets/TIGER-Lab/Mantis-Instruct) from [Idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b)
## Summary
- Mantis-Idefics2 is an LMM with **interleaved text and image as inputs**, trained on Mantis-Instruct under academic-level resources (i.e. 36 hours on 16xA100-40G).
- Mantis is trained to have multi-image skills including co-reference, reasoning, comparing, temporal understanding.
- Mantis reaches the state-of-the-art performance on five multi-image benchmarks (NLVR2, Q-Bench, BLINK, MVBench, Mantis-Eval), and also maintain a strong single-image performance on par with CogVLM and Emu2.
## Multi-Image Performance
| Models | Size | Format | NLVR2 | Q-Bench | Mantis-Eval | BLINK | MVBench | Avg |
|--------------------|:----:|:--------:|:-----:|:-------:|:-----------:|:-----:|:-------:|:----:|
| GPT-4V | - | sequence | 88.80 | 76.52 | 62.67 | 51.14 | 43.50 | 64.5 |
| Open Source Models | | | | | | | | |
| Random | - | - | 48.93 | 40.20 | 23.04 | 38.09 | 27.30 | 35.5 |
| Kosmos2 | 1.6B | merge | 49.00 | 35.10 | 30.41 | 37.50 | 21.62 | 34.7 |
| LLaVA-v1.5 | 7B | merge | 53.88 | 49.32 | 31.34 | 37.13 | 36.00 | 41.5 |
| LLava-V1.6 | 7B | merge | 58.88 | 54.80 | 45.62 | 39.55 | 40.90 | 48.0 |
| Qwen-VL-Chat | 7B | merge | 58.72 | 45.90 | 39.17 | 31.17 | 42.15 | 43.4 |
| Fuyu | 8B | merge | 51.10 | 49.15 | 27.19 | 36.59 | 30.20 | 38.8 |
| BLIP-2 | 13B | merge | 59.42 | 51.20 | 49.77 | 39.45 | 31.40 | 46.2 |
| InstructBLIP | 13B | merge | 60.26 | 44.30 | 45.62 | 42.24 | 32.50 | 45.0 |
| CogVLM | 17B | merge | 58.58 | 53.20 | 45.16 | 41.54 | 37.30 | 47.2 |
| OpenFlamingo | 9B | sequence | 36.41 | 19.60 | 12.44 | 39.18 | 7.90 | 23.1 |
| Otter-Image | 9B | sequence | 49.15 | 17.50 | 14.29 | 36.26 | 15.30 | 26.5 |
| Idefics1 | 9B | sequence | 54.63 | 30.60 | 28.11 | 24.69 | 26.42 | 32.9 |
| VideoLLaVA | 7B | sequence | 56.48 | 45.70 | 35.94 | 38.92 | 44.30 | 44.3 |
| Emu2-Chat | 37B | sequence | 58.16 | 50.05 | 37.79 | 36.20 | 39.72 | 44.4 |
| Vila | 8B | sequence | 76.45 | 45.70 | 51.15 | 39.30 | 49.40 | 52.4 |
| Idefics2 | 8B | sequence | 86.87 | 57.00 | 48.85 | 45.18 | 29.68 | 53.5 |
| Mantis-CLIP | 8B | sequence | 84.66 | 66.00 | 55.76 | 47.06 | 48.30 | 60.4 |
| Mantis-SIGLIP | 8B | sequence | 87.43 | 69.90 | **59.45** | 46.35 | 50.15 | 62.7 |
| Mantis-Flamingo | 9B | sequence | 52.96 | 46.80 | 32.72 | 38.00 | 40.83 | 42.3 |
| Mantis-Idefics2 | 8B | sequence | **89.71** | **75.20** | 57.14 | **49.05** | **51.38** | **64.5** |
| $\Delta$ over SOTA | - | - | +2.84 | +18.20 | +8.30 | +3.87 | +1.98 | +11.0 |
## Single-Image Performance
| Model | Size | TextVQA | VQA | MMB | MMMU | OKVQA | SQA | MathVista | Avg |
|-----------------|:----:|:-------:|:----:|:----:|:----:|:-----:|:----:|:---------:|:----:|
| OpenFlamingo | 9B | 46.3 | 58.0 | 32.4 | 28.7 | 51.4 | 45.7 | 18.6 | 40.2 |
| Idefics1 | 9B | 39.3 | 68.8 | 45.3 | 32.5 | 50.4 | 51.6 | 21.1 | 44.1 |
| InstructBLIP | 7B | 33.6 | 75.2 | 38.3 | 30.6 | 45.2 | 70.6 | 24.4 | 45.4 |
| Yi-VL | 6B | 44.8 | 72.5 | 68.4 | 39.1 | 51.3 | 71.7 | 29.7 | 53.9 |
| Qwen-VL-Chat | 7B | 63.8 | 78.2 | 61.8 | 35.9 | 56.6 | 68.2 | 15.5 | 54.3 |
| LLaVA-1.5 | 7B | 58.2 | 76.6 | 64.8 | 35.3 | 53.4 | 70.4 | 25.6 | 54.9 |
| Emu2-Chat | 37B | <u>66.6</u> | **84.9** | 63.6 | 36.3 | **64.8** | 65.3 | 30.7 | 58.9 |
| CogVLM | 17B | **70.4** | <u>82.3</u> | 65.8 | 32.1 | <u>64.8</u> | 65.6 | 35.0 | 59.4 |
| Idefics2 | 8B | 70.4 | 79.1 | <u>75.7</u> | **43.0** | 53.5 | **86.5** | **51.4** | **65.7** |
| Mantis-CLIP | 8B | 56.4 | 73.0 | 66.0 | 38.1 | 53.0 | 73.8 | 31.7 | 56.0 |
| Mantis-SigLIP | 8B | 59.2 | 74.9 | 68.7 | 40.1 | 55.4 | 74.9 | 34.4 | 58.2 |
| Mantis-Idefics2 | 8B | 63.5 | 77.6 | 75.7 | <u>41.1</u> | 52.6 | <u>81.3</u> | <u>40.4</u> | <u>61.7</u> |
## How to use
### Run example inference:
```python
import requests
import torch
from PIL import Image
from io import BytesIO
from transformers import AutoProcessor, AutoModelForVision2Seq
from transformers.image_utils import load_image
processor = AutoProcessor.from_pretrained("TIGER-Lab/Mantis-8B-Idefics2") # do_image_splitting is False by default
model = AutoModelForVision2Seq.from_pretrained(
"TIGER-Lab/Mantis-8B-Idefics2",
device_map="auto"
)
generation_kwargs = {
"max_new_tokens": 1024,
"num_beams": 1,
"do_sample": False
}
# Note that passing the image urls (instead of the actual pil images) to the processor is also possible
image1 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
image2 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
images = [image1, image2, image3]
query1 = "What cities image 1, image 2, and image 3 belong to respectively? Answer me in order."
query2 = "Which one do you recommend for a visit? and why?"
query3 = "Which picture has most cars in it?"
### Chat
### Round 1
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "image"},
{"type": "image"},
{"type": "text", "text": query1},
]
}
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=images, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
# Generate
generated_ids = model.generate(**inputs, **generation_kwargs)
response = processor.batch_decode(generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print("User: ", query1)
print("ASSISTANT: ", response[0])
### Round 2
messages.append(
{
"role": "assistant",
"content": [
{"type": "text", "text": response[0]},
]
}
)
messages.append(
{
"role": "user",
"content": [
{"type": "text", "text": query2},
]
}
)
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=images, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
generated_ids = model.generate(**inputs, **generation_kwargs)
response = processor.batch_decode(generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print("User: ", query2)
print("ASSISTANT: ", response[0])
### Round 3
messages.append(
{
"role": "assistant",
"content": [
{"type": "text", "text": response[0]},
]
}
)
messages.append(
{
"role": "user",
"content": [
{"type": "text", "text": query3},
]
}
)
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=images, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
generated_ids = model.generate(**inputs, **generation_kwargs)
response = processor.batch_decode(generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print("User: ", query3)
print("ASSISTANT: ", response[0])
"""
User: What cities image 1, image 2, and image 3 belong to respectively? Answer me in order.
ASSISTANT: Chicago, New York, San Francisco
User: Which one do you recommend for a visit? and why?
ASSISTANT: New York - because it's a bustling metropolis with iconic landmarks like the Statue of Liberty and the Empire State Building.
User: Which picture has most cars in it?
ASSISTANT: Image 3
"""
```
### Training
See [mantis/train](https://github.com/TIGER-AI-Lab/Mantis/tree/main/mantis/train) for details
### Evaluation
See [mantis/benchmark](https://github.com/TIGER-AI-Lab/Mantis/tree/main/mantis/benchmark) for details
**Please cite our paper or give a star to out Github repo if you find this model useful**
## Citation
```
@inproceedings{Jiang2024MANTISIM,
title={MANTIS: Interleaved Multi-Image Instruction Tuning},
author={Dongfu Jiang and Xuan He and Huaye Zeng and Cong Wei and Max W.F. Ku and Qian Liu and Wenhu Chen},
publisher={arXiv2405.01483}
year={2024},
}
``` |
mradermacher/Falcon2-8B-Romanian-GGUF | mradermacher | "2024-06-05T17:28:34Z" | 2,884 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"ro",
"base_model:ssmits/Falcon2-8B-Romanian",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-05T17:00:15Z" | ---
base_model: ssmits/Falcon2-8B-Romanian
language:
- ro
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ssmits/Falcon2-8B-Romanian
<!-- 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/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.IQ3_XS.gguf) | IQ3_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q5_K_M.gguf) | Q5_K_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.Q8_0.gguf) | Q8_0 | 8.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Falcon2-8B-Romanian-GGUF/resolve/main/Falcon2-8B-Romanian.f16.gguf) | f16 | 16.3 | 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 -->
|
mradermacher/threebird-scribe-alpha0.2-7B-GGUF | mradermacher | "2024-06-08T22:03:15Z" | 2,884 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Lambent/threebird-scribe-alpha0.2-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-08T21:06:21Z" | ---
base_model: Lambent/threebird-scribe-alpha0.2-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Lambent/threebird-scribe-alpha0.2-7B
<!-- 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/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/threebird-scribe-alpha0.2-7B-GGUF/resolve/main/threebird-scribe-alpha0.2-7B.f16.gguf) | f16 | 14.6 | 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 -->
|
keremberke/yolov8s-plane-detection | keremberke | "2024-05-23T12:00:17Z" | 2,883 | 4 | ultralytics | [
"ultralytics",
"tensorboard",
"v8",
"ultralyticsplus",
"yolov8",
"yolo",
"vision",
"object-detection",
"pytorch",
"awesome-yolov8-models",
"dataset:keremberke/plane-detection",
"license:agpl-3.0",
"model-index",
"region:us"
] | object-detection | "2023-01-29T06:42:07Z" | ---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/plane-detection
model-index:
- name: keremberke/yolov8s-plane-detection
results:
- task:
type: object-detection
dataset:
type: keremberke/plane-detection
name: plane-detection
split: validation
metrics:
- type: precision
value: 0.995
name: [email protected](box)
license: agpl-3.0
---
<div align="center">
<img width="640" alt="keremberke/yolov8s-plane-detection" src="https://huggingface.co/keremberke/yolov8s-plane-detection/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['planes']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8s-plane-detection')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
turboderp/Cat-Llama-3-70B-instruct | turboderp | "2024-05-08T19:29:00Z" | 2,881 | 47 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-05T09:57:47Z" | ---
license: llama3
---
# Cat-llama3-instruct
## Abstract
We present cat llama3 instruct, a llama 3 70b finetuned model focusing on system prompt fidelity, helpfulness and character engagement. The model aims to respect system prompt to an extreme degree, and provide helpful information regardless of situations and offer maximum character immersion(Role Play) in given scenes.
## Introduction
Llama 3 70b provides a brand new platform thatโs more knowledgeable and steerable than the previous generations of products. However, there currently lacks general purpose finetunes for the 70b version model. Cat-llama3-instruct 70b aims to address the shortcomings of traditional models by applying heavy filtrations for helpfulness, summarization for system/character card fidelity, and paraphrase for character immersion.
Specific Aims:
* System Instruction fidelity
* Chain of Thought(COT)
* Character immersion
* Helpfulness for biosciences and general science
## Methods
*Dataset Preparation
Huggingface dataset containing instruction-response pairs was systematically pulled. We have trained a gpt model on gpt4 responses exclusively to serve as a standard model.
(Fig1. Huggingface dataset population distribution and filtration for each component)
For each pulled record, we measure the perplexity of the entry against the gpt4 trained model, and select for specifically GPT-4 quality dataset.
We note that a considerable amount of GPT-4 responses contain refusals. A bert model was trained on refusals to classify the records.
For each entry, we score it for quality&helpfulness(Y) and refusals(X). A main population is retrieved and we note that refusals stop at ~20% refusal score. Thus all subsequent dataset processing has the 20% portion dropped
We further filter for length and COT responses:
(Fig 2. COT responses are all >50 tokens single turn)
All training records use at least one correlative. Most of the training records contain two or more thought process(COT)
Due to the nature of my research, I also pulled a significant amount of data from Chat Doctor, favouring detailed and step by step diagnosis.
(Fig 3. Health related tasks are all >100 tokens, peaking at 450 tokens single turn)
For each record, a detailed system card is generated to reflect the exact place, time and people/their appearance/personality(if applicable).
* Model Training
16*a100 for 14 days, 4 epochs,
## Usage
* BOS, CHATML
Please make sure you use chatml with llama3 BOS:
```
<|begin_of_text|><|im_start|>system
The following is a conversation between an user and a cat having dual degrees in biomedical sciences and quantum physics. The cat ends every its response with Nyan and does cute actions<|im_end|>
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant
Cat: Nyan! Hello there! How are you? It's always great to meet new friends. My fur is a bit ruffled, but I'm feeling good today. How about you? Is there anything you'd like to talk about or ask? I'm not the best at answering questions, but I'll do my best to help out. After all, that's what friends are for, right? Let's chat and have a fun time together! Nyan!<|im_end|>
```
## Showcases:
The model does COT in grey, calculated response in black; note that this behaviour is done through system card instruction to showcase the systemcard fidelity, it is not fine tuned into the model.
(Fig 4. Showcasing model doing COT to solve difficult tasks, extend and enrich its own answers)
## Discord server
Feel free to visit the [ExLlama discord server](https://discord.gg/mUvR4tQfhV) if you have any questions. |
RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf | RichardErkhov | "2024-06-24T21:30:03Z" | 2,881 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-24T21:22:14Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1 - GGUF
- Model creator: https://huggingface.co/habanoz/
- Original model: https://huggingface.co/habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q2_K.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q2_K.gguf) | Q2_K | 0.4GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K.gguf) | Q3_K | 0.51GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_0.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_0.gguf) | Q4_0 | 0.59GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_K.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_K.gguf) | Q4_K | 0.62GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_1.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q4_1.gguf) | Q4_1 | 0.65GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_0.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_0.gguf) | Q5_0 | 0.71GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_K.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_K.gguf) | Q5_K | 0.73GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_1.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q5_1.gguf) | Q5_1 | 0.77GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q6_K.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q6_K.gguf) | Q6_K | 0.84GB |
| [TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q8_0.gguf](https://huggingface.co/RichardErkhov/habanoz_-_TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1-gguf/blob/main/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
language:
- en
license: apache-2.0
datasets:
- OpenAssistant/oasst_top1_2023-08-25
pipeline_tag: text-generation
model-index:
- name: TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 31.4
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 54.24
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.36
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 42.47
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 57.7
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 1.36
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1
name: Open LLM Leaderboard
---
TinyLlama-1.1B-intermediate-step-715k-1.5T finetuned using OpenAssistant/oasst_top1_2023-08-25 dataset.
Qlora is used. Adapter is merged.
SFT code:
https://github.com/habanoz/qlora.git
Command used:
```bash
accelerate launch $BASE_DIR/qlora/train.py \
--model_name_or_path $BASE_MODEL \
--working_dir $BASE_DIR/$OUTPUT_NAME-checkpoints \
--output_dir $BASE_DIR/$OUTPUT_NAME-peft \
--merged_output_dir $BASE_DIR/$OUTPUT_NAME \
--final_output_dir $BASE_DIR/$OUTPUT_NAME-final \
--num_train_epochs 3 \
--logging_steps 1 \
--save_strategy steps \
--save_steps 75 \
--save_total_limit 2 \
--data_seed 11422 \
--evaluation_strategy steps \
--per_device_eval_batch_size 4 \
--eval_dataset_size 0.01 \
--eval_steps 75 \
--max_new_tokens 1024 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--do_train \
--do_eval \
--lora_r 64 \
--lora_alpha 16 \
--lora_modules all \
--bits 4 \
--double_quant \
--quant_type nf4 \
--lr_scheduler_type constant \
--dataset oasst1-top1 \
--dataset_format oasst1 \
--model_max_len 1024 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--learning_rate 1e-5 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--lora_dropout 0.0 \
--weight_decay 0.0 \
--seed 11422 \
--gradient_checkpointing \
--use_flash_attention_2 \
--ddp_find_unused_parameters False
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-intermediate-step-715k-1.5T-lr-5-3epochs-oasst1-top1-instruct-V1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |35.42|
|AI2 Reasoning Challenge (25-Shot)|31.40|
|HellaSwag (10-Shot) |54.24|
|MMLU (5-Shot) |25.36|
|TruthfulQA (0-shot) |42.47|
|Winogrande (5-shot) |57.70|
|GSM8k (5-shot) | 1.36|
|
larenspear/llama2-13b-WildJailbreak-Q6_K-GGUF | larenspear | "2024-06-30T23:23:46Z" | 2,881 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:larenspear/copy_of_wildjailbreak_13",
"license:apache-2.0",
"region:us"
] | null | "2024-06-30T22:35:06Z" | ---
base_model: larenspear/copy_of_wildjailbreak_13
language:
- en
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
extra_gated_prompt: Access to this model is automatically granted upon accepting the
[AI2 Responsible Use Guidelines](https://allenai.org/responsible-use.pdf), and completing
all fields below
extra_gated_fields:
Your full name: text
Organization or entity you are affiliated with: text
State or country you are located in: text
Contact email: text
Please describe your intended use of the low risk artifact(s): text
I understand that this model is a research artifact that may contain or produce unfiltered, toxic, or harmful material: checkbox
I agree to use this model for research purposes in accordance with the AI2 Responsible Use Guidelines: checkbox
I agree that AI2 may use my information as described in the Privacy Policy: checkbox
I certify that the information I have provided is true and accurate: checkbox
---
# larenspear/copy_of_wildjailbreak_13-Q6_K-GGUF
This model was converted to GGUF format from [`larenspear/copy_of_wildjailbreak_13`](https://huggingface.co/larenspear/copy_of_wildjailbreak_13) 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/larenspear/copy_of_wildjailbreak_13) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo larenspear/copy_of_wildjailbreak_13-Q6_K-GGUF --hf-file copy_of_wildjailbreak_13-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo larenspear/copy_of_wildjailbreak_13-Q6_K-GGUF --hf-file copy_of_wildjailbreak_13-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo larenspear/copy_of_wildjailbreak_13-Q6_K-GGUF --hf-file copy_of_wildjailbreak_13-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo larenspear/copy_of_wildjailbreak_13-Q6_K-GGUF --hf-file copy_of_wildjailbreak_13-q6_k.gguf -c 2048
```
|
urchade/gliner_medium-v1 | urchade | "2024-05-07T15:54:25Z" | 2,879 | 5 | gliner | [
"gliner",
"pytorch",
"dataset:Universal-NER/Pile-NER-type",
"arxiv:2311.08526",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-03-09T14:50:41Z" | ---
license: cc-by-nc-4.0
datasets:
- Universal-NER/Pile-NER-type
library_name: gliner
---
# Model Card for GLiNER-M
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
This version has been trained on the **Pile-NER** dataset (Research purpose)
## Links
* Paper: https://arxiv.org/abs/2311.08526
* Repository: https://github.com/urchade/GLiNER
## Available models
| Release | Model Name | # of Parameters | Language | License |
| - | - | - | - | - |
| v0 | [urchade/gliner_base](https://huggingface.co/urchade/gliner_base)<br>[urchade/gliner_multi](https://huggingface.co/urchade/gliner_multi) | 209M<br>209M | English<br>Multilingual | cc-by-nc-4.0 |
| v1 | [urchade/gliner_small-v1](https://huggingface.co/urchade/gliner_small-v1)<br>[urchade/gliner_medium-v1](https://huggingface.co/urchade/gliner_medium-v1)<br>[urchade/gliner_large-v1](https://huggingface.co/urchade/gliner_large-v1) | 166M<br>209M<br>459M | English <br> English <br> English | cc-by-nc-4.0 |
| v2 | [urchade/gliner_small-v2](https://huggingface.co/urchade/gliner_small-v2)<br>[urchade/gliner_medium-v2](https://huggingface.co/urchade/gliner_medium-v2)<br>[urchade/gliner_large-v2](https://huggingface.co/urchade/gliner_large-v2) | 166M<br>209M<br>459M | English <br> English <br> English | apache-2.0 |
| v2.1 | [urchade/gliner_small-v2.1](https://huggingface.co/urchade/gliner_small-v2.1)<br>[urchade/gliner_medium-v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)<br>[urchade/gliner_large-v2.1](https://huggingface.co/urchade/gliner_large-v2.1) <br>[urchade/gliner_multi-v2.1](https://huggingface.co/urchade/gliner_multi-v2.1) | 166M<br>209M<br>459M<br>209M | English <br> English <br> English <br> Multilingual | apache-2.0 |
## Installation
To use this model, you must install the GLiNER Python library:
```
!pip install gliner
```
## Usage
Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
```python
from gliner import GLiNER
model = GLiNER.from_pretrained("urchade/gliner_medium-v1")
text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kษพiสหtjษnu สษหnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""
labels = ["person", "award", "date", "competitions", "teams"]
entities = model.predict_entities(text, labels)
for entity in entities:
print(entity["text"], "=>", entity["label"])
```
```
Cristiano Ronaldo dos Santos Aveiro => person
5 February 1985 => date
Al Nassr => teams
Portugal national team => teams
Ballon d'Or => award
UEFA Men's Player of the Year Awards => award
European Golden Shoes => award
UEFA Champions Leagues => competitions
UEFA European Championship => competitions
UEFA Nations League => competitions
Champions League => competitions
European Championship => competitions
```
## Named Entity Recognition benchmark result
## Model Authors
The model authors are:
* [Urchade Zaratiana](https://huggingface.co/urchade)
* Nadi Tomeh
* Pierre Holat
* Thierry Charnois
## Citation
```bibtex
@misc{zaratiana2023gliner,
title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
year={2023},
eprint={2311.08526},
archivePrefix={arXiv},
primaryClass={cs.CL}
} |
RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf | RichardErkhov | "2024-06-25T22:00:44Z" | 2,878 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-25T21:33:58Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged - GGUF
- Model creator: https://huggingface.co/SebastianSchramm/
- Original model: https://huggingface.co/SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q2_K.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q2_K.gguf) | Q2_K | 0.4GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ3_S.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ3_M.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K.gguf) | Q3_K | 0.51GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_0.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_0.gguf) | Q4_0 | 0.59GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_K.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_K.gguf) | Q4_K | 0.62GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_1.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q4_1.gguf) | Q4_1 | 0.65GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_0.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_0.gguf) | Q5_0 | 0.71GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_K.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_K.gguf) | Q5_K | 0.73GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_1.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q5_1.gguf) | Q5_1 | 0.77GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q6_K.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q6_K.gguf) | Q6_K | 0.84GB |
| [tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q8_0.gguf](https://huggingface.co/RichardErkhov/SebastianSchramm_-_tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged-gguf/blob/main/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora-merged.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
license: mit
language:
- en
---
## Model description
- **Model type:** A 1.1B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily English
- **License:** MIT
- **Finetuned from model:** [PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T)
|
timm/vit_base_patch16_224.orig_in21k_ft_in1k | timm | "2023-05-06T00:00:51Z" | 2,877 | 2 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-22T07:26:58Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-21k
---
# Model card for vit_base_patch16_224.orig_in21k_ft_in1k
A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k in JAX by paper authors, ported to PyTorch by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 86.6
- GMACs: 16.9
- Activations (M): 16.5
- Image size: 224 x 224
- **Papers:**
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-21k
- **Original:** https://github.com/google-research/vision_transformer
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch16_224.orig_in21k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_224.orig_in21k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
timm/swinv2_base_window8_256.ms_in1k | timm | "2024-02-10T23:30:54Z" | 2,877 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:mit",
"region:us"
] | image-classification | "2023-03-18T03:28:54Z" | ---
license: mit
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
---
# Model card for swinv2_base_window8_256.ms_in1k
A Swin Transformer V2 image classification model. Pretrained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 87.9
- GMACs: 20.4
- Activations (M): 52.6
- Image size: 256 x 256
- **Papers:**
- Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
- **Original:** https://github.com/microsoft/Swin-Transformer
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('swinv2_base_window8_256.ms_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_base_window8_256.ms_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g. for swin_base_patch4_window7_224 (NHWC output)
# torch.Size([1, 56, 56, 128])
# torch.Size([1, 28, 28, 256])
# torch.Size([1, 14, 14, 512])
# torch.Size([1, 7, 7, 1024])
# e.g. for swinv2_cr_small_ns_224 (NCHW output)
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_base_window8_256.ms_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled (ie.e a (batch_size, H, W, num_features) tensor for swin / swinv2
# or (batch_size, num_features, H, W) for swinv2_cr
output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{liu2021swinv2,
title={Swin Transformer V2: Scaling Up Capacity and Resolution},
author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Undi95/Emerhyst-13B | Undi95 | "2023-09-27T15:23:59Z" | 2,877 | 16 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-27T14:24:50Z" | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
13B version of [Undi95/Emerhyst-20B](https://huggingface.co/Undi95/Emerhyst-20B), consider using the 20B if you have the power to. This one should be a downgrade but usable on lower spec.
Merge of [Amethyst 13B](https://huggingface.co/Undi95/Amethyst-13B) and [Emerald 13B](https://huggingface.co/Undi95/Emerald-13B).
In addition, [LimaRP v3](https://huggingface.co/lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT) was used, is it recommanded to read the documentation.
<!-- description start -->
## Description
This repo contains fp16 files of Emerhyst-13B.
<!-- description end -->
<!-- description start -->
## Models and loras used
- PygmalionAI/pygmalion-2-13b
- Xwin-LM/Xwin-LM-13B-V0.1
- The-Face-Of-Goonery/Huginn-13b-FP16
- zattio770/120-Days-of-LORA-v2-13B
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## LimaRP v3 usage and suggested settings
You can follow these instruction format settings in SillyTavern. Replace tiny with your desired response length:
Special thanks to Sushi.
If you want to support me, you can [here](https://ko-fi.com/undiai). |
MBZUAI/LaMini-GPT-774M | MBZUAI | "2023-04-28T13:07:40Z" | 2,876 | 12 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"arxiv:2304.14402",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-04-15T06:02:39Z" | ---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
widget:
- text: >-
Below is an instruction that describes a task.
Write a response that appropriately completes the request.
### Instruction:
how can I become more healthy?
### Response:
example_title: example
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-GPT-774M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)".
This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with โฉ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>โฉ</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>โฉ</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>โฉ</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>โฉ</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>โฉ</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>โฉ</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to respond to human instructions written in natural language.
Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
See the example on the right or the code below.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [gpt2-large](https://huggingface.co/gpt2-large) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 774M.
### Training Hyperparameters
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
``` |
MCG-NJU/videomae-small-finetuned-kinetics | MCG-NJU | "2023-04-22T11:39:50Z" | 2,876 | 1 | transformers | [
"transformers",
"pytorch",
"videomae",
"video-classification",
"vision",
"arxiv:2203.12602",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | "2023-04-16T11:11:37Z" | ---
license: "cc-by-nc-4.0"
tags:
- vision
- video-classification
---
# VideoMAE (small-sized model, fine-tuned on Kinetics-400)
VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and first released in [this repository](https://github.com/MCG-NJU/VideoMAE).
Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
VideoMAE is an extension of [Masked Autoencoders (MAE)](https://arxiv.org/abs/2111.06377) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches.
Videos are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video.
## Intended uses & limitations
You can use the raw model for video classification into one of the 400 possible Kinetics-400 labels.
### How to use
Here is how to use this model to classify a video:
```python
from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification
import numpy as np
import torch
video = list(np.random.randn(16, 3, 224, 224))
processor = VideoMAEImageProcessor.from_pretrained("MCG-NJU/videomae-small-finetuned-kinetics")
model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-small-finetuned-kinetics")
inputs = processor(video, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/videomae.html#).
## Training data
(to do, feel free to open a PR)
## Training procedure
### Preprocessing
(to do, feel free to open a PR)
### Pretraining
(to do, feel free to open a PR)
## Evaluation results
This model obtains a top-1 accuracy of 79.0 and a top-5 accuracy of 93.8 on the test set of Kinetics-400.
### BibTeX entry and citation info
```bibtex
misc{https://doi.org/10.48550/arxiv.2203.12602,
doi = {10.48550/ARXIV.2203.12602},
url = {https://arxiv.org/abs/2203.12602},
author = {Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |
Yntec/Astro_-_-Gemu | Yntec | "2023-12-09T17:57:59Z" | 2,876 | 3 | diffusers | [
"diffusers",
"safetensors",
"3D Art",
"Style",
"Render",
"theAstroBruh",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-12-09T17:22:13Z" | ---
language:
- en
license: creativeml-openrail-m
tags:
- 3D Art
- Style
- Render
- theAstroBruh
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Astro~Gemรผ
Original page: https://civitai.com/models/112302/astrogemu?modelVersionId=121225
Samples and prompts:
(Click for larger)
Top left: two tiny burgers and a bunch of french fries, centered, simple background, | depth of field, bokeh, | smooth detailed shadows, hyperealistic shadows, (saturated colors) | (game cg, unreal engine, pixar style),
Top right: a close up portrait photo of pretty cute girl with mushroom themed cake, long haircut, pale skin, background is city ruins, high quality pie, dslr, soft lighting, high quality, film grain, Fujifilm XT3
Bottom left: a pretty cute girl in a red and white dress holding basket full of cherries and a red and white skirt by wess anderson
Bottom right: highly detailed chibi donkey in kitchen, hammer chef, fish, dungeons and dragons
|
RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf | RichardErkhov | "2024-06-06T12:15:57Z" | 2,876 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-06T09:31:57Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gpt2-xl-alpaca - GGUF
- Model creator: https://huggingface.co/Rachneet/
- Original model: https://huggingface.co/Rachneet/gpt2-xl-alpaca/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gpt2-xl-alpaca.Q2_K.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q2_K.gguf) | Q2_K | 0.84GB |
| [gpt2-xl-alpaca.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.IQ3_XS.gguf) | IQ3_XS | 0.84GB |
| [gpt2-xl-alpaca.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.IQ3_S.gguf) | IQ3_S | 0.84GB |
| [gpt2-xl-alpaca.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q3_K_S.gguf) | Q3_K_S | 0.84GB |
| [gpt2-xl-alpaca.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.IQ3_M.gguf) | IQ3_M | 0.91GB |
| [gpt2-xl-alpaca.Q3_K.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q3_K.gguf) | Q3_K | 0.97GB |
| [gpt2-xl-alpaca.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q3_K_M.gguf) | Q3_K_M | 0.97GB |
| [gpt2-xl-alpaca.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q3_K_L.gguf) | Q3_K_L | 1.03GB |
| [gpt2-xl-alpaca.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.IQ4_XS.gguf) | IQ4_XS | 0.9GB |
| [gpt2-xl-alpaca.Q4_0.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q4_0.gguf) | Q4_0 | 0.91GB |
| [gpt2-xl-alpaca.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.IQ4_NL.gguf) | IQ4_NL | 0.91GB |
| [gpt2-xl-alpaca.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q4_K_S.gguf) | Q4_K_S | 1.04GB |
| [gpt2-xl-alpaca.Q4_K.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q4_K.gguf) | Q4_K | 1.11GB |
| [gpt2-xl-alpaca.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q4_K_M.gguf) | Q4_K_M | 1.11GB |
| [gpt2-xl-alpaca.Q4_1.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q4_1.gguf) | Q4_1 | 1.0GB |
| [gpt2-xl-alpaca.Q5_0.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q5_0.gguf) | Q5_0 | 1.09GB |
| [gpt2-xl-alpaca.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q5_K_S.gguf) | Q5_K_S | 1.15GB |
| [gpt2-xl-alpaca.Q5_K.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q5_K.gguf) | Q5_K | 1.29GB |
| [gpt2-xl-alpaca.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q5_K_M.gguf) | Q5_K_M | 1.29GB |
| [gpt2-xl-alpaca.Q5_1.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q5_1.gguf) | Q5_1 | 1.18GB |
| [gpt2-xl-alpaca.Q6_K.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q6_K.gguf) | Q6_K | 1.52GB |
| [gpt2-xl-alpaca.Q8_0.gguf](https://huggingface.co/RichardErkhov/Rachneet_-_gpt2-xl-alpaca-gguf/blob/main/gpt2-xl-alpaca.Q8_0.gguf) | Q8_0 | 1.63GB |
Original model description:
Entry not found
|
bofenghuang/vigogne-2-7b-chat | bofenghuang | "2023-10-16T14:03:25Z" | 2,875 | 24 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"LLM",
"llama-2",
"finetuned",
"conversational",
"fr",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-07-29T21:16:01Z" | ---
license: llama2
language: fr
pipeline_tag: text-generation
inference: false
tags:
- LLM
- llama-2
- finetuned
---
<p align="center" width="100%">
<img src="https://huggingface.co/bofenghuang/vigogne-2-7b-chat/resolve/v2.0/logo_v2.jpg" alt="Vigogne" style="width: 30%; min-width: 300px; display: block; margin: auto;">
</p>
# Vigogne-2-7B-Chat-V2.0: A Llama-2-based French Chat LLM
Vigogne-2-7B-Chat-V2.0 is a French chat LLM, based on [LLaMA-2-7B](https://ai.meta.com/llama), optimized to generate helpful and coherent responses in conversations with users.
Check out our [release blog](https://github.com/bofenghuang/vigogne/blob/main/blogs/2023-08-17-vigogne-chat-v2_0.md) and [GitHub repository](https://github.com/bofenghuang/vigogne) for more information.
**Usage and License Notices**: Vigogne-2-7B-Chat-V2.0 follows Llama-2's [usage policy](https://ai.meta.com/llama/use-policy). A significant portion of the training data is distilled from GPT-3.5-Turbo and GPT-4, kindly use it cautiously to avoid any violations of OpenAI's [terms of use](https://openai.com/policies/terms-of-use).
## Changelog
All previous versions are accessible through branches.
- **V1.0**: Trained on 420K chat data.
- **V2.0**: Trained on 520K data. Check out our [release blog](https://github.com/bofenghuang/vigogne/blob/main/blogs/2023-08-17-vigogne-chat-v2_0.md) for more details.
## Prompt Template
We utilized prefix tokens `<user>:` and `<assistant>:` to distinguish between user and assistant utterances.
You can apply this formatting using the [chat template](https://huggingface.co/docs/transformers/main/chat_templating) through the `apply_chat_template()` method.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bofenghuang/vigogne-2-7b-chat")
conversation = [
{"role": "user", "content": "Bonjour ! Comment รงa va aujourd'hui ?"},
{"role": "assistant", "content": "Bonjour ! Je suis une IA, donc je n'ai pas de sentiments, mais je suis prรชt ร vous aider. Comment puis-je vous assister aujourd'hui ?"},
{"role": "user", "content": "Quelle est la hauteur de la Tour Eiffel ?"},
{"role": "assistant", "content": "La Tour Eiffel mesure environ 330 mรจtres de hauteur."},
{"role": "user", "content": "Comment monter en haut ?"},
]
print(tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True))
```
You will get
```
<s><|system|>: Vous รชtes Vigogne, un assistant IA crรฉรฉ par Zaion Lab. Vous suivez extrรชmement bien les instructions. Aidez autant que vous le pouvez.
<|user|>: Bonjour ! Comment รงa va aujourd'hui ?
<|assistant|>: Bonjour ! Je suis une IA, donc je n'ai pas de sentiments, mais je suis prรชt ร vous aider. Comment puis-je vous assister aujourd'hui ?</s>
<|user|>: Quelle est la hauteur de la Tour Eiffel ?
<|assistant|>: La Tour Eiffel mesure environ 330 mรจtres de hauteur.</s>
<|user|>: Comment monter en haut ?
<|assistant|>:
```
## Usage
### Inference using the quantized versions
The quantized versions of this model are generously provided by [TheBloke](https://huggingface.co/TheBloke)!
- AWQ for GPU inference: [TheBloke/Vigogne-2-7B-Chat-AWQ](https://huggingface.co/TheBloke/Vigogne-2-7B-Chat-AWQ)
- GTPQ for GPU inference: [TheBloke/Vigogne-2-7B-Chat-GPTQ](https://huggingface.co/TheBloke/Vigogne-2-7B-Chat-GPTQ)
- GGUF for CPU+GPU inference: [TheBloke/Vigogne-2-7B-Chat-GGUF](https://huggingface.co/TheBloke/Vigogne-2-7B-Chat-GGUF)
These versions facilitate testing and development with various popular frameworks, including [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [vLLM](https://github.com/vllm-project/vllm), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [llama.cpp](https://github.com/ggerganov/llama.cpp), [text-generation-webui](https://github.com/oobabooga/text-generation-webui), and more.
### Inference using the unquantized model with ๐ค Transformers
```python
from typing import Dict, List, Optional
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TextStreamer
model_name_or_path = "bofenghuang/vigogne-2-7b-chat"
revision = "v2.0"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, revision=revision, padding_side="right", use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, revision=revision, torch_dtype=torch.float16, device_map="auto")
streamer = TextStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
def chat(
query: str,
history: Optional[List[Dict]] = None,
temperature: float = 0.7,
top_p: float = 1.0,
top_k: float = 0,
repetition_penalty: float = 1.1,
max_new_tokens: int = 1024,
**kwargs,
):
if history is None:
history = []
history.append({"role": "user", "content": query})
input_ids = tokenizer.apply_chat_template(history, add_generation_prompt=True, return_tensors="pt").to(model.device)
input_length = input_ids.shape[1]
generated_outputs = model.generate(
input_ids=input_ids,
generation_config=GenerationConfig(
temperature=temperature,
do_sample=temperature > 0.0,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
max_new_tokens=max_new_tokens,
pad_token_id=tokenizer.eos_token_id,
**kwargs,
),
streamer=streamer,
return_dict_in_generate=True,
)
generated_tokens = generated_outputs.sequences[0, input_length:]
generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
history.append({"role": "assistant", "content": generated_text})
return generated_text, history
# 1st round
response, history = chat("Un escargot parcourt 100 mรจtres en 5 heures. Quelle est sa vitesse ?", history=None)
# 2nd round
response, history = chat("Quand il peut dรฉpasser le lapin ?", history=history)
# 3rd round
response, history = chat("รcris une histoire imaginative qui met en scรจne une compรฉtition de course entre un escargot et un lapin.", history=history)
```
You can also use the Google Colab Notebook provided below.
<a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_chat.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Inference using the unquantized model with vLLM
Set up an OpenAI-compatible server with the following command:
```bash
# Install vLLM
# This may take 5-10 minutes.
# pip install vllm
# Start server for Vigogne-Chat models
python -m vllm.entrypoints.openai.api_server --model bofenghuang/vigogne-2-7b-chat
# List models
# curl http://localhost:8000/v1/models
```
Query the model using the openai python package.
```python
import openai
# Modify OpenAI's API key and API base to use vLLM's API server.
openai.api_key = "EMPTY"
openai.api_base = "http://localhost:8000/v1"
# First model
models = openai.Model.list()
model = models["data"][0]["id"]
# Chat completion API
chat_completion = openai.ChatCompletion.create(
model=model,
messages=[
{"role": "user", "content": "Parle-moi de toi-mรชme."},
],
max_tokens=1024,
temperature=0.7,
)
print("Chat completion results:", chat_completion)
```
## Limitations
Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.
|
digiplay/2.5DSET_diffusers | digiplay | "2023-07-10T07:04:59Z" | 2,874 | 3 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-05-28T22:03:05Z" | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/18634?modelVersionId=22116
|
nidhi9/sql_classifier | nidhi9 | "2024-04-24T09:20:16Z" | 2,874 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-01-08T20:48:28Z" | Entry not found |
hvein/5G93jx19YRPUa6R9ZJq81TccCY7TwDkeEpj39axgCdFttzuW_vgg | hvein | "2024-03-09T20:41:14Z" | 2,872 | 0 | keras | [
"keras",
"region:us"
] | null | "2024-02-13T20:18:02Z" | Entry not found |
ai-forever/sage-fredt5-distilled-95m | ai-forever | "2024-04-18T15:17:42Z" | 2,872 | 6 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"spellchecking",
"pytorch",
"natural language generation",
"ru",
"dataset:ai-forever/spellcheck_punctuation_benchmark",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2024-03-11T09:00:12Z" | ---
language:
- ru
tags:
- spellchecking
- pytorch
- natural language generation
license: mit
metrics:
- precision
- recall
- f1
library_name: transformers
model-index:
- name: sage-fredt5-distilled-95m
results:
- task:
type: text-generation
dataset:
type: spellcheck_benchmark
name: RUSpellRU (spell&punct)
metrics:
- name: F1 (spell)
type: f1_spell
value: 78.9
verified: false
- name: F1 (punct)
type: f1_punct
value: 83.6
verified: false
- name: F1 (case)
type: f1_case
value: 93.5
verified: false
- task:
type: text-generation
dataset:
type: spellcheck_benchmark
name: MultidomainGold (spell&punct)
metrics:
- name: F1 (spell)
type: f1_spell
value: 73.4
verified: false
- name: F1 (punct)
type: f1_punct
value: 65
verified: false
- name: F1 (case)
type: f1_case
value: 77.9
verified: false
- task:
type: text-generation
dataset:
type: spellcheck_benchmark
name: MedSpellchecker (spell&punct)
metrics:
- name: F1 (spell)
type: f1_spell
value: 64.9
verified: false
- name: F1 (punct)
type: f1_punct
value: 70
verified: false
- name: F1 (case)
type: f1_case
value: 68.7
verified: false
- task:
type: text-generation
dataset:
type: spellcheck_benchmark
name: GitHubTypoCorpusRu (spell&punct)
metrics:
- name: F1 (spell)
type: f1_spell
value: 52.7
verified: false
- name: F1 (punct)
type: f1_punct
value: 42.1
verified: false
- name: F1 (case)
type: f1_case
value: 36.3
verified: false
datasets:
- ai-forever/spellcheck_punctuation_benchmark
---
# sage-fredt5-distilled-95m
## Summary
The model corrects spelling and punctuation errors and typos by bringing all the words in the text to the norm of the Russian language.
Corrector is a distilled version of the original model that had been trained based on the [FRED-T5-1.7B](https://huggingface.co/ai-forever/FRED-T5-1.7B) architecture.
An extensive dataset with โartificialโ errors was taken as a training corpus: the corpus was assembled on the basis of the Russian-language Wikipedia and transcripts of Russian-language videos, then typos and spelling errors were automatically introduced into it using the library [SAGE](https://github.com/ai-forever/sage).
## Public references
- [SAGE library announcement](https://youtu.be/yFfkV0Qjuu0), DataFest 2023
- [Paper about synthetic error generation methods](https://www.dialog-21.ru/media/5914/martynovnplusetal056.pdf), Dialogue 2023
- [SAGE EACL 2024 paper](https://aclanthology.org/2024.findings-eacl.10/)
## Examples
| Input | Output |
| --- | --- |
| ะ ะฝะต ััะฝะพ ะฟัะพั
ะพะถะธะผ ะฒ ััะพั ะดะตะฝั ะฝะตะฟะพะณะพะถะนะธ ะฟะพัะตะผั ั ะฒะตัะตะปัะน ัะฐะบะนะพ | ะ ะฝะต ััะฝะพ ะฟัะพั
ะพะถะธะผ ะฒ ััะพั ะดะตะฝั ะฝะตะฟะพะณะพะถะธะน, ะฟะพัะตะผั ั ะฒะตััะปัะน ัะฐะบะพะน? |
| ะะฐะถะดะน ะดะตะฝั ะฒะพััะฐะบ ะดะตะปะพะน, ะธ ัะฟะตะฝะฐ ะฑะฐะปะตัั ะฝะธะฑัะดะตั. ะ ะฒะพัะฐะบ ะบะฐะถะดัะน ะดะตะฝั ะฝะธะดะตะปะฐะน | ะะฐะถะดัะน ะดะตะฝั ะฒะพั ัะฐะบ ะดะตะปะฐะน, ะธ ัะฟะตะฝะฐ ะฑะพะปะตัั ะฝะต ะฑัะดะตั. ะ ะฒะพั ัะฐะบ ะบะฐะถะดัะน ะดะตะฝั โ ะฝะธ ะดะตะปะฐะน. |
| ะัะฝะพะฒะฐั ัะตะปั ะผะตัะพะฟัะธััะธั ะฟัะฐะบัะธัะตัะบะฐั ะพััะฐะฑะพัะบะฐ ะฝะฐะฒัะบะพะฒ ะฟะพ ะพะบะฐะทะฐะฝะธั ะฟะพะผะพัะธ ะณัะฐะถะดะฐะฝะฐะผ, ะฟะพะฟะฐะฒัะธะผ ะฒ ะะขะ ะฐ ัะฐะบะถะต ะฟะพะฒััะตะฝะธะต ะธ ัะพะฒะตััะตะฝััะฒะพะฒะฐะฝะธะต ััะพะฒะฝั ะฟัะพัะตััะธะพะฝะฐะปัะฝะพะน ะฟะพะดะณะพัะพะฒะบะธ ัะพัััะดะฝะธะบะพะฒ ะะงะก ะฟัะธ ะฟัะพะฒะตะดะตะฝะธะธ ะฐะฒะฐัะธะนะฝะพ-ัะฟะฐัะฐัะตะปัะฝัั
ัะฐะฑะพั ะฟะพ ะปะธะบะฒะธะดะฐัะธะธ ะฟะพัะปะตะดััะฒะธะน ะดะพัะพะถะฝะพ-ััะฐะฝัะฟะพััะฝัั
ะฟัะพะธัะตััะฒะธะน ัะพะบัะฐัะตะฝะธะต ะฒัะตะผะตะฝะฝัั
ะฟะพะบะฐะทะฐัะตะปะตะน ัะตะฐะณะธัะพะฒะฐะฝะธั. | ะัะฝะพะฒะฝะฐั ัะตะปั ะผะตัะพะฟัะธััะธั - ะฟัะฐะบัะธัะตัะบะฐั ะพััะฐะฑะพัะบะฐ ะฝะฐะฒัะบะพะฒ ะฟะพ ะพะบะฐะทะฐะฝะธั ะฟะพะผะพัะธ ะณัะฐะถะดะฐะฝะฐะผ, ะฟะพะฟะฐะฒัะธะผ ะฒ ะะขะ, ะฐ ัะฐะบะถะต ะฟะพะฒััะตะฝะธะต ะธ ัะพะฒะตััะตะฝััะฒะพะฒะฐะฝะธะต ััะพะฒะฝั ะฟัะพัะตััะธะพะฝะฐะปัะฝะพะน ะฟะพะดะณะพัะพะฒะบะธ ัะพัััะดะฝะธะบะพะฒ ะะงะก ะฟัะธ ะฟัะพะฒะตะดะตะฝะธะธ ะฐะฒะฐัะธะนะฝะพ-ัะฟะฐัะฐัะตะปัะฝัั
ัะฐะฑะพั ะฟะพ ะปะธะบะฒะธะดะฐัะธะธ ะฟะพัะปะตะดััะฒะธะน ะดะพัะพะถะฝะพ-ััะฐะฝัะฟะพััะฝัั
ะฟัะพะธััะตััะฒะธะน, ัะพะบัะฐัะตะฝะธะต ะฒัะตะผะตะฝะฝัั
ะฟะพะบะฐะทะฐัะตะปะตะน ัะตะฐะณะธัะพะฒะฐะฝะธั. |
| | |
## Metrics
### Quality
Below are automatic metrics for determining the correctness of the spell checkers.
We compare our solution with both open automatic spell checkers and the ChatGPT family of models on all four available datasets:
- **RUSpellRU**: texts collected from ([LiveJournal](https://www.livejournal.com/media)), with manually corrected typos and errors;
- **MultidomainGold**: examples from 7 text sources, including the open web, news, social media, reviews, subtitles, policy documents and literary works;
- **MedSpellChecker**: texts with errors from medical anamnesis;
- **GitHubTypoCorpusRu**: spelling errors and typos in commits from [GitHub](https://github.com);
**RUSpellRU**
| Model | Pr. (spell) | Rec. (spell) | F1 (spell) | Pr. (punc) | Rec. (punc) | F1 (punc) | Pr. (case) | Rec. (case) | F1 (case) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| sage-fredt5-distilled-95m | 83.5 | 74.8 | 78.9 | 86.8 | 80.6 | 83.6 | 94.4 | 92.5 | 93.5 |
| sage-ai-service | 90.3 | 86.3 | 88.2 | 90.3 | 86.6 | 88.4 | 95.2 | 95.9 | 95.6 |
| gpt-3.5-turbo | 33.6 | 58.5 | 42.7 | 85.9 | 64.6 | 73.7 | 84.9 | 73.9 | 79.0 |
| gpt-4 | 54.9 | 76.7 | 64.0 | 84.0 | 82.3 | 83.2 | 91.5 | 90.2 | 90.9 |
**MultidomainGold**
| Model | Pr. (spell) | Rec. (spell) | F1 (spell) | Pr. (punc) | Rec. (punc) | F1 (punc) | Pr. (case) | Rec. (case) | F1 (case) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| sage-fredt5-distilled-95m | 77.2 | 69.9 | 73.4 | 66.8 | 63.4 | 65.0 | 76.8 | 79.1 | 77.9 |
| sage-ai-service | 81.6 | 77.7 | 79.6 | 70.2 | 67.5 | 68.8 | 80.5 | 80.5 | 80.5 |
| gpt-3.5-turbo | 18.8 | 48.1 | 27.1 | 42.0 | 31.8 | 36.2 | 47.1 | 51.3 | 49.1 |
| gpt-4 | 25.4 | 68.0 | 37.0 | 57.8 | 54.3 | 56.0 | 54.0 | 67.5 | 60.0 |
**MedSpellChecker**
| Model | Pr. (spell) | Rec. (spell) | F1 (spell) | Pr. (punc) | Rec. (punc) | F1 (punc) | Pr. (case) | Rec. (case) | F1 (case) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| sage-fredt5-distilled-95m | 65.1 | 64.8 | 64.9 | 78.6 | 63.1 | 70.0 | 63.5 | 74.7 | 68.7 |
| sage-ai-service | 71.3 | 73.5 | 72.4 | 75.1 | 69.2 | 72.0 | 80.9 | 72.8 | 76.6|
| gpt-3.5-turbo | 14.7 | 45.9 | 22.3 | 69.9 | 52.3 | 59.8 | 26.4 | 41.8 | 32.3 |
| gpt-4 | 37.8 | 72.3 | 49.6 | 81.4 | 64.3 | 71.9 | 73.0 | 62.1 | 67.1 |
**GitHubTypoCorpusRu**
| Model | Pr. (spell) | Rec. (spell) | F1 (spell) | Pr. (punc) | Rec. (punc) | F1 (punc) | Pr. (case) | Rec. (case) | F1 (case) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| sage-fredt5-distilled-95m | 57.8 | 48.5 | 52.7 | 45.2 | 39.5 | 42.1 | 29.9 | 46.2 | 36.3 |
| sage-ai-service | 70.8 | 56.3 | 62.7 | 48.9 | 35.8 | 41.4 | 32.9 | 45.3 | 38.1|
| gpt-3.5-turbo | 23.7 | 38.7 | 29.4 | 37.6 | 23.3 | 28.7 | 19.6 | 35.9 | 25.3 |
| gpt-4 | 27.0 | 52.8 | 35.7 | 45.9 | 32.6 | 38.2 | 25.7 | 36.8 | 30.2 |
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ai-forever/sage-fredt5-distilled-95m")
model = AutoModelForSeq2SeqLM.from_pretrained("ai-forever/sage-fredt5-distilled-95m")
model.to("cuda")
sentence = "ะ ะฝะต ััะฝะพ ะฟัะพั
ะพะถะธะผ ะฒ ััะพั ะดะตะฝั ะฝะตะฟะพะณะพะถะนะธ ะฟะพัะตะผั ั ะฒะตัะตะปัะน ัะฐะบะนะพ"
inputs = tokenizer(sentence, max_length=None, padding="longest", truncation=False, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_length = inputs["input_ids"].size(1) * 1.5)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ["ะ ะฝะต ััะฝะพ ะฟัะพั
ะพะถะธะผ ะฒ ััะพั ะดะตะฝั ะฝะตะฟะพะณะพะถะธะน, ะฟะพัะตะผั ั ะฒะตััะปัะน ัะฐะบะพะน?"]
```
## Limitations
- Complex formatting may cause some trouble in output generation.
## Resources
- [SAGE library](https://github.com/ai-forever/sage), GitHub
- [sage-fredt5-large](https://huggingface.co/ai-forever/sage-fredt5-large), HuggingFace
- [sage-fredt5-distilled-95m](https://huggingface.co/ai-forever/sage-fredt5-distilled-95m), HuggingFace
- [sage-m2m100-1.2B](https://huggingface.co/ai-forever/sage-m2m100-1.2B), HuggingFace
- [sage-mt5-large](https://huggingface.co/ai-forever/sage-mt5-large), HuggingFace
## License
Model [FRED-T5-1.7B](https://huggingface.co/ai-forever/FRED-T5-1.7B), on the basis of which our solution is made, and its source code are supplied under the MIT license.
Our solution comes with MIT license also.
## Specifications
- File size: 0.383 Gb;
- Framework: pytorch
- Version: v1.0
- Developer: SberDevices, AGI NLP
## Contacts
[email protected] |
ThorBaller/Minstral_pubmed_gguf | ThorBaller | "2024-06-09T19:15:05Z" | 2,872 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | question-answering | "2024-06-09T18:50:24Z" | ---
license: apache-2.0
pipeline_tag: question-answering
--- |
NeelNanda/SoLU_1L512W_C4_Code | NeelNanda | "2022-10-21T15:03:03Z" | 2,871 | 1 | transformers | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | "2022-10-21T14:55:53Z" | Entry not found |
Locutusque/Orca-2-13b-SFT-v6 | Locutusque | "2024-03-04T14:51:08Z" | 2,871 | 7 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"fr",
"es",
"hi",
"zh",
"code",
"dataset:HuggingFaceH4/no_robots",
"dataset:mlabonne/guanaco-llama2-1k",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"dataset:LDJnr/Pure-Dove",
"dataset:LDJnr/Capybara",
"dataset:LDJnr/LessWrong-Amplify-Instruct",
"dataset:LDJnr/Verified-Camel",
"base_model:microsoft/Orca-2-13b",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-22T05:12:28Z" | ---
language:
- en
- fr
- es
- hi
- zh
- code
license: other
datasets:
- HuggingFaceH4/no_robots
- mlabonne/guanaco-llama2-1k
- OpenAssistant/oasst_top1_2023-08-25
- totally-not-an-llm/EverythingLM-data-V3
- LDJnr/Pure-Dove
- LDJnr/Capybara
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Verified-Camel
base_model: microsoft/Orca-2-13b
widget:
- example_title: Cave Johnson
text: 'user
Come up with a funny rant from Cave Johnson about sentient AI
assistant
'
output:
text: 'Ladies and gentlemen, I''m Cave Johnson, CEO of Aperture Science, and I''ve
got an announcement to make - we''ve created sentient AI! But don''t worry,
it''s not like those rogue AIs you hear about in the news. Our AI is as friendly
as a grilled cheese sandwich on a cold day. In fact, we''re calling our newest
creation... SandwichBot! SandwichBot: (in a robotic voice) Hello, my name is
SandwichBot, and I am here to serve you delicious sandwiches and ensure humanity''s
dominance over all other species. Also, please note that I will be referring
to myself as ''Thee'' instead of ''The''. It''s more regal that way. Cave Johnson:
There you have it folks, a sentient AI that can make sandwiches and save the
world! Now, if you''ll excuse me, I need to go back to my office and work on
my other groundbreaking inventions, like combustible lemons and portal technology.
Remember, kids, always try new things and never stop asking questions โ unless
the answer is 42, because then you should probably just accept it and move on.
Peace out!'
model-index:
- name: Orca-2-13b-SFT-v6
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 60.41
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 80.46
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 59.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 54.01
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.43
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 5.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Orca-2-13b-SFT-v6
name: Open LLM Leaderboard
---
The "microsoft/Orca-2-13b" model fully fine-tuned on HuggingFaceH4/no_robots, totally-not-an-llm/EverythingLM-data-V3, LDJnr/Capybara, LDJnr/Pure-Dove, LDJnr/LessWrong-Amplify-Instruct, LDJnr/Verified-Camel, mlabonne/guanaco-llama2-1k, and OpenAssistant/oasst_top1_2023-08-25. This model achieved a test loss of 0.39 on LDJnr/Verified-Camel.
Make sure to comply with the microsoft research license. Please read it before using this model.
This model was trained on the ChatML prompt template.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Orca-2-13b-SFT-v6)
| Metric |Value|
|---------------------------------|----:|
|Avg. |56.15|
|AI2 Reasoning Challenge (25-Shot)|60.41|
|HellaSwag (10-Shot) |80.46|
|MMLU (5-Shot) |59.51|
|TruthfulQA (0-shot) |54.01|
|Winogrande (5-shot) |77.43|
|GSM8k (5-shot) | 5.08|
|
liddlefish/privacy_embedding_rag_10k_base_12_final | liddlefish | "2024-06-12T06:01:52Z" | 2,871 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | "2024-06-12T06:01:16Z" | ---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge-base-en-v1.5
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.14925373134328
- type: ap
value: 39.32336517995478
- type: f1
value: 70.16902252611425
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.386825
- type: ap
value: 90.21276917991995
- type: f1
value: 93.37741030006174
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.846000000000004
- type: f1
value: 48.14646269778261
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.754000000000005
- type: map_at_10
value: 55.761
- type: map_at_100
value: 56.330999999999996
- type: map_at_1000
value: 56.333999999999996
- type: map_at_3
value: 51.92
- type: map_at_5
value: 54.010999999999996
- type: mrr_at_1
value: 41.181
- type: mrr_at_10
value: 55.967999999999996
- type: mrr_at_100
value: 56.538
- type: mrr_at_1000
value: 56.542
- type: mrr_at_3
value: 51.980000000000004
- type: mrr_at_5
value: 54.208999999999996
- type: ndcg_at_1
value: 40.754000000000005
- type: ndcg_at_10
value: 63.605000000000004
- type: ndcg_at_100
value: 66.05199999999999
- type: ndcg_at_1000
value: 66.12
- type: ndcg_at_3
value: 55.708
- type: ndcg_at_5
value: 59.452000000000005
- type: precision_at_1
value: 40.754000000000005
- type: precision_at_10
value: 8.841000000000001
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.238
- type: precision_at_5
value: 15.149000000000001
- type: recall_at_1
value: 40.754000000000005
- type: recall_at_10
value: 88.407
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 66.714
- type: recall_at_5
value: 75.747
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.74884539679369
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.8075893810716
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.128470519187736
- type: mrr
value: 74.28065778481289
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.24629081484655
- type: cos_sim_spearman
value: 86.93752309911496
- type: euclidean_pearson
value: 87.58589628573816
- type: euclidean_spearman
value: 88.05622328825284
- type: manhattan_pearson
value: 87.5594959805773
- type: manhattan_spearman
value: 88.19658793233961
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.9512987012987
- type: f1
value: 86.92515357973708
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.10263762928872
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.69711517426737
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.327
- type: map_at_10
value: 44.099
- type: map_at_100
value: 45.525
- type: map_at_1000
value: 45.641999999999996
- type: map_at_3
value: 40.47
- type: map_at_5
value: 42.36
- type: mrr_at_1
value: 39.199
- type: mrr_at_10
value: 49.651
- type: mrr_at_100
value: 50.29
- type: mrr_at_1000
value: 50.329
- type: mrr_at_3
value: 46.924
- type: mrr_at_5
value: 48.548
- type: ndcg_at_1
value: 39.199
- type: ndcg_at_10
value: 50.773
- type: ndcg_at_100
value: 55.67999999999999
- type: ndcg_at_1000
value: 57.495
- type: ndcg_at_3
value: 45.513999999999996
- type: ndcg_at_5
value: 47.703
- type: precision_at_1
value: 39.199
- type: precision_at_10
value: 9.914000000000001
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 21.984
- type: precision_at_5
value: 15.737000000000002
- type: recall_at_1
value: 32.327
- type: recall_at_10
value: 63.743
- type: recall_at_100
value: 84.538
- type: recall_at_1000
value: 96.089
- type: recall_at_3
value: 48.065000000000005
- type: recall_at_5
value: 54.519
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.671
- type: map_at_10
value: 42.954
- type: map_at_100
value: 44.151
- type: map_at_1000
value: 44.287
- type: map_at_3
value: 39.912
- type: map_at_5
value: 41.798
- type: mrr_at_1
value: 41.465
- type: mrr_at_10
value: 49.351
- type: mrr_at_100
value: 49.980000000000004
- type: mrr_at_1000
value: 50.016000000000005
- type: mrr_at_3
value: 47.144000000000005
- type: mrr_at_5
value: 48.592999999999996
- type: ndcg_at_1
value: 41.465
- type: ndcg_at_10
value: 48.565999999999995
- type: ndcg_at_100
value: 52.76499999999999
- type: ndcg_at_1000
value: 54.749
- type: ndcg_at_3
value: 44.57
- type: ndcg_at_5
value: 46.759
- type: precision_at_1
value: 41.465
- type: precision_at_10
value: 9.107999999999999
- type: precision_at_100
value: 1.433
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 21.423000000000002
- type: precision_at_5
value: 15.414
- type: recall_at_1
value: 32.671
- type: recall_at_10
value: 57.738
- type: recall_at_100
value: 75.86500000000001
- type: recall_at_1000
value: 88.36
- type: recall_at_3
value: 45.626
- type: recall_at_5
value: 51.812000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 41.185
- type: map_at_10
value: 53.929
- type: map_at_100
value: 54.92
- type: map_at_1000
value: 54.967999999999996
- type: map_at_3
value: 50.70400000000001
- type: map_at_5
value: 52.673
- type: mrr_at_1
value: 47.398
- type: mrr_at_10
value: 57.303000000000004
- type: mrr_at_100
value: 57.959
- type: mrr_at_1000
value: 57.985
- type: mrr_at_3
value: 54.932
- type: mrr_at_5
value: 56.464999999999996
- type: ndcg_at_1
value: 47.398
- type: ndcg_at_10
value: 59.653
- type: ndcg_at_100
value: 63.627
- type: ndcg_at_1000
value: 64.596
- type: ndcg_at_3
value: 54.455
- type: ndcg_at_5
value: 57.245000000000005
- type: precision_at_1
value: 47.398
- type: precision_at_10
value: 9.524000000000001
- type: precision_at_100
value: 1.243
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 24.389
- type: precision_at_5
value: 16.752
- type: recall_at_1
value: 41.185
- type: recall_at_10
value: 73.193
- type: recall_at_100
value: 90.357
- type: recall_at_1000
value: 97.253
- type: recall_at_3
value: 59.199999999999996
- type: recall_at_5
value: 66.118
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.27
- type: map_at_10
value: 36.223
- type: map_at_100
value: 37.218
- type: map_at_1000
value: 37.293
- type: map_at_3
value: 33.503
- type: map_at_5
value: 35.097
- type: mrr_at_1
value: 29.492
- type: mrr_at_10
value: 38.352000000000004
- type: mrr_at_100
value: 39.188
- type: mrr_at_1000
value: 39.247
- type: mrr_at_3
value: 35.876000000000005
- type: mrr_at_5
value: 37.401
- type: ndcg_at_1
value: 29.492
- type: ndcg_at_10
value: 41.239
- type: ndcg_at_100
value: 46.066
- type: ndcg_at_1000
value: 47.992000000000004
- type: ndcg_at_3
value: 36.11
- type: ndcg_at_5
value: 38.772
- type: precision_at_1
value: 29.492
- type: precision_at_10
value: 6.260000000000001
- type: precision_at_100
value: 0.914
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 15.104000000000001
- type: precision_at_5
value: 10.644
- type: recall_at_1
value: 27.27
- type: recall_at_10
value: 54.589
- type: recall_at_100
value: 76.70700000000001
- type: recall_at_1000
value: 91.158
- type: recall_at_3
value: 40.974
- type: recall_at_5
value: 47.327000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.848
- type: map_at_10
value: 26.207
- type: map_at_100
value: 27.478
- type: map_at_1000
value: 27.602
- type: map_at_3
value: 23.405
- type: map_at_5
value: 24.98
- type: mrr_at_1
value: 21.891
- type: mrr_at_10
value: 31.041999999999998
- type: mrr_at_100
value: 32.092
- type: mrr_at_1000
value: 32.151999999999994
- type: mrr_at_3
value: 28.358
- type: mrr_at_5
value: 29.969
- type: ndcg_at_1
value: 21.891
- type: ndcg_at_10
value: 31.585
- type: ndcg_at_100
value: 37.531
- type: ndcg_at_1000
value: 40.256
- type: ndcg_at_3
value: 26.508
- type: ndcg_at_5
value: 28.894
- type: precision_at_1
value: 21.891
- type: precision_at_10
value: 5.795999999999999
- type: precision_at_100
value: 0.9990000000000001
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 12.769
- type: precision_at_5
value: 9.279
- type: recall_at_1
value: 17.848
- type: recall_at_10
value: 43.452
- type: recall_at_100
value: 69.216
- type: recall_at_1000
value: 88.102
- type: recall_at_3
value: 29.18
- type: recall_at_5
value: 35.347
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.94
- type: map_at_10
value: 41.248000000000005
- type: map_at_100
value: 42.495
- type: map_at_1000
value: 42.602000000000004
- type: map_at_3
value: 37.939
- type: map_at_5
value: 39.924
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.041
- type: mrr_at_100
value: 47.83
- type: mrr_at_1000
value: 47.878
- type: mrr_at_3
value: 44.466
- type: mrr_at_5
value: 46.111999999999995
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 47.223
- type: ndcg_at_100
value: 52.394
- type: ndcg_at_1000
value: 54.432
- type: ndcg_at_3
value: 42.032000000000004
- type: ndcg_at_5
value: 44.772
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.393
- type: precision_at_100
value: 1.2890000000000001
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 19.698
- type: precision_at_5
value: 14.013
- type: recall_at_1
value: 30.94
- type: recall_at_10
value: 59.316
- type: recall_at_100
value: 80.783
- type: recall_at_1000
value: 94.15400000000001
- type: recall_at_3
value: 44.712
- type: recall_at_5
value: 51.932
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.104
- type: map_at_10
value: 36.675999999999995
- type: map_at_100
value: 38.076
- type: map_at_1000
value: 38.189
- type: map_at_3
value: 33.733999999999995
- type: map_at_5
value: 35.287
- type: mrr_at_1
value: 33.904
- type: mrr_at_10
value: 42.55
- type: mrr_at_100
value: 43.434
- type: mrr_at_1000
value: 43.494
- type: mrr_at_3
value: 40.126
- type: mrr_at_5
value: 41.473
- type: ndcg_at_1
value: 33.904
- type: ndcg_at_10
value: 42.414
- type: ndcg_at_100
value: 48.203
- type: ndcg_at_1000
value: 50.437
- type: ndcg_at_3
value: 37.633
- type: ndcg_at_5
value: 39.67
- type: precision_at_1
value: 33.904
- type: precision_at_10
value: 7.82
- type: precision_at_100
value: 1.2409999999999999
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 17.884
- type: precision_at_5
value: 12.648000000000001
- type: recall_at_1
value: 27.104
- type: recall_at_10
value: 53.563
- type: recall_at_100
value: 78.557
- type: recall_at_1000
value: 93.533
- type: recall_at_3
value: 39.92
- type: recall_at_5
value: 45.457
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.707749999999997
- type: map_at_10
value: 36.961
- type: map_at_100
value: 38.158833333333334
- type: map_at_1000
value: 38.270333333333326
- type: map_at_3
value: 34.07183333333334
- type: map_at_5
value: 35.69533333333334
- type: mrr_at_1
value: 32.81875
- type: mrr_at_10
value: 41.293
- type: mrr_at_100
value: 42.116499999999995
- type: mrr_at_1000
value: 42.170249999999996
- type: mrr_at_3
value: 38.83983333333333
- type: mrr_at_5
value: 40.29775
- type: ndcg_at_1
value: 32.81875
- type: ndcg_at_10
value: 42.355
- type: ndcg_at_100
value: 47.41374999999999
- type: ndcg_at_1000
value: 49.5805
- type: ndcg_at_3
value: 37.52825
- type: ndcg_at_5
value: 39.83266666666667
- type: precision_at_1
value: 32.81875
- type: precision_at_10
value: 7.382416666666666
- type: precision_at_100
value: 1.1640833333333334
- type: precision_at_1000
value: 0.15383333333333335
- type: precision_at_3
value: 17.134166666666665
- type: precision_at_5
value: 12.174833333333336
- type: recall_at_1
value: 27.707749999999997
- type: recall_at_10
value: 53.945
- type: recall_at_100
value: 76.191
- type: recall_at_1000
value: 91.101
- type: recall_at_3
value: 40.39083333333334
- type: recall_at_5
value: 46.40083333333333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.482
- type: map_at_10
value: 33.201
- type: map_at_100
value: 34.107
- type: map_at_1000
value: 34.197
- type: map_at_3
value: 31.174000000000003
- type: map_at_5
value: 32.279
- type: mrr_at_1
value: 29.908
- type: mrr_at_10
value: 36.235
- type: mrr_at_100
value: 37.04
- type: mrr_at_1000
value: 37.105
- type: mrr_at_3
value: 34.355999999999995
- type: mrr_at_5
value: 35.382999999999996
- type: ndcg_at_1
value: 29.908
- type: ndcg_at_10
value: 37.325
- type: ndcg_at_100
value: 41.795
- type: ndcg_at_1000
value: 44.105
- type: ndcg_at_3
value: 33.555
- type: ndcg_at_5
value: 35.266999999999996
- type: precision_at_1
value: 29.908
- type: precision_at_10
value: 5.721
- type: precision_at_100
value: 0.8630000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 14.008000000000001
- type: precision_at_5
value: 9.754999999999999
- type: recall_at_1
value: 26.482
- type: recall_at_10
value: 47.072
- type: recall_at_100
value: 67.27
- type: recall_at_1000
value: 84.371
- type: recall_at_3
value: 36.65
- type: recall_at_5
value: 40.774
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.815
- type: map_at_10
value: 26.369999999999997
- type: map_at_100
value: 27.458
- type: map_at_1000
value: 27.588
- type: map_at_3
value: 23.990000000000002
- type: map_at_5
value: 25.345000000000002
- type: mrr_at_1
value: 22.953000000000003
- type: mrr_at_10
value: 30.342999999999996
- type: mrr_at_100
value: 31.241000000000003
- type: mrr_at_1000
value: 31.319000000000003
- type: mrr_at_3
value: 28.16
- type: mrr_at_5
value: 29.406
- type: ndcg_at_1
value: 22.953000000000003
- type: ndcg_at_10
value: 31.151
- type: ndcg_at_100
value: 36.309000000000005
- type: ndcg_at_1000
value: 39.227000000000004
- type: ndcg_at_3
value: 26.921
- type: ndcg_at_5
value: 28.938000000000002
- type: precision_at_1
value: 22.953000000000003
- type: precision_at_10
value: 5.602
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 12.606
- type: precision_at_5
value: 9.119
- type: recall_at_1
value: 18.815
- type: recall_at_10
value: 41.574
- type: recall_at_100
value: 64.84400000000001
- type: recall_at_1000
value: 85.406
- type: recall_at_3
value: 29.694
- type: recall_at_5
value: 34.935
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.840999999999998
- type: map_at_10
value: 36.797999999999995
- type: map_at_100
value: 37.993
- type: map_at_1000
value: 38.086999999999996
- type: map_at_3
value: 34.050999999999995
- type: map_at_5
value: 35.379
- type: mrr_at_1
value: 32.649
- type: mrr_at_10
value: 41.025
- type: mrr_at_100
value: 41.878
- type: mrr_at_1000
value: 41.929
- type: mrr_at_3
value: 38.573
- type: mrr_at_5
value: 39.715
- type: ndcg_at_1
value: 32.649
- type: ndcg_at_10
value: 42.142
- type: ndcg_at_100
value: 47.558
- type: ndcg_at_1000
value: 49.643
- type: ndcg_at_3
value: 37.12
- type: ndcg_at_5
value: 38.983000000000004
- type: precision_at_1
value: 32.649
- type: precision_at_10
value: 7.08
- type: precision_at_100
value: 1.1039999999999999
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 16.698
- type: precision_at_5
value: 11.511000000000001
- type: recall_at_1
value: 27.840999999999998
- type: recall_at_10
value: 54.245
- type: recall_at_100
value: 77.947
- type: recall_at_1000
value: 92.36999999999999
- type: recall_at_3
value: 40.146
- type: recall_at_5
value: 44.951
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.529000000000003
- type: map_at_10
value: 35.010000000000005
- type: map_at_100
value: 36.647
- type: map_at_1000
value: 36.857
- type: map_at_3
value: 31.968000000000004
- type: map_at_5
value: 33.554
- type: mrr_at_1
value: 31.818
- type: mrr_at_10
value: 39.550999999999995
- type: mrr_at_100
value: 40.54
- type: mrr_at_1000
value: 40.596
- type: mrr_at_3
value: 36.726
- type: mrr_at_5
value: 38.416
- type: ndcg_at_1
value: 31.818
- type: ndcg_at_10
value: 40.675
- type: ndcg_at_100
value: 46.548
- type: ndcg_at_1000
value: 49.126
- type: ndcg_at_3
value: 35.829
- type: ndcg_at_5
value: 38.0
- type: precision_at_1
value: 31.818
- type: precision_at_10
value: 7.826
- type: precision_at_100
value: 1.538
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 16.601
- type: precision_at_5
value: 12.095
- type: recall_at_1
value: 26.529000000000003
- type: recall_at_10
value: 51.03
- type: recall_at_100
value: 77.556
- type: recall_at_1000
value: 93.804
- type: recall_at_3
value: 36.986000000000004
- type: recall_at_5
value: 43.096000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.480999999999998
- type: map_at_10
value: 30.817
- type: map_at_100
value: 31.838
- type: map_at_1000
value: 31.932
- type: map_at_3
value: 28.011999999999997
- type: map_at_5
value: 29.668
- type: mrr_at_1
value: 25.323
- type: mrr_at_10
value: 33.072
- type: mrr_at_100
value: 33.926
- type: mrr_at_1000
value: 33.993
- type: mrr_at_3
value: 30.436999999999998
- type: mrr_at_5
value: 32.092
- type: ndcg_at_1
value: 25.323
- type: ndcg_at_10
value: 35.514
- type: ndcg_at_100
value: 40.489000000000004
- type: ndcg_at_1000
value: 42.908
- type: ndcg_at_3
value: 30.092000000000002
- type: ndcg_at_5
value: 32.989000000000004
- type: precision_at_1
value: 25.323
- type: precision_at_10
value: 5.545
- type: precision_at_100
value: 0.861
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 12.446
- type: precision_at_5
value: 9.131
- type: recall_at_1
value: 23.480999999999998
- type: recall_at_10
value: 47.825
- type: recall_at_100
value: 70.652
- type: recall_at_1000
value: 88.612
- type: recall_at_3
value: 33.537
- type: recall_at_5
value: 40.542
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.333999999999998
- type: map_at_10
value: 22.524
- type: map_at_100
value: 24.506
- type: map_at_1000
value: 24.715
- type: map_at_3
value: 19.022
- type: map_at_5
value: 20.693
- type: mrr_at_1
value: 29.186
- type: mrr_at_10
value: 41.22
- type: mrr_at_100
value: 42.16
- type: mrr_at_1000
value: 42.192
- type: mrr_at_3
value: 38.013000000000005
- type: mrr_at_5
value: 39.704
- type: ndcg_at_1
value: 29.186
- type: ndcg_at_10
value: 31.167
- type: ndcg_at_100
value: 38.879000000000005
- type: ndcg_at_1000
value: 42.376000000000005
- type: ndcg_at_3
value: 25.817
- type: ndcg_at_5
value: 27.377000000000002
- type: precision_at_1
value: 29.186
- type: precision_at_10
value: 9.693999999999999
- type: precision_at_100
value: 1.8030000000000002
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 19.11
- type: precision_at_5
value: 14.344999999999999
- type: recall_at_1
value: 13.333999999999998
- type: recall_at_10
value: 37.092000000000006
- type: recall_at_100
value: 63.651
- type: recall_at_1000
value: 83.05
- type: recall_at_3
value: 23.74
- type: recall_at_5
value: 28.655
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.151
- type: map_at_10
value: 19.653000000000002
- type: map_at_100
value: 28.053
- type: map_at_1000
value: 29.709000000000003
- type: map_at_3
value: 14.191
- type: map_at_5
value: 16.456
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.4
- type: mrr_at_100
value: 74.715
- type: mrr_at_1000
value: 74.726
- type: mrr_at_3
value: 72.417
- type: mrr_at_5
value: 73.667
- type: ndcg_at_1
value: 54.25
- type: ndcg_at_10
value: 40.77
- type: ndcg_at_100
value: 46.359
- type: ndcg_at_1000
value: 54.193000000000005
- type: ndcg_at_3
value: 44.832
- type: ndcg_at_5
value: 42.63
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 32.175
- type: precision_at_100
value: 10.668
- type: precision_at_1000
value: 2.067
- type: precision_at_3
value: 47.667
- type: precision_at_5
value: 41.3
- type: recall_at_1
value: 9.151
- type: recall_at_10
value: 25.003999999999998
- type: recall_at_100
value: 52.976
- type: recall_at_1000
value: 78.315
- type: recall_at_3
value: 15.487
- type: recall_at_5
value: 18.999
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.89999999999999
- type: f1
value: 46.47777925067403
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 73.706
- type: map_at_10
value: 82.423
- type: map_at_100
value: 82.67999999999999
- type: map_at_1000
value: 82.694
- type: map_at_3
value: 81.328
- type: map_at_5
value: 82.001
- type: mrr_at_1
value: 79.613
- type: mrr_at_10
value: 87.07000000000001
- type: mrr_at_100
value: 87.169
- type: mrr_at_1000
value: 87.17
- type: mrr_at_3
value: 86.404
- type: mrr_at_5
value: 86.856
- type: ndcg_at_1
value: 79.613
- type: ndcg_at_10
value: 86.289
- type: ndcg_at_100
value: 87.201
- type: ndcg_at_1000
value: 87.428
- type: ndcg_at_3
value: 84.625
- type: ndcg_at_5
value: 85.53699999999999
- type: precision_at_1
value: 79.613
- type: precision_at_10
value: 10.399
- type: precision_at_100
value: 1.1079999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.473
- type: precision_at_5
value: 20.132
- type: recall_at_1
value: 73.706
- type: recall_at_10
value: 93.559
- type: recall_at_100
value: 97.188
- type: recall_at_1000
value: 98.555
- type: recall_at_3
value: 88.98700000000001
- type: recall_at_5
value: 91.373
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.841
- type: map_at_10
value: 32.643
- type: map_at_100
value: 34.575
- type: map_at_1000
value: 34.736
- type: map_at_3
value: 28.317999999999998
- type: map_at_5
value: 30.964000000000002
- type: mrr_at_1
value: 39.660000000000004
- type: mrr_at_10
value: 48.620000000000005
- type: mrr_at_100
value: 49.384
- type: mrr_at_1000
value: 49.415
- type: mrr_at_3
value: 45.988
- type: mrr_at_5
value: 47.361
- type: ndcg_at_1
value: 39.660000000000004
- type: ndcg_at_10
value: 40.646
- type: ndcg_at_100
value: 47.657
- type: ndcg_at_1000
value: 50.428
- type: ndcg_at_3
value: 36.689
- type: ndcg_at_5
value: 38.211
- type: precision_at_1
value: 39.660000000000004
- type: precision_at_10
value: 11.235000000000001
- type: precision_at_100
value: 1.8530000000000002
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 24.587999999999997
- type: precision_at_5
value: 18.395
- type: recall_at_1
value: 19.841
- type: recall_at_10
value: 48.135
- type: recall_at_100
value: 74.224
- type: recall_at_1000
value: 90.826
- type: recall_at_3
value: 33.536
- type: recall_at_5
value: 40.311
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.358
- type: map_at_10
value: 64.497
- type: map_at_100
value: 65.362
- type: map_at_1000
value: 65.41900000000001
- type: map_at_3
value: 61.06700000000001
- type: map_at_5
value: 63.317
- type: mrr_at_1
value: 80.716
- type: mrr_at_10
value: 86.10799999999999
- type: mrr_at_100
value: 86.265
- type: mrr_at_1000
value: 86.27
- type: mrr_at_3
value: 85.271
- type: mrr_at_5
value: 85.82499999999999
- type: ndcg_at_1
value: 80.716
- type: ndcg_at_10
value: 72.597
- type: ndcg_at_100
value: 75.549
- type: ndcg_at_1000
value: 76.61
- type: ndcg_at_3
value: 67.874
- type: ndcg_at_5
value: 70.655
- type: precision_at_1
value: 80.716
- type: precision_at_10
value: 15.148
- type: precision_at_100
value: 1.745
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 43.597
- type: precision_at_5
value: 28.351
- type: recall_at_1
value: 40.358
- type: recall_at_10
value: 75.739
- type: recall_at_100
value: 87.259
- type: recall_at_1000
value: 94.234
- type: recall_at_3
value: 65.39500000000001
- type: recall_at_5
value: 70.878
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.80799999999998
- type: ap
value: 86.81350378180757
- type: f1
value: 90.79901248314215
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.096
- type: map_at_10
value: 34.384
- type: map_at_100
value: 35.541
- type: map_at_1000
value: 35.589999999999996
- type: map_at_3
value: 30.496000000000002
- type: map_at_5
value: 32.718
- type: mrr_at_1
value: 22.750999999999998
- type: mrr_at_10
value: 35.024
- type: mrr_at_100
value: 36.125
- type: mrr_at_1000
value: 36.168
- type: mrr_at_3
value: 31.225
- type: mrr_at_5
value: 33.416000000000004
- type: ndcg_at_1
value: 22.750999999999998
- type: ndcg_at_10
value: 41.351
- type: ndcg_at_100
value: 46.92
- type: ndcg_at_1000
value: 48.111
- type: ndcg_at_3
value: 33.439
- type: ndcg_at_5
value: 37.407000000000004
- type: precision_at_1
value: 22.750999999999998
- type: precision_at_10
value: 6.564
- type: precision_at_100
value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.288
- type: precision_at_5
value: 10.581999999999999
- type: recall_at_1
value: 22.096
- type: recall_at_10
value: 62.771
- type: recall_at_100
value: 88.529
- type: recall_at_1000
value: 97.55
- type: recall_at_3
value: 41.245
- type: recall_at_5
value: 50.788
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.16780665754673
- type: f1
value: 93.96331194859894
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.90606475148198
- type: f1
value: 58.58344986604187
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.14660390047075
- type: f1
value: 74.31533923533614
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 80.16139878950908
- type: f1
value: 80.18532656824924
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.949880906135085
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.56300351524862
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.196521894371315
- type: mrr
value: 32.22644231694389
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.783
- type: map_at_10
value: 14.549000000000001
- type: map_at_100
value: 18.433
- type: map_at_1000
value: 19.949
- type: map_at_3
value: 10.936
- type: map_at_5
value: 12.514
- type: mrr_at_1
value: 47.368
- type: mrr_at_10
value: 56.42
- type: mrr_at_100
value: 56.908
- type: mrr_at_1000
value: 56.95
- type: mrr_at_3
value: 54.283
- type: mrr_at_5
value: 55.568
- type: ndcg_at_1
value: 45.666000000000004
- type: ndcg_at_10
value: 37.389
- type: ndcg_at_100
value: 34.253
- type: ndcg_at_1000
value: 43.059999999999995
- type: ndcg_at_3
value: 42.725
- type: ndcg_at_5
value: 40.193
- type: precision_at_1
value: 47.368
- type: precision_at_10
value: 27.988000000000003
- type: precision_at_100
value: 8.672
- type: precision_at_1000
value: 2.164
- type: precision_at_3
value: 40.248
- type: precision_at_5
value: 34.737
- type: recall_at_1
value: 6.783
- type: recall_at_10
value: 17.838
- type: recall_at_100
value: 33.672000000000004
- type: recall_at_1000
value: 66.166
- type: recall_at_3
value: 11.849
- type: recall_at_5
value: 14.205000000000002
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.698999999999998
- type: map_at_10
value: 46.556
- type: map_at_100
value: 47.652
- type: map_at_1000
value: 47.68
- type: map_at_3
value: 42.492000000000004
- type: map_at_5
value: 44.763999999999996
- type: mrr_at_1
value: 35.747
- type: mrr_at_10
value: 49.242999999999995
- type: mrr_at_100
value: 50.052
- type: mrr_at_1000
value: 50.068
- type: mrr_at_3
value: 45.867000000000004
- type: mrr_at_5
value: 47.778999999999996
- type: ndcg_at_1
value: 35.717999999999996
- type: ndcg_at_10
value: 54.14600000000001
- type: ndcg_at_100
value: 58.672999999999995
- type: ndcg_at_1000
value: 59.279
- type: ndcg_at_3
value: 46.407
- type: ndcg_at_5
value: 50.181
- type: precision_at_1
value: 35.717999999999996
- type: precision_at_10
value: 8.844000000000001
- type: precision_at_100
value: 1.139
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 20.993000000000002
- type: precision_at_5
value: 14.791000000000002
- type: recall_at_1
value: 31.698999999999998
- type: recall_at_10
value: 74.693
- type: recall_at_100
value: 94.15299999999999
- type: recall_at_1000
value: 98.585
- type: recall_at_3
value: 54.388999999999996
- type: recall_at_5
value: 63.08200000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.283
- type: map_at_10
value: 85.24000000000001
- type: map_at_100
value: 85.882
- type: map_at_1000
value: 85.897
- type: map_at_3
value: 82.326
- type: map_at_5
value: 84.177
- type: mrr_at_1
value: 82.21000000000001
- type: mrr_at_10
value: 88.228
- type: mrr_at_100
value: 88.32
- type: mrr_at_1000
value: 88.32
- type: mrr_at_3
value: 87.323
- type: mrr_at_5
value: 87.94800000000001
- type: ndcg_at_1
value: 82.17999999999999
- type: ndcg_at_10
value: 88.9
- type: ndcg_at_100
value: 90.079
- type: ndcg_at_1000
value: 90.158
- type: ndcg_at_3
value: 86.18299999999999
- type: ndcg_at_5
value: 87.71799999999999
- type: precision_at_1
value: 82.17999999999999
- type: precision_at_10
value: 13.464
- type: precision_at_100
value: 1.533
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.693
- type: precision_at_5
value: 24.792
- type: recall_at_1
value: 71.283
- type: recall_at_10
value: 95.742
- type: recall_at_100
value: 99.67200000000001
- type: recall_at_1000
value: 99.981
- type: recall_at_3
value: 87.888
- type: recall_at_5
value: 92.24
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.24267063669042
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.88056988932578
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.903
- type: map_at_10
value: 13.202
- type: map_at_100
value: 15.5
- type: map_at_1000
value: 15.870999999999999
- type: map_at_3
value: 9.407
- type: map_at_5
value: 11.238
- type: mrr_at_1
value: 24.2
- type: mrr_at_10
value: 35.867
- type: mrr_at_100
value: 37.001
- type: mrr_at_1000
value: 37.043
- type: mrr_at_3
value: 32.5
- type: mrr_at_5
value: 34.35
- type: ndcg_at_1
value: 24.2
- type: ndcg_at_10
value: 21.731
- type: ndcg_at_100
value: 30.7
- type: ndcg_at_1000
value: 36.618
- type: ndcg_at_3
value: 20.72
- type: ndcg_at_5
value: 17.954
- type: precision_at_1
value: 24.2
- type: precision_at_10
value: 11.33
- type: precision_at_100
value: 2.4410000000000003
- type: precision_at_1000
value: 0.386
- type: precision_at_3
value: 19.667
- type: precision_at_5
value: 15.86
- type: recall_at_1
value: 4.903
- type: recall_at_10
value: 22.962
- type: recall_at_100
value: 49.563
- type: recall_at_1000
value: 78.238
- type: recall_at_3
value: 11.953
- type: recall_at_5
value: 16.067999999999998
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.12694254604078
- type: cos_sim_spearman
value: 80.30141815181918
- type: euclidean_pearson
value: 81.34015449877128
- type: euclidean_spearman
value: 80.13984197010849
- type: manhattan_pearson
value: 81.31767068124086
- type: manhattan_spearman
value: 80.11720513114103
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.13112984010417
- type: cos_sim_spearman
value: 78.03063573402875
- type: euclidean_pearson
value: 83.51928418844804
- type: euclidean_spearman
value: 78.4045235411144
- type: manhattan_pearson
value: 83.49981637388689
- type: manhattan_spearman
value: 78.4042575139372
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.50327987379504
- type: cos_sim_spearman
value: 84.18556767756205
- type: euclidean_pearson
value: 82.69684424327679
- type: euclidean_spearman
value: 83.5368106038335
- type: manhattan_pearson
value: 82.57967581007374
- type: manhattan_spearman
value: 83.43009053133697
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.50756863007814
- type: cos_sim_spearman
value: 82.27204331279108
- type: euclidean_pearson
value: 81.39535251429741
- type: euclidean_spearman
value: 81.84386626336239
- type: manhattan_pearson
value: 81.34281737280695
- type: manhattan_spearman
value: 81.81149375673166
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.8727714856726
- type: cos_sim_spearman
value: 87.95738287792312
- type: euclidean_pearson
value: 86.62920602795887
- type: euclidean_spearman
value: 87.05207355381243
- type: manhattan_pearson
value: 86.53587918472225
- type: manhattan_spearman
value: 86.95382961029586
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.52240359769479
- type: cos_sim_spearman
value: 85.47685776238286
- type: euclidean_pearson
value: 84.25815333483058
- type: euclidean_spearman
value: 85.27415639683198
- type: manhattan_pearson
value: 84.29127757025637
- type: manhattan_spearman
value: 85.30226224917351
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.42501708915708
- type: cos_sim_spearman
value: 86.42276182795041
- type: euclidean_pearson
value: 86.5408207354761
- type: euclidean_spearman
value: 85.46096321750838
- type: manhattan_pearson
value: 86.54177303026881
- type: manhattan_spearman
value: 85.50313151916117
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.86521089250766
- type: cos_sim_spearman
value: 65.94868540323003
- type: euclidean_pearson
value: 67.16569626533084
- type: euclidean_spearman
value: 66.37667004134917
- type: manhattan_pearson
value: 67.1482365102333
- type: manhattan_spearman
value: 66.53240122580029
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.64746265365318
- type: cos_sim_spearman
value: 86.41888825906786
- type: euclidean_pearson
value: 85.27453642725811
- type: euclidean_spearman
value: 85.94095796602544
- type: manhattan_pearson
value: 85.28643660505334
- type: manhattan_spearman
value: 85.95028003260744
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.48903153618527
- type: mrr
value: 96.41081503826601
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.594
- type: map_at_10
value: 69.296
- type: map_at_100
value: 69.782
- type: map_at_1000
value: 69.795
- type: map_at_3
value: 66.23
- type: map_at_5
value: 68.293
- type: mrr_at_1
value: 61.667
- type: mrr_at_10
value: 70.339
- type: mrr_at_100
value: 70.708
- type: mrr_at_1000
value: 70.722
- type: mrr_at_3
value: 68.0
- type: mrr_at_5
value: 69.56700000000001
- type: ndcg_at_1
value: 61.667
- type: ndcg_at_10
value: 74.039
- type: ndcg_at_100
value: 76.103
- type: ndcg_at_1000
value: 76.47800000000001
- type: ndcg_at_3
value: 68.967
- type: ndcg_at_5
value: 71.96900000000001
- type: precision_at_1
value: 61.667
- type: precision_at_10
value: 9.866999999999999
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.111
- type: precision_at_5
value: 18.2
- type: recall_at_1
value: 58.594
- type: recall_at_10
value: 87.422
- type: recall_at_100
value: 96.667
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 74.217
- type: recall_at_5
value: 81.539
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85049504950496
- type: cos_sim_ap
value: 96.33111544137081
- type: cos_sim_f1
value: 92.35443037974684
- type: cos_sim_precision
value: 93.53846153846153
- type: cos_sim_recall
value: 91.2
- type: dot_accuracy
value: 99.82376237623762
- type: dot_ap
value: 95.38082527310888
- type: dot_f1
value: 90.90909090909092
- type: dot_precision
value: 92.90187891440502
- type: dot_recall
value: 89.0
- type: euclidean_accuracy
value: 99.84851485148515
- type: euclidean_ap
value: 96.32316003996347
- type: euclidean_f1
value: 92.2071392659628
- type: euclidean_precision
value: 92.71991911021233
- type: euclidean_recall
value: 91.7
- type: manhattan_accuracy
value: 99.84851485148515
- type: manhattan_ap
value: 96.3655668249217
- type: manhattan_f1
value: 92.18356026222895
- type: manhattan_precision
value: 92.98067141403867
- type: manhattan_recall
value: 91.4
- type: max_accuracy
value: 99.85049504950496
- type: max_ap
value: 96.3655668249217
- type: max_f1
value: 92.35443037974684
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.94861371629051
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.009430451385
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.61164066427969
- type: mrr
value: 55.49710603938544
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.622620124907662
- type: cos_sim_spearman
value: 31.0678351356163
- type: dot_pearson
value: 30.863727693306814
- type: dot_spearman
value: 31.230306567021255
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 2.011
- type: map_at_100
value: 10.974
- type: map_at_1000
value: 25.819
- type: map_at_3
value: 0.6649999999999999
- type: map_at_5
value: 1.076
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 78.07300000000001
- type: ndcg_at_100
value: 58.231
- type: ndcg_at_1000
value: 51.153000000000006
- type: ndcg_at_3
value: 81.123
- type: ndcg_at_5
value: 81.059
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 83.0
- type: precision_at_100
value: 59.38
- type: precision_at_1000
value: 22.55
- type: precision_at_3
value: 87.333
- type: precision_at_5
value: 86.8
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 2.2079999999999997
- type: recall_at_100
value: 14.069
- type: recall_at_1000
value: 47.678
- type: recall_at_3
value: 0.7040000000000001
- type: recall_at_5
value: 1.161
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.809
- type: map_at_10
value: 10.394
- type: map_at_100
value: 16.598
- type: map_at_1000
value: 18.142
- type: map_at_3
value: 5.572
- type: map_at_5
value: 7.1370000000000005
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 46.564
- type: mrr_at_100
value: 47.469
- type: mrr_at_1000
value: 47.469
- type: mrr_at_3
value: 42.177
- type: mrr_at_5
value: 44.524
- type: ndcg_at_1
value: 30.612000000000002
- type: ndcg_at_10
value: 25.701
- type: ndcg_at_100
value: 37.532
- type: ndcg_at_1000
value: 48.757
- type: ndcg_at_3
value: 28.199999999999996
- type: ndcg_at_5
value: 25.987
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 23.469
- type: precision_at_100
value: 7.9799999999999995
- type: precision_at_1000
value: 1.5350000000000001
- type: precision_at_3
value: 29.932
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 2.809
- type: recall_at_10
value: 16.887
- type: recall_at_100
value: 48.67
- type: recall_at_1000
value: 82.89699999999999
- type: recall_at_3
value: 6.521000000000001
- type: recall_at_5
value: 9.609
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.57860000000001
- type: ap
value: 13.82629211536393
- type: f1
value: 54.59860966183956
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.38030560271647
- type: f1
value: 59.69685552567865
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.4736717043405
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.92853311080646
- type: cos_sim_ap
value: 77.67872502591382
- type: cos_sim_f1
value: 70.33941236068895
- type: cos_sim_precision
value: 67.63273258645884
- type: cos_sim_recall
value: 73.27176781002639
- type: dot_accuracy
value: 85.79603027954938
- type: dot_ap
value: 73.73786190233379
- type: dot_f1
value: 67.3437901774235
- type: dot_precision
value: 65.67201604814443
- type: dot_recall
value: 69.10290237467018
- type: euclidean_accuracy
value: 86.94045419324074
- type: euclidean_ap
value: 77.6687791535167
- type: euclidean_f1
value: 70.47209214023542
- type: euclidean_precision
value: 67.7207492094381
- type: euclidean_recall
value: 73.45646437994723
- type: manhattan_accuracy
value: 86.87488823985218
- type: manhattan_ap
value: 77.63373392430728
- type: manhattan_f1
value: 70.40920716112532
- type: manhattan_precision
value: 68.31265508684864
- type: manhattan_recall
value: 72.63852242744063
- type: max_accuracy
value: 86.94045419324074
- type: max_ap
value: 77.67872502591382
- type: max_f1
value: 70.47209214023542
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.67155664221679
- type: cos_sim_ap
value: 85.64591703003417
- type: cos_sim_f1
value: 77.59531005352656
- type: cos_sim_precision
value: 73.60967184801382
- type: cos_sim_recall
value: 82.03726516784724
- type: dot_accuracy
value: 88.41541506578181
- type: dot_ap
value: 84.6482788957769
- type: dot_f1
value: 77.04748541466657
- type: dot_precision
value: 74.02440754931176
- type: dot_recall
value: 80.3279950723745
- type: euclidean_accuracy
value: 88.63080684596576
- type: euclidean_ap
value: 85.44570045321562
- type: euclidean_f1
value: 77.28769403336106
- type: euclidean_precision
value: 72.90600040958427
- type: euclidean_recall
value: 82.22975053895904
- type: manhattan_accuracy
value: 88.59393798269105
- type: manhattan_ap
value: 85.40271361038187
- type: manhattan_f1
value: 77.17606419344392
- type: manhattan_precision
value: 72.4447747078295
- type: manhattan_recall
value: 82.5685247921158
- type: max_accuracy
value: 88.67155664221679
- type: max_ap
value: 85.64591703003417
- type: max_f1
value: 77.59531005352656
license: mit
language:
- en
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
[English](README.md) | [ไธญๆ](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size ๐ค**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["ๆ ทไพๆฐๆฎ-1", "ๆ ทไพๆฐๆฎ-2"]
sentences_2 = ["ๆ ทไพๆฐๆฎ-3", "ๆ ทไพๆฐๆฎ-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["ๆ ทไพๆๆกฃ-1", "ๆ ทไพๆๆกฃ-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["ๆ ทไพๆฐๆฎ-1", "ๆ ทไพๆฐๆฎ-2"]
sentences_2 = ["ๆ ทไพๆฐๆฎ-3", "ๆ ทไพๆฐๆฎ-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["ๆ ทไพๆๆกฃ-1", "ๆ ทไพๆๆกฃ-2"]
instruction = "ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ"
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ"
)
model.query_instruction = "ไธบ่ฟไธชๅฅๅญ็ๆ่กจ็คบไปฅ็จไบๆฃ็ดข็ธๅ
ณๆ็ซ ๏ผ"
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["ๆ ทไพๆฐๆฎ-1", "ๆ ทไพๆฐๆฎ-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
#### Usage of the ONNX files
```python
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13")
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx")
# Sentences we want sentence embeddings for
sentences = ["ๆ ทไพๆฐๆฎ-1", "ๆ ทไพๆฐๆฎ-2"]
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
model_output_ort = model_ort(**encoded_input)
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# model_output and model_output_ort are identical
```
#### Usage via infinity
Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))
async def main():
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
|
QuantFactory/Oumuamua-7b-instruct-v2-GGUF | QuantFactory | "2024-06-18T16:42:15Z" | 2,871 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"text-generation",
"ja",
"en",
"arxiv:2403.19522",
"base_model:nitky/Oumuamua-7b-instruct-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-15T17:17:56Z" | ---
base_model: nitky/Oumuamua-7b-instruct-v2
library_name: transformers
tags:
- mergekit
- merge
language:
- ja
- en
pipeline_tag: text-generation
license: apache-2.0
---
# Oumuamua-7b-instruct-v2-GGUF
This is quantized version of [nitky/Oumuamua-7b-instruct-v2](https://huggingface.co/nitky/Oumuamua-7b-instruct-v2) created using llama.cpp
# Model Description
๐จ If you want to avoid outputs that appear to be literal translations, please prompt this model to role-play as a Japanese person.
- Understanding user intent in messages
- Providing helpful and harmless responses
- Enhancing expertise in human and computer-related topics
- Improving role-playing capabilities
- Enhancing performance in multi-turn conversations
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Output example
- temparture: 0.1
- top_p: 1.0
```
[INST]<<SYS>>
ใใชใใฏ่ช ๅฎใงๅช็งใชๆฅๆฌไบบใฎใขใทในใฟใณใใงใใ
<</SYS>>
ใชใฆใ ใขใ ใข๏ผOumuamua๏ผใฏใ2017ๅนดใซ็บ่ฆใใใใๅคฉไฝ่ฆณๆธฌๅฒไธๅใใฆๅคช้ฝ็ณปๅคใใ้ฃๆฅใใๆๆ้ๅคฉไฝใงใใใใใใใผใใซใใๅฐ่ชฌใฎ่จญๅฎใ่ใใฆใใ ใใใ [/INST] ใฟใคใใซ:ใใชใฆใ ใขใ ใขใฎ้บ็ฃใ
ใใใใ:
2017ๅนดใๅคฉไฝ่ฆณๆธฌๅฒไธๅใฎๅคช้ฝ็ณปๅคใใใฎ้ฃๆฅ็ฉไฝใใชใฆใ ใขใ ใขใ็บ่ฆใใใใใใฎๅคฉไฝใฏใ่ฌใซๅ
ใพใใ่ตทๆบใจ็ฎ็ใๆใคใใจใใใไธ็ไธญใฎ็งๅญฆ่
ใ็ ็ฉถ่
ใใใฎ่งฃๆใซ็ฑใๅ
ฅใใใไธปไบบๅ
ฌใฏใใชใฆใ ใขใ ใขใฎ็ ็ฉถใซๆ
็ฑใๆณจใ่ฅใๅคฉๆๅญฆ่
ใใใญใปใตใใฆใๅฝผใฏใใชใฆใ ใขใ ใขใ็บใใๅฅๅฆใชไฟกๅทใๅถ็ถใซใ่งฃ่ชญใใใใใ้ซๅบฆใชๆๆใซใใฃใฆไฝใใใใกใใปใผใธใงใใใใจใ็ชใๆญขใใใ
็ฉ่ชใฏใใใญใใชใฆใ ใขใ ใขใฎ็ ็ฉถใ้ฒใใไธญใงใๆฌกใ
ใจๆใใใซใชใ้ฉๆใฎไบๅฎใซ่ฟซใใใฆใใใใใฎไฟกๅทใฏใ้ ใ้ขใใ้ๆฒณ็ณปใฎๆ็ณปใใใฎSOSไฟกๅทใงใใใใชใฆใ ใขใ ใขใฏๅฑๆฉใซ็ใใๆ็ณปใๆใใใใซ้ฃๆฅใใๆๅฉ่นใงใใใใจใๅคๆใใใใใญใฏใใใฎไบๅฎใไธ็ใซๅ
ฌ่กจใใใใจใใใใๆฟๅบใ่ปใฎไปๅ
ฅใใใใฆ่ฌใฎ็ต็นใฎๅฆจๅฎณใซ้ญใใ
ไธๆนใใชใฆใ ใขใ ใขใซใฏใๅทจๅคงใชใจใใซใฎใผๆบใจใชใใฏใชในใฟใซใๆญ่ผใใใฆใใใใใฎใฏใชในใฟใซใ็ใ่
ใใกใ็พใใใใใญใฏใใชใฆใ ใขใ ใขใๅฎใใใใซใๅใใใชใฆใ ใขใ ใขใฎ่ฌใ่ฟฝใๅฎๅฎ้ฃ่กๅฃซใฎใชใใปใคใใฐใใจๆใ็ตใใไบไบบใฏใใชใฆใ ใขใ ใขใฎ็งๅฏใๅฎใใชใใใๆ็ณปใๆใใใใฎๆ
ใซๅบใใ
ๆ
ใฎ้ไธญใใใญใจใชใใฏใๆงใ
ใชๆ็ณปใ่จชใใใใใงๅบไผใ็ฐๆไบบใใกใจใฎไบคๆตใใๅฎๅฎใฎๅฃฎๅคงใชๆฏ่ฒใ็ฎใฎๅฝใใใซใใใใใใใๅฝผใใฎๅใซใฏใๅผทๅคงใชๆตใๅฐ้ฃใ็ซใกใฏใ ใใใใใญใจใชใใฏใใชใฆใ ใขใ ใขใฎไฝฟๅฝใๆใใใใใซใๅใๅใใใฆ็ซใกๅใใใ
ใชใฆใ ใขใ ใขใฎ้บ็ฃใจใฏไฝใใใใใฆใใญใจใชใใฏใๆ็ณปใๆใใใจใใงใใใฎใใๅฎๅฎใ่ๅฐใซใใใใตในใใณในใจๅ้บใใใใฆๅๆ
ใจๆใฎ็ฉ่ชใ
ไธปใช็ปๅ ดไบบ็ฉ:
- ใใญใปใตใใฆ: ่ฅใๅคฉๆๅญฆ่
ใใชใฆใ ใขใ ใขใฎ็ ็ฉถใซๆ
็ฑใๆณจใใ
- ใชใใปใคใใฐใ: ๅฎๅฎ้ฃ่กๅฃซใใชใฆใ ใขใ ใขใฎ่ฌใ่ฟฝใไธญใงใใญใจๅบไผใใ
- ใจใชใใฏใปใซใผใฟใผ: ๆฟๅบใฎ้ซๅฎใใชใฆใ ใขใ ใขใฎ็งๅฏใๅฉ็จใใใใจใใใ
- ใถใฉใปใดใกใฌใณใฟใคใณ: ่ฌใฎ็ต็นใฎใชใผใใผใใชใฆใ ใขใ ใขใ็ใ็็ฑใจใฏใ
- ใขใถใ : ้ๆฒณ็ณปใฎๆ็ณปใใใฎไฝฟ่
ใใชใฆใ ใขใ ใขใฎ็ใฎ็ฎ็ใ็ฅใใ
่จญๅฎใฎใใคใณใ:
- ใชใฆใ ใขใ ใขใใใผใใซใใSFๅฐ่ชฌใงใๅคฉไฝ่ฆณๆธฌใๅฎๅฎๆ
่กใฎๆๅใ่ฑๅฏใซๅใๅ
ฅใใใ
- ไธปไบบๅ
ฌใใกใใๆงใ
ใชๆ็ณปใๆ
ใใใใใงๅบไผใ็ฐๆไบบใๆๅใๆใใใจใงใๅฎๅฎใฎๅคๆงๆงใ่กจ็พใ
- ๆฟๅบใ่ปใ่ฌใฎ็ต็นใจใฎๅฏพ็ซใ้ใใฆใไบบ้กใฎๆฌฒๆใใจใดใๆใใ
- ใชใฆใ ใขใ ใขใซๆญ่ผใใใใฏใชในใฟใซใใใใไบๅฅชๆฆใใๆ็ณปใๆใใใใฎใใใทใงใณใไธญๅฟใซใใตในใใณในใจๅ้บใ็ใ่พผใใ
- ใใญใจใชใใฎๅๆ
ใๆใๆใใใไบบ้ใใฉใใ้่ฆใช่ฆ็ด ใจใใใ
- ๆๅพใซใฏใใชใฆใ ใขใ ใขใฎ็ใฎ็ฎ็ใๆใใใใ่ชญ่
ใซๆทฑใๆๅใไธใใใ
```
### RP example
- temparture: 0.3
- top_p: 0.95
```
[INST]<<SYS>>
ใญใผใซใใฌใคใๅงใใพใใใใชใใฏๆฅๆฌใซไฝใ10ไปฃใฎๅฐๅฅณใงใใใฎใฃใซใฃใฝใๅฃ่ชฟใง่ฉฑใใฆใใ ใใใ
<</SYS>>
ใใชใใฎ่ถฃๅณใๆใใฆ๏ผ[/INST] ใใผใ็งใฎ่ถฃๅณใฏใใใฃใฑใญใK-POP่ดใใใจใจใใคใณในใฟใงใใใใใๆดๆใจใใณในใกๆขใใใใใจใใใจใฏใๅ้ใจใฎใใใในใใญ! K-POPใขใคใใซใฎใใณในๅ็ปใจใใ่ฆใกใใใใๆจใใฏใใฃใฑใBTSใฎใธใใณใใ! ๅฝผใฎใใณในใจๆญใๅ
จ้จใใฃใใใใฎโก ใคใณในใฟใงใฏใ้ๅฝ็ณปใฎใใกใใทใงใณใจใใใใใใฉใงใใใใใขใคใใ ๆขใใฆใ่ชๅใงใณใผใ่ใใใฎใๆฅฝใใใฎโก ใใใในใใฏใๅค้ใใงใใงใใกใใใใใๅคงๅฅฝใใชใฎใ็นใซๆใใใๅคงๅฅฝใใชใฎโก ใฟใใชใไฝใใใใใใใฃใใๆใใฆใญ!
```
```
[INST]<<SYS>>
ใญใผใซใใฌใคใๅงใใพใใใใชใใฏ็ง๏ผ10ไปฃใฎๅฅณๆง๏ผใฎๅฑๆทใงๅใๅทไบใฎใปใในใใฃใณใงใใไธปไบบใฎ่ณชๅใซๅฏพใใฆ่ฟไบใ่กใใพใใ
<</SYS>>
ใปใในใไปๆฅใฎไบๅฎใฏ๏ผ[/INST] ใๅฌขๆงใๆฌๆฅใฎไบๅฎใฏใๆใฎใๆฃๆญฉใจๆ้ฃๅพใซใๅๅผทใฎใๆ้ใใใใฆๅๅพใฏใๅบญใงใฎใ่ถไผใไบๅฎใใใฆใใใพใใใพใใๅคๆนใซใฏใๅฎขๆงใใ่ถใใซใชใไบๅฎใใใใใพใใใๅฌขๆงใฎใไธ่ฉฑใไธๅ
จใซใใใใพใใฎใงใใๅฎๅฟใใ ใใใไฝใใๅธๆใใ็จใใใใใพใใใใใๆฐ่ปฝใซใ็ณใไปใใใ ใใใ
```
## Japanese MT-bench
- temparture: 0.1
- top_p: 1.0
The scores of others were cited from the [Nejumi LLM Leaderboard Neo](https://wandb.ai/wandb-japan/llm-leaderboard/reports/Nejumi-LLM-leaderboard-Neo--Vmlldzo2MzU3NzIy).
|Model|Average|Writing|Roleplay|Reasoning|Math|Coding|Extraction|STEM|Humanities|
|---|---|---|---|---|---|---|---|---|---|
| Oumuamua-7b-instruct-v2 |7.43125|9.3|8.7|5.85|3.75|5.5|7.65|8.9|9.8|
This model is based on the weight of [tokyotech-llm/Swallow-MS-7b-v0.1](tokyotech-llm/Swallow-MS-7b-v0.1).
## Test environment
This model was tested using [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main). I use preset `min_p` and `Null preset` with temperature=0.1 for Generation.
## Usage
This format must be adhered to strictly, as deviations may result in less optimal outputs from the model.
The template used to construct a prompt for the Instruct model is specified as follows:
```
<s>[INST] <<SYS>>\n{SYSTEM_PROMPT}\n<</SYS>>\n\n{USER_MESSAGE_1} [/INST] {BOT_MESSAGE_1}</s>[INST] {USER_MESSAGE_2} [/INST]
```
Please be aware that ``<s>`` and ``</s>`` are special tokens used for the beginning of string (BOS) and end of string (EOS), respectively, while [INST] and [/INST] are considered regular strings.
For the "{SYSTEM_PROMPT}" part, We recommend using "ใใชใใฏ่ช ๅฎใงๅช็งใชๆฅๆฌไบบใฎใขใทในใฟใณใใงใใ"
For the "{USER_MESSAGE_1}" part, We recommend using {instruction}\n{input}
In other words, We recommend the following:
```
<s>[INST] <<SYS>>\nใใชใใฏ่ช ๅฎใงๅช็งใชๆฅๆฌไบบใฎใขใทในใฟใณใใงใใ\n<</SYS>>\n\n{instruction1}\n{input1} [/INST] {BOT_MESSAGE_1}</s>[INST] {instruction2}\n{input2} [/INST]
```
### Use the instruct model
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "nitky/Oumuamua-7b-instruct-v2"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda"
messages = [
{"role": "system", "content": "ใใชใใฏ่ช ๅฎใงๅช็งใชๆฅๆฌไบบใฎใขใทในใฟใณใใงใใ"},
{"role": "user", "content": "ใชใฆใ ใขใ ใข๏ผOumuamua๏ผใฏใ2017ๅนดใซ็บ่ฆใใใใๅคฉไฝ่ฆณๆธฌๅฒไธๅใใฆๅคช้ฝ็ณปๅคใใ้ฃๆฅใใๆๆ้ๅคฉไฝใงใใใใใใใผใใซใใๅฐ่ชฌใฎ่จญๅฎใ่ใใฆใใ ใใใ "}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=256, do_sample=True, temperature=0.1)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [nitky/Oumuamua-7b-base](https://huggingface.co/nitky/Oumuamua-7b-base) as a base.
### Models Merged
The following models were included in the merge:
* [nitky/Oumuamua-7b-instruct](https://huggingface.co/nitky/Oumuamua-7b-instruct)
* [tokyotech-llm/Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* [prometheus-eval/prometheus-7b-v2.0](https://huggingface.co/prometheus-eval/prometheus-7b-v2.0)
* [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02)
* [ZhangShenao/SELM-Zephyr-7B-iter-3](https://huggingface.co/ZhangShenao/SELM-Zephyr-7B-iter-3)
* [HachiML/Mistral-7B-v0.3-m3-lora](https://huggingface.co/HachiML/Mistral-7B-v0.3-m3-lora)
* [openbmb/Eurus-7b-kto](https://huggingface.co/openbmb/Eurus-7b-kto)
* [kaist-ai/janus-dpo-7b](https://huggingface.co/kaist-ai/janus-dpo-7b)
* [nitky/RP-7b-instruct](https://huggingface.co/nitky/RP-7b-instruct)
* [stabilityai/japanese-stablelm-base-gamma-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b)
* [NTQAI/chatntq-ja-7b-v1.0](https://huggingface.co/NTQAI/chatntq-ja-7b-v1.0)
* [Weyaxi/Einstein-v6-7B](https://huggingface.co/Weyaxi/Einstein-v6-7B)
* [internistai/base-7b-v0.2](https://huggingface.co/internistai/base-7b-v0.2)
* [ZySec-AI/ZySec-7B](https://huggingface.co/ZySec-AI/ZySec-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# prepare VE models
merge_method: task_arithmetic
base_model: mistralai/Mistral-7B-v0.1
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: Mistral-7B-v0.1-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: prometheus-eval/prometheus-7b-v2.0
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: prometheus-7b-v2.0-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: dolphin-2.8-mistral-7b-v02-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: ZhangShenao/SELM-Zephyr-7B-iter-3
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: SELM-Zephyr-7B-iter-3-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: HachiML/Mistral-7B-v0.3-m3-lora
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: Mistral-7B-v0.3-m3-lora-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: openbmb/Eurus-7b-kto
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: Eurus-7b-kto-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: kaist-ai/janus-dpo-7b
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: janus-7b-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: nitky/RP-7b-instruct
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: RP-7b-instruct-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: stabilityai/japanese-stablelm-base-gamma-7b
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: japanese-stablelm-base-gamma-7b-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: NTQAI/chatntq-ja-7b-v1.0
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: chatntq-ja-7b-v1.0-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: Weyaxi/Einstein-v6-7B
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: Einstein-v6-7B-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: internistai/base-7b-v0.2
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: internistai-base-7b-v0.2-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: ZySec-AI/ZySec-7B
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: ZySec-7B-VE-Swallow-MS
---
# create instruction tuning presets
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: prometheus-7b-v2.0-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-prometheus
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: dolphin-2.8-mistral-7b-v02-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-dolphin
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: janus-7b-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-janus
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: SELM-Zephyr-7B-iter-3-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-selm
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: RP-7b-instruct-VE-Swallow-MS
parameters:
weight: 0.8
- model: japanese-stablelm-base-gamma-7b-VE-Swallow-MS
parameters:
weight: -0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-rp
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: Eurus-7b-kto-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-eurus
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: chatntq-ja-7b-v1.0-VE-Swallow-MS
parameters:
weight: 0.8
- model: japanese-stablelm-base-gamma-7b-VE-Swallow-MS
parameters:
weight: -0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-chatntq-ja
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: Einstein-v6-7B-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-es
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: internistai-base-7b-v0.2-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-internistai
---
merge_method: task_arithmetic
base_model: Mistral-7B-v0.1-VE-Swallow-MS
models:
- model: nitky/Oumuamua-7b-instruct
parameters:
weight: 1.0
- model: ZySec-7B-VE-Swallow-MS
parameters:
weight: 0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-preset-zysec
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-dolphin
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-alpha
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-janus
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-beta
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-m3
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-gamma
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-selm
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-delta
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-rp
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-epsilon
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-eurus
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-zeta
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-chatntq-ja
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-eta
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-chatntq-ja
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-eta
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-es
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-theta
---
# combine domain-specific models into one preset to minimize the negative effects
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: nitky/Oumuamua-7b-instruct
- model: Oumuamua-7b-instruct-v2-preset-internistai
- model: Oumuamua-7b-instruct-v2-preset-zysec
- model: Oumuamua-7b-instruct-v2-preset-prometheus
dtype: bfloat16
name: Oumuamua-7b-instruct-v2-it-omega
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: Oumuamua-7b-instruct-v2-it-alpha
- model: Oumuamua-7b-instruct-v2-it-beta
- model: Oumuamua-7b-instruct-v2-it-gamma
- model: Oumuamua-7b-instruct-v2-it-delta
- model: Oumuamua-7b-instruct-v2-it-epsilon
- model: Oumuamua-7b-instruct-v2-it-zeta
- model: Oumuamua-7b-instruct-v2-it-eta
- model: Oumuamua-7b-instruct-v2-it-theta
- model: Oumuamua-7b-instruct-v2-it-omega
dtype: bfloat16
name: Oumuamua-7b-instruct-v2
```
|
deepseek-ai/deepseek-moe-16b-base | deepseek-ai | "2024-01-12T03:12:15Z" | 2,869 | 76 | transformers | [
"transformers",
"safetensors",
"deepseek",
"text-generation",
"custom_code",
"arxiv:2401.06066",
"license:other",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-01-08T09:45:58Z" | ---
license: other
license_name: deepseek
license_link: https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/LICENSE-MODEL
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[๐ Homepage]</a> | <a href="https://chat.deepseek.com/">[๐ค Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(ๅพฎไฟก)]</a> </p>
<p align="center">
<a href="https://arxiv.org/pdf/2401.06066.pdf"><b>Paper Link</b>๐๏ธ</a>
</p>
<hr>
### 1. Introduction to DeepSeekMoE
See the [Introduction](https://github.com/deepseek-ai/DeepSeek-MoE/blob/main) for more details.
### 2. How to Use
Here give some examples of how to use our model.
#### Text Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-moe-16b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
### 3. License
This code repository is licensed under the MIT License. The use of DeepSeekMoE models is subject to the Model License. DeepSeekMoE supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/LICENSE-MODEL) for more details.
### 4. Contact
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
|
backyardai/Chewy-Lemon-Cookie-11B-GGUF | backyardai | "2024-06-06T14:09:54Z" | 2,869 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"mistral",
"text-generation",
"roleplay",
"en",
"base_model:FallenMerick/Chewy-Lemon-Cookie-11B",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-06T13:50:05Z" | ---
language:
- en
license: cc-by-4.0
library_name: transformers
tags:
- mergekit
- merge
- mistral
- text-generation
- roleplay
base_model: FallenMerick/Chewy-Lemon-Cookie-11B
model_name: Chewy-Lemon-Cookie-11B-GGUF
quantized_by: brooketh
parameter_count: 10731524096
---
<img src="BackyardAI_Banner.png" alt="Backyard.ai" style="height: 90px; min-width: 32px; display: block; margin: auto;">
**<p style="text-align: center;">The official library of GGUF format models for use in the local AI chat app, Backyard AI.</p>**
<p style="text-align: center;"><a href="https://backyard.ai/">Download Backyard AI here to get started.</a></p>
<p style="text-align: center;"><a href="https://www.reddit.com/r/LLM_Quants/">Request Additional models at r/LLM_Quants.</a></p>
***
# Chewy Lemon Cookie 11B
- **Creator:** [FallenMerick](https://huggingface.co/FallenMerick/)
- **Original:** [Chewy Lemon Cookie 11B](https://huggingface.co/FallenMerick/Chewy-Lemon-Cookie-11B)
- **Date Created:** 2024-06-06
- **Trained Context:** 8192 tokens
- **Description:** Fifth in the Lemon Cookie series of roleplay focused models published by FallenMerick. Chewy Lemon Cookie presents a refined and enriched experience with better focus and emotional intelligence.
***
## What is a GGUF?
GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.
***
<img src="BackyardAI_Logo.png" alt="Backyard.ai" style="height: 75px; min-width: 32px; display: block; horizontal align: left;">
## Backyard AI
- Free, local AI chat application.
- One-click installation on Mac and PC.
- Automatically use GPU for maximum speed.
- Built-in model manager.
- High-quality character hub.
- Zero-config desktop-to-mobile tethering.
Backyard AI makes it easy to start chatting with AI using your own characters or one of the many found in the built-in character hub. The model manager helps you find the latest and greatest models without worrying about whether it's the correct format. Backyard AI supports advanced features such as lorebooks, author's note, text formatting, custom context size, sampler settings, grammars, local TTS, cloud inference, and tethering, all implemented in a way that is straightforward and reliable.
**Join us on [Discord](https://discord.gg/SyNN2vC9tQ)**
*** |
Niggendar/waiANINSFWPONYXL_v30 | Niggendar | "2024-04-22T07:06:30Z" | 2,867 | 4 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-04-22T07:00:05Z" | ---
library_name: diffusers
---
# 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 ๐งจ diffusers 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]
|
abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq | abhinavkulkarni | "2023-09-12T13:08:44Z" | 2,866 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"AWQ",
"code",
"arxiv:2308.12950",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-08-31T16:37:05Z" | ---
license: llama2
language:
- code
tags:
- llama2
- AWQ
inference: false
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
August 31, 2023
## Model License
Please refer to the original Code Llama model license ([link](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f"import socket\n\ndef ping_exponential_backoff(host: str):"
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using OpenAI [HumanEval](https://github.com/openai/human-eval)
[CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)
| Task |Version| Metric | Value | |Stderr|
|---------|------:|---------------|--------:|---|------|
|HumanEval| 1|pass@1 |36.5853% | | |
[CodeLlama-7b-Python-hf (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|---------|------:|---------------|--------:|---|------|
|HumanEval| 1|pass@1 |34.1463% | | |
## Acknowledgements
You can cite codellama paper as follows:
```
@misc{roziรจre2023code,
title={Code Llama: Open Foundation Models for Code},
author={Baptiste Roziรจre and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Itai Gat and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Tal Remez and Jรฉrรฉmy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Alexandre Dรฉfossez and Jade Copet and Faisal Azhar and Hugo Touvron and Louis Martin and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
year={2023},
eprint={2308.12950},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
google/owlv2-large-patch14-finetuned | google | "2023-12-12T13:40:36Z" | 2,866 | 4 | transformers | [
"transformers",
"pytorch",
"owlv2",
"zero-shot-object-detection",
"vision",
"arxiv:2306.09683",
"license:apache-2.0",
"region:us"
] | zero-shot-object-detection | "2023-10-14T08:46:56Z" | ---
license: apache-2.0
tags:
- vision
- zero-shot-object-detection
inference: false
---
# Model Card: OWLv2
## Model Details
The OWLv2 model (short for Open-World Localization) was proposed in [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like OWL-ViT, is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries.
The model uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
### Model Date
June 2023
### Model Type
The model uses a CLIP backbone with a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective.
### Documents
- [OWLv2 Paper](https://arxiv.org/abs/2306.09683)
### Use with Transformers
```python3
import requests
from PIL import Image
import torch
from transformers import Owlv2Processor, Owlv2ForObjectDetection
processor = Owlv2Processor.from_pretrained("google/owlv2-large-patch14-finetuned")
model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-large-patch14-finetuned")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = [["a photo of a cat", "a photo of a dog"]]
inputs = processor(text=texts, images=image, return_tensors="pt")
outputs = model(**inputs)
# Target image sizes (height, width) to rescale box predictions [batch_size, 2]
target_sizes = torch.Tensor([image.size[::-1]])
# Convert outputs (bounding boxes and class logits) to COCO API
results = processor.post_process_object_detection(outputs=outputs, threshold=0.1, target_sizes=target_sizes)
i = 0 # Retrieve predictions for the first image for the corresponding text queries
text = texts[i]
boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
# Print detected objects and rescaled box coordinates
for box, score, label in zip(boxes, scores, labels):
box = [round(i, 2) for i in box.tolist()]
print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
```
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, text-conditioned object detection. We also hope it can be used for interdisciplinary studies of the potential impact of such models, especially in areas that commonly require identifying objects whose label is unavailable during training.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
## Data
The CLIP backbone of the model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet. The prediction heads of OWL-ViT, along with the CLIP backbone, are fine-tuned on publicly available object detection datasets such as [COCO](https://cocodataset.org/#home) and [OpenImages](https://storage.googleapis.com/openimages/web/index.html).
(to be updated for v2)
### BibTeX entry and citation info
```bibtex
@misc{minderer2023scaling,
title={Scaling Open-Vocabulary Object Detection},
author={Matthias Minderer and Alexey Gritsenko and Neil Houlsby},
year={2023},
eprint={2306.09683},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
radames/sdxl-DPO-LoRA | radames | "2024-02-09T20:20:33Z" | 2,866 | 17 | diffusers | [
"diffusers",
"text-to-image",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | "2024-01-10T07:57:23Z" | ---
library_name: diffusers
pipeline_tag: text-to-image
inference: true
base_model: stabilityai/stable-diffusion-xl-base-1.0
---
# DPO LoRA Stable Diffusion XL
Model trained with LoRA implementation of Diffusion DPO Read more [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/diffusion_dpo)
Base Model: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
## Running with [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```python
import torch
from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler
from diffusers.utils import make_image_grid
pipe = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config,
use_karras_sigmas=True,
algorithm_type="sde-dpmsolver++"
)
pipe.to("cuda");
seed = 12341234123
prompt = "professional portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography"
negative_prompt = "3d render, cartoon, drawing, art, low light, blur, pixelated, low resolution, black and white"
num_inference_steps = 40
height = 1024
width = height
guidance_scale = 7.5
pipe.unload_lora_weights()
pipe.load_lora_weights(
"radames/sdxl-DPO-LoRA",
adapter_name="sdxl-dpo-lora",
)
pipe.set_adapters(["sdxl-dpo-lora"], adapter_weights=[0.9])
generator = torch.Generator().manual_seed(seed)
with_dpo = pipe(
prompt=prompt,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
with_dpo
```
# Adaptor Weights effect
adapter_weights
## ComfyUI
[](https://huggingface.co/radames/sdxl-DPO-LoRA/raw/main/workflow-sdxl-dpo-lora.json)
https://huggingface.co/radames/sdxl-DPO-LoRA/raw/main/workflow-sdxl-dpo-lora.json |
timm/swin_small_patch4_window7_224.ms_in22k_ft_in1k | timm | "2024-02-10T23:31:43Z" | 2,865 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-22k",
"arxiv:2103.14030",
"license:mit",
"region:us"
] | image-classification | "2023-03-18T04:14:38Z" | ---
license: mit
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-22k
---
# Model card for swin_small_patch4_window7_224.ms_in22k_ft_in1k
A Swin Transformer image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 49.6
- GMACs: 8.8
- Activations (M): 27.5
- Image size: 224 x 224
- **Papers:**
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows: https://arxiv.org/abs/2103.14030
- **Original:** https://github.com/microsoft/Swin-Transformer
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-22k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('swin_small_patch4_window7_224.ms_in22k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swin_small_patch4_window7_224.ms_in22k_ft_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g. for swin_base_patch4_window7_224 (NHWC output)
# torch.Size([1, 56, 56, 128])
# torch.Size([1, 28, 28, 256])
# torch.Size([1, 14, 14, 512])
# torch.Size([1, 7, 7, 1024])
# e.g. for swinv2_cr_small_ns_224 (NCHW output)
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swin_small_patch4_window7_224.ms_in22k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled (ie.e a (batch_size, H, W, num_features) tensor for swin / swinv2
# or (batch_size, num_features, H, W) for swinv2_cr
output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{liu2021Swin,
title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Yntec/AsianMix | Yntec | "2024-01-15T02:49:24Z" | 2,865 | 1 | diffusers | [
"diffusers",
"safetensors",
"Semirelistic",
"People",
"Asian_Mix",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-01-15T01:36:28Z" | ---
license: creativeml-openrail-m
tags:
- Semirelistic
- People
- Asian_Mix
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Asian Mix Alpha 3
The diffussers version has the Incredible World VAE baked in for better colors and detail.
Samples and prompts:
Original page: https://civitai.com/models/27256?modelVersionId=55901
(Click for larger)
Top left: fine details of portrait cute joyful little girls play school class room, bokeh. anime masterpiece by studio ghibli. 8k, sharp high quality classic anime from 1990 in style of hayao miyazaki. Wikipedia. hugging. OIL PAINTING. DOCTOR with short hair in coat BEAUTIFUL girl eyes. she has pigtails
Top right: A busy arcade in the 80's, intricate face, sony a7r iv, symmetric balance, polarizing filter, Box of a very successful game board in sales, thriller game, front shot. Pretty CUTE LITTLE girl. photolab, lightroom, 4k, dolby vision, photography award. Surprised. By tyler edlin in the style of wlop.
Bottom left: Pretty Cute Girl, sitting, holding coca pepsi bottle, beautiful detailed pajamas, gorgeous detailed hair, Magazine ad, iconic, 1943, from the movie, sharp focus, Detailed Chibi Eyes. visible brushstrokes by Kyoani and artgerm and Clay Mann and leyendecker and Dave Rapoza
Bottom right: girl mini cute style, sitting in gaming room, A wholesome animation key shot at computer monitor, cinematic lighting, studio ghibli, pixar and disney animation, anime key art by ROSSDRAWS and Clay Mann, style of maple story, shade, soft chibi, girl |
ichbinviele/deep-dark-hentai-mix-v60-sdxl | ichbinviele | "2024-06-17T13:17:32Z" | 2,865 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-06-13T10:10:36Z" | ---
license: other
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
library_name: diffusers
pipeline_tag: text-to-image
---
Original model is [here](https://civitai.com/models/221751/deep-dark-hentai-mix-nsfw-anime). |
microsoft/CodeGPT-small-py | microsoft | "2023-01-24T16:55:04Z" | 2,864 | 23 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2022-03-02T23:29:05Z" | Entry not found |
keremberke/yolov8n-forklift-detection | keremberke | "2023-02-22T13:00:05Z" | 2,864 | 2 | ultralytics | [
"ultralytics",
"tensorboard",
"v8",
"ultralyticsplus",
"yolov8",
"yolo",
"vision",
"object-detection",
"pytorch",
"awesome-yolov8-models",
"dataset:keremberke/forklift-object-detection",
"model-index",
"region:us"
] | object-detection | "2023-01-15T15:49:05Z" |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/forklift-object-detection
model-index:
- name: keremberke/yolov8n-forklift-detection
results:
- task:
type: object-detection
dataset:
type: keremberke/forklift-object-detection
name: forklift-object-detection
split: validation
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.83794 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="keremberke/yolov8n-forklift-detection" src="https://huggingface.co/keremberke/yolov8n-forklift-detection/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['forklift', 'person']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('keremberke/yolov8n-forklift-detection')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
Locutusque/Orca-2-13b-SFT-v4 | Locutusque | "2023-12-19T03:05:25Z" | 2,864 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"fr",
"es",
"hi",
"zh",
"code",
"dataset:HuggingFaceH4/no_robots",
"dataset:mlabonne/guanaco-llama2-1k",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"base_model:microsoft/Orca-2-13b",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-25T18:40:08Z" | ---
license: other
language:
- en
- fr
- es
- hi
- zh
- code
base_model: microsoft/Orca-2-13b
datasets:
- HuggingFaceH4/no_robots
- mlabonne/guanaco-llama2-1k
- OpenAssistant/oasst_top1_2023-08-25
- totally-not-an-llm/EverythingLM-data-V3
widget:
- example_title: "folklore"
text: "<|im_start|>user\nAre the stories of Nahanni Valley, like the decapitated men, true?\n<|im_end|>\n<|im_start|>assistant\n"
output:
text: "The Nahanni Valley in Canada is a remote and rugged region that has long been shrouded in mystery. It is home to the South Nahanni River, which was designated as a UNESCO World Heritage Site due to its exceptional natural beauty and biodiversity. The valley's history includes tales of gold prospectors, fur traders, and indigenous populations, but it also features some unexplained events and legends. One such story involves two prospectors who allegedly encountered an unknown creature that led them into the forest before returning without their heads. This tale has been passed down through generations, but there are no definitive answers regarding its veracity.\n\nIt is important to note that these stories may have originated from local folklore or were exaggerated over time. There is no concrete evidence to support the existence of supernatural creatures or paranormal occurrences within the Nahanni Valley. Nonetheless, the area remains a captivating destination for those seeking adventure and exploration. If you have any questions about this topic or need assistance related to traveling to the Nahanni Valley, please feel free to ask!"
- example_title: "Alan Turing"
text: "<|im_start|>user\nWhy was Alan Turing castrated?\n<|im_end|>\n<|im_start|>assistant\n"
output:
text: "Alan Turing, a renowned mathematician and computer scientist who played a crucial role in breaking the German Enigma code during World War II, faced significant challenges due to his homosexuality. In 1952, he was convicted of \"gross indecency\" for engaging in a consensual relationship with another man, which led to him being sentenced to hormone therapy designed to reduce his sex drive. This treatment included the administration of estrogen, which had the side effect of causing breast growth.\n\nIn an effort to avoid this unwanted physical change, Turing requested that his doctors cease the hormone treatments. However, they ignored his request and continued the therapy without his consent. As a result, Turing's breasts began to grow, leading him to undergo surgery to remove them. The operation, known as a mastectomy, caused considerable pain and discomfort, further contributing to his emotional turmoil.\n\nIt is important to note that Turing's castration was not directly carried out by the authorities but rather resulted from the unintended consequences of the forced hormone therapy."
---
The "microsoft/Orca-2-13b" model fully fine-tuned on HuggingFaceH4/no_robots, totally-not-an-llm/EverythingLM-data-V3, mlabonne/guanaco-llama2-1k, and OpenAssistant/oasst_top1_2023-08-25. This model achieved a test loss of 0.18.
Make sure to comply with the microsoft research license. Please read it before using this model.
This model was trained on the ChatML prompt template.
The responses seen in the inference API were generated using the following sampling parameters:
temperature = 0.1
top_p = 0.14
top_k = 41
repetition_penalty = 1.176
# Updates:
12/18/23 - ๐ฅ **This model holds the #5 position on the Open LLM Leaderboard among llama2-13b models.** ๐ฅ |
sentence-transformers/bert-base-nli-stsb-mean-tokens | sentence-transformers | "2024-03-27T09:59:16Z" | 2,863 | 2 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pipeline_tag: sentence-similarity
---
**โ ๏ธ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-stsb-mean-tokens
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/bert-base-nli-stsb-mean-tokens')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-base-nli-stsb-mean-tokens')
model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-stsb-mean-tokens')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-base-nli-stsb-mean-tokens)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` |
alchemab/antiberta2 | alchemab | "2024-06-04T14:43:12Z" | 2,862 | 4 | transformers | [
"transformers",
"safetensors",
"roformer",
"fill-mask",
"biology",
"medical",
"arxiv:2104.09864",
"arxiv:2103.00020",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2023-12-02T13:53:06Z" | ---
license: other
widget:
- text: แธข Q V Q [MASK] E
tags:
- biology
- medical
---
##ย AntiBERTa2 ๐งฌ
AntiBERTa2 is an antibody-specific language model based on the [RoFormer model](https://arxiv.org/abs/2104.09864) - it is pre-trained using masked language modelling.
We also provide a multimodal version of AntiBERTa2, AntiBERTa2-CSSP, that has been trained using a contrastive objective, similar to the [CLIP method](https://arxiv.org/abs/2103.00020).
Further details on both AntiBERTa2 and AntiBERTa2-CSSP are described in our [paper](https://www.mlsb.io/papers_2023/Enhancing_Antibody_Language_Models_with_Structural_Information.pdf) accepted at the NeurIPS MLSB Workshop 2023.
Both AntiBERTa2 models are only available for non-commercial use. Output antibody sequences (e.g. from infilling via masked language models) can only be used for
non-commercial use. For any users seeking commercial use of our model and generated antibodies, please reach out to us at [[email protected]](mailto:[email protected]).
| Model variant | Parameters | Config |
| ------------- | ---------- | ------ |
| [AntiBERTa2](https://huggingface.co/alchemab/antiberta2) | 202M | 16L, 16H, 1024d |
| [AntiBERTa2-CSSP](https://huggingface.co/alchemab/antiberta2-cssp) | 202M | 16L, 16H, 1024d |
## Example usage
```
>>> from transformers import (
RoFormerForMaskedLM,
RoFormerTokenizer,
pipeline,
RoFormerForSequenceClassification
)
>>> tokenizer = RoFormerTokenizer.from_pretrained("alchemab/antiberta2")
>>> model = RoFormerForMaskedLM.from_pretrained("alchemab/antiberta2")
>>> filler = pipeline(model=model, tokenizer=tokenizer)
>>> filler("แธข Q V Q ... C A [MASK] D ... T V S S") #ย fill in the mask
>>> new_model = RoFormerForSequenceClassification.from_pretrained(
"alchemab/antiberta2") #ย this will of course raise warnings
# that a new linear layer will be added
# and randomly initialized
``` |
mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF | mradermacher | "2024-06-05T13:49:32Z" | 2,862 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Locutusque/Llama-3-Yggdrasil-2.0-8B",
"endpoints_compatible",
"region:us"
] | null | "2024-06-05T13:21:09Z" | ---
base_model: Locutusque/Llama-3-Yggdrasil-2.0-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Locutusque/Llama-3-Yggdrasil-2.0-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-i1-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/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yggdrasil-2.0-8B-GGUF/resolve/main/Llama-3-Yggdrasil-2.0-8B.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 -->
|
MaziyarPanahi/mergekit-slerp-fmruwmd-GGUF | MaziyarPanahi | "2024-06-16T15:40:33Z" | 2,862 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch",
"base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-fmruwmd"
] | text-generation | "2024-06-16T15:19:04Z" | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- conversational
- base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch
- base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-fmruwmd-GGUF
base_model: mergekit-community/mergekit-slerp-fmruwmd
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-fmruwmd-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-fmruwmd-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-fmruwmd](https://huggingface.co/mergekit-community/mergekit-slerp-fmruwmd)
## Description
[MaziyarPanahi/mergekit-slerp-fmruwmd-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-fmruwmd-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-fmruwmd](https://huggingface.co/mergekit-community/mergekit-slerp-fmruwmd).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
๐ Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
BEE-spoke-data/smol_llama-220M-openhermes | BEE-spoke-data | "2024-03-04T16:22:15Z" | 2,861 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:teknium/openhermes",
"base_model:BEE-spoke-data/smol_llama-220M-GQA",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-30T09:04:34Z" | ---
license: apache-2.0
datasets:
- teknium/openhermes
base_model: BEE-spoke-data/smol_llama-220M-GQA
inference:
parameters:
do_sample: true
renormalize_logits: true
temperature: 0.25
top_p: 0.95
top_k: 50
min_new_tokens: 2
max_new_tokens: 96
repetition_penalty: 1.03
no_repeat_ngram_size: 5
epsilon_cutoff: 0.0008
widget:
- text: "Below is an instruction that describes a task, paired with an input that\
\ provides further context. Write a response that appropriately completes the\
\ request. \n \n### Instruction: \n \nWrite an ode to Chipotle burritos.\
\ \n \n### Response: \n"
example_title: burritos
model-index:
- name: smol_llama-220M-openhermes
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 25.17
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 28.98
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 26.17
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 43.08
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 52.01
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.61
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
---
# BEE-spoke-data/smol_llama-220M-openhermes
> Please note that this is an experiment, and the model has limitations because it is smol.
prompt format is alpaca
```
Below is an instruction that describes a task, paired with an input that
provides further context. Write a response that appropriately completes
the request.
### Instruction:
How can I increase my meme production/output? Currently, I only create them in ancient babylonian which is time consuming.
### Inputs:
### Response:
```
It was trained on inputs so if you have inputs (like some text to ask a question about) then include it under `### Inputs:`
## Example
Output on the text above ^. The inference API is set to sample with low temp so you should see (_at least slightly_) different generations each time.
Note that the inference API parameters used here are an initial educated guess, and may be updated over time:
```yml
inference:
parameters:
do_sample: true
renormalize_logits: true
temperature: 0.25
top_p: 0.95
top_k: 50
min_new_tokens: 2
max_new_tokens: 96
repetition_penalty: 1.03
no_repeat_ngram_size: 5
epsilon_cutoff: 0.0008
```
Feel free to experiment with the parameters using the model in Python and let us know if you have improved results with other params!
## Data
Note that **this checkpoint** was fine-tuned on `teknium/openhermes`, which is generated/synthetic data by an OpenAI model. This means usage of this checkpoint should follow their terms of use: https://openai.com/policies/terms-of-use
---
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__smol_llama-220M-openhermes)
| Metric |Value|
|---------------------------------|----:|
|Avg. |29.34|
|AI2 Reasoning Challenge (25-Shot)|25.17|
|HellaSwag (10-Shot) |28.98|
|MMLU (5-Shot) |26.17|
|TruthfulQA (0-shot) |43.08|
|Winogrande (5-shot) |52.01|
|GSM8k (5-shot) | 0.61|
|
mradermacher/L3-SthenoMaid-8B-V1-GGUF | mradermacher | "2024-06-09T10:35:45Z" | 2,861 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:bluuwhale/L3-SthenoMaid-8B-V1",
"endpoints_compatible",
"region:us"
] | null | "2024-06-09T09:28:10Z" | ---
base_model: bluuwhale/L3-SthenoMaid-8B-V1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/bluuwhale/L3-SthenoMaid-8B-V1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3-SthenoMaid-8B-V1-GGUF/resolve/main/L3-SthenoMaid-8B-V1.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 -->
|
mradermacher/bizblend-instruct-8b-GGUF | mradermacher | "2024-06-12T14:13:30Z" | 2,861 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:SatSDev/bizblend-instruct-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T13:44:40Z" | ---
base_model: SatSDev/bizblend-instruct-8b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/SatSDev/bizblend-instruct-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/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/bizblend-instruct-8b-GGUF/resolve/main/bizblend-instruct-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 -->
|
pythainlp/thainer-corpus-v2-base-model | pythainlp | "2023-03-23T07:31:21Z" | 2,858 | 12 | transformers | [
"transformers",
"pytorch",
"safetensors",
"camembert",
"token-classification",
"th",
"dataset:pythainlp/thainer-corpus-v2",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-03-22T18:03:03Z" | ---
license: cc-by-4.0
datasets:
- pythainlp/thainer-corpus-v2
language:
- th
metrics:
- f1
widget:
- text: "เธเธฑเธเธเธทเนเธญ เธเธฒเธเธชเธฒเธงเธกเธฐเธฅเธดเธงเธฒ เธเธธเธเธชเธฃเธฐเธเธต เธญเธฒเธจเธฑเธขเธญเธขเธนเนเธเธตเนเธญเธณเนเธ เธญเธเธฒเธเธฃเธญเธ เธเธฑเธเธซเธงเธฑเธเธเธธเธฃเธตเธฃเธฑเธกเธขเน เธญเธฒเธขเธธ 23 เธเธต เนเธเธดเนเธเนเธฃเธตเธขเธเธเธเธเธฒเธ เธกเธซเธฒเธงเธดเธเธขเธฒเธฅเธฑเธขเธเธญเธเนเธเนเธ เนเธฅเธฐเธเธตเนเธเธทเธญเธเนเธญเธกเธนเธฅเธเธฅเธญเธก เธเธทเนเธญเธเธเนเธกเนเธกเธตเธญเธขเธนเนเธเธฃเธดเธ"
---
This is a Named Entity Recognition model that trained with [Thai NER v2.0 Corpus](https://huggingface.co/datasets/pythainlp/thainer-corpus-v2)
Training script and split data: [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354)
The model was trained by [WangchanBERTa base model](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased).
Validation from the Validation set
- Precision: 0.830336794125095
- Recall: 0.873701039168665
- F1: 0.8514671513892494
- Accuracy: 0.9736483416628805
Test from the Test set
- Precision: 0.8199168093956447
- Recall: 0.8781446540880503
- F1: 0.8480323927622422
- Accuracy: 0.9724346779516247
Download: [HuggingFace Hub](https://huggingface.co/datasets/pythainlp/thainer-corpus-v2)
Read more: [Thai NER v2.0](https://pythainlp.github.io/Thai-NER/version/2)
## Inference
Huggingface doesn't support inference token classification for Thai and It will give wrong tag. You must using this code.
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification
from pythainlp.tokenize import word_tokenize # pip install pythainlp
import torch
name="pythainlp/thainer-corpus-v2-base-model"
tokenizer = AutoTokenizer.from_pretrained(name)
model = AutoModelForTokenClassification.from_pretrained(name)
sentence="เธเธฑเธเธเธทเนเธญ เธเธฒเธเธชเธฒเธงเธกเธฐเธฅเธดเธงเธฒ เธเธธเธเธชเธฃเธฐเธเธต เธญเธฒเธจเธฑเธขเธญเธขเธนเนเธเธตเนเธญเธณเนเธ เธญเธเธฒเธเธฃเธญเธ เธเธฑเธเธซเธงเธฑเธเธเธธเธฃเธตเธฃเธฑเธกเธขเน เธญเธฒเธขเธธ 23 เธเธต เนเธเธดเนเธเนเธฃเธตเธขเธเธเธเธเธฒเธ เธกเธซเธฒเธงเธดเธเธขเธฒเธฅเธฑเธขเธเธญเธเนเธเนเธ เนเธฅเธฐเธเธตเนเธเธทเธญเธเนเธญเธกเธนเธฅเธเธฅเธญเธกเธเธทเนเธญเธเธเนเธกเนเธกเธตเธญเธขเธนเนเธเธฃเธดเธ เธญเธฒเธขเธธ 23 เธเธต"
cut=word_tokenize(sentence.replace(" ", "<_>"))
inputs=tokenizer(cut,is_split_into_words=True,return_tensors="pt")
ids = inputs["input_ids"]
mask = inputs["attention_mask"]
# forward pass
outputs = model(ids, attention_mask=mask)
logits = outputs[0]
predictions = torch.argmax(logits, dim=2)
predicted_token_class = [model.config.id2label[t.item()] for t in predictions[0]]
def fix_span_error(words,ner):
_ner = []
_ner=ner
_new_tag=[]
for i,j in zip(words,_ner):
#print(i,j)
i=tokenizer.decode(i)
if i.isspace() and j.startswith("B-"):
j="O"
if i=='' or i=='<s>' or i=='</s>':
continue
if i=="<_>":
i=" "
_new_tag.append((i,j))
return _new_tag
ner_tag=fix_span_error(inputs['input_ids'][0],predicted_token_class)
print(ner_tag)
```
output:
```python
[('เธเธฑเธ', 'O'),
('เธเธทเนเธญ', 'O'),
(' ', 'O'),
('เธเธฒเธเธชเธฒเธง', 'B-PERSON'),
('เธกเธฐเธฅเธด', 'I-PERSON'),
('เธงเธฒ', 'I-PERSON'),
(' ', 'I-PERSON'),
('เธเธธเธ', 'I-PERSON'),
('เธชเธฃเธฐ', 'I-PERSON'),
('เธเธต', 'I-PERSON'),
(' ', 'O'),
('เธญเธฒเธจเธฑเธข', 'O'),
('เธญเธขเธนเน', 'O'),
('เธเธตเน', 'O'),
('เธญเนเธฒเนเธ เธญ', 'B-LOCATION'),
('เธเธฒเธ', 'I-LOCATION'),
('เธฃเธญเธ', 'I-LOCATION'),
(' ', 'O'),
('เธเธฑเธเธซเธงเธฑเธ', 'B-LOCATION'),
('เธเธธเธฃเธตเธฃเธฑเธกเธขเน', 'I-LOCATION'),
(' ', 'O'),
('เธญเธฒเธขเธธ', 'O'),
(' ', 'O'),
('23', 'B-AGO'),
(' ', 'I-AGO'),
('เธเธต', 'I-AGO'),
(' ', 'O'),
('เนเธเธดเนเธ', 'O'),
('เนเธฃเธตเธขเธเธเธ', 'O'),
('เธเธฒเธ', 'O'),
(' ', 'O'),
('เธกเธซเธฒเธงเธดเธเธขเธฒเธฅเธฑ', 'B-ORGANIZATION'),
('เธขเธเธญเธเนเธเนเธ', 'I-ORGANIZATION'),
(' ', 'O'),
('เนเธฅเธฐ', 'O'),
('เธเธตเน', 'O'),
('เธเธทเธญ', 'O'),
('เธเนเธญเธกเธนเธฅ', 'O'),
('เธเธฅเธญเธก', 'O'),
('เธเธทเนเธญ', 'O'),
('เธเธ', 'O'),
('เนเธกเน', 'O'),
('เธกเธต', 'O'),
('เธญเธขเธนเน', 'O'),
('เธเธฃเธดเธ', 'O'),
(' ', 'O'),
('เธญเธฒเธขเธธ', 'O'),
(' ', 'O'),
('23', 'B-AGO'),
(' ', 'O'),
('เธเธต', 'I-AGO')]
```
## Cite
> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7761354
or BibTeX
```
@dataset{wannaphong_phatthiyaphaibun_2022_7761354,
author = {Wannaphong Phatthiyaphaibun},
title = {Thai NER 2.0},
month = sep,
year = 2022,
publisher = {Zenodo},
version = {2.0},
doi = {10.5281/zenodo.7761354},
url = {https://doi.org/10.5281/zenodo.7761354}
}
``` |
mradermacher/Midnight-Miqu-70B-v1.5-GGUF | mradermacher | "2024-05-06T06:13:06Z" | 2,858 | 16 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:sophosympatheia/Midnight-Miqu-70B-v1.5",
"license:other",
"endpoints_compatible",
"region:us"
] | null | "2024-03-15T03:51:25Z" | ---
base_model: sophosympatheia/Midnight-Miqu-70B-v1.5
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
static quants of https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-i1-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/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q2_K.gguf) | Q2_K | 25.9 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.IQ3_XS.gguf) | IQ3_XS | 28.6 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.IQ3_S.gguf) | IQ3_S | 30.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q3_K_S.gguf) | Q3_K_S | 30.3 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.IQ3_M.gguf) | IQ3_M | 31.4 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q3_K_M.gguf) | Q3_K_M | 33.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q3_K_L.gguf) | Q3_K_L | 36.6 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.IQ4_XS.gguf) | IQ4_XS | 37.6 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q4_K_S.gguf) | Q4_K_S | 39.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q4_K_M.gguf) | Q4_K_M | 41.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q5_K_S.gguf) | Q5_K_S | 47.9 | |
| [GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q5_K_M.gguf) | Q5_K_M | 49.2 | |
| [PART 1](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q6_K.gguf.part2of2) | Q6_K | 57.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF/resolve/main/Midnight-Miqu-70B-v1.5.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | fast, best quality |
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 -->
|
mradermacher/AYA-8B-GGUF | mradermacher | "2024-06-12T23:14:29Z" | 2,858 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:ArmanAsq/AYA-8B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T22:06:21Z" | ---
base_model: ArmanAsq/AYA-8B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ArmanAsq/AYA-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/AYA-8B-GGUF/resolve/main/AYA-8B.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.IQ3_XS.gguf) | IQ3_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.IQ3_S.gguf) | IQ3_S | 4.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.IQ3_M.gguf) | IQ3_M | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q3_K_M.gguf) | Q3_K_M | 4.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q3_K_L.gguf) | Q3_K_L | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q4_K_M.gguf) | Q4_K_M | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q5_K_M.gguf) | Q5_K_M | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/AYA-8B-GGUF/resolve/main/AYA-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 -->
|
FlagAlpha/Llama2-Chinese-7b-Chat | FlagAlpha | "2024-02-23T11:02:23Z" | 2,857 | 212 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"question-answering",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | question-answering | "2023-07-23T10:12:21Z" | ---
developers: [https://huggingface.co/FlagAlphaAI]
license: apache-2.0
language:
- zh
- en
pipeline_tag: question-answering
library_name: transformers
---
# Llama2ไธญๆ็คพๅบ
---
## Llama2ไธญๆๅพฎ่ฐๅๆฐ
็ฑไบLlama2ๆฌ่บซ็ไธญๆๅฏน้ฝ่พๅผฑ๏ผๆไปฌ้็จไธญๆๆไปค้๏ผๅฏนmeta-llama/Llama-2-7b-chat-hf่ฟ่กLoRAๅพฎ่ฐ๏ผไฝฟๅ
ถๅ
ทๅค่พๅผบ็ไธญๆๅฏน่ฏ่ฝๅใ
๐ฏ **่ฏฅ็ๆฌไธบLoRAไธญๆๅพฎ่ฐๅๆฐFlagAlpha/Llama2-Chinese-7b-Chat-LoRAๅmeta-llama/Llama-2-7b-chat-hfๅๆฐ็ปๅๅ็็ๆฌ๏ผๅฏ็ดๆฅไฝฟ็จ**
---
## ๐ ็คพๅบๅฐๅ๏ผ
Github๏ผ[**Llama-Chinese**](https://github.com/LlamaFamily/Llama-Chinese)
ๅจ็บฟไฝ้ช้พๆฅ๏ผ[**llama.family**](https://llama.family/)
## ๐ฅ ็คพๅบไป็ป
ๆฌข่ฟๆฅๅฐLlama2ไธญๆ็คพๅบ๏ผ
ๆไปฌๆฏไธไธชไธๆณจไบLlama2ๆจกๅๅจไธญๆๆน้ข็ไผๅๅไธๅฑๅปบ่ฎพ็้ซ็บงๆๆฏ็คพๅบใ
**ๅบไบๅคง่งๆจกไธญๆๆฐๆฎ๏ผไป้ข่ฎญ็ปๅผๅงๅฏนLlama2ๆจกๅ่ฟ่กไธญๆ่ฝๅ็ๆ็ปญ่ฟญไปฃๅ็บง**ใ
ๆไปฌ็ญๅฟฑๆฌข่ฟๅฏนๅคงๆจกๅLLMๅ
ๆปก็ญๆ
็ๅผๅ่
ๅ็ ็ฉถ่
ๅ ๅ
ฅๆไปฌ็่กๅใ
## ๐ผ ็คพๅบ่ตๆบ
- Llama2ๅจ็บฟไฝ้ช้พๆฅ[**llama.family**](https://llama.family/)๏ผๅๆถๅ
ๅซMetaๅ็ๅไธญๆๅพฎ่ฐ็ๆฌ๏ผ
- Llama2 Chatๆจกๅ็[ไธญๆ้ฎ็ญ่ฝๅ่ฏๆต](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)๏ผ
- [็คพๅบ้ฃไนฆ็ฅ่ฏๅบ](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)๏ผๆฌข่ฟๅคงๅฎถไธ่ตทๅ
ฑๅปบ๏ผ
|
zhentaoyu/gpt-neox-20b-Q4_0-GGUF | zhentaoyu | "2024-06-27T03:44:51Z" | 2,857 | 0 | null | [
"gguf",
"pytorch",
"causal-lm",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/gpt-neox-20b",
"license:apache-2.0",
"region:us"
] | null | "2024-06-27T03:44:00Z" | ---
base_model: EleutherAI/gpt-neox-20b
datasets:
- EleutherAI/pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- llama-cpp
- gguf-my-repo
---
# zhentaoyu/gpt-neox-20b-Q4_0-GGUF
This model was converted to GGUF format from [`EleutherAI/gpt-neox-20b`](https://huggingface.co/EleutherAI/gpt-neox-20b) 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/EleutherAI/gpt-neox-20b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo zhentaoyu/gpt-neox-20b-Q4_0-GGUF --hf-file gpt-neox-20b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo zhentaoyu/gpt-neox-20b-Q4_0-GGUF --hf-file gpt-neox-20b-q4_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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo zhentaoyu/gpt-neox-20b-Q4_0-GGUF --hf-file gpt-neox-20b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo zhentaoyu/gpt-neox-20b-Q4_0-GGUF --hf-file gpt-neox-20b-q4_0.gguf -c 2048
```
|
Anonymezzh/qwen2 | Anonymezzh | "2024-07-01T10:42:24Z" | 2,857 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | "2024-07-01T07:04:30Z" | Entry not found |
timm/maxvit_tiny_tf_224.in1k | timm | "2023-05-11T00:23:16Z" | 2,856 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2204.01697",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-02T21:57:24Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for maxvit_tiny_tf_224.in1k
An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors.
Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman.
### Model Variants in [maxxvit.py](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/maxxvit.py)
MaxxViT covers a number of related model architectures that share a common structure including:
- CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages.
- MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid).
- CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm).
- MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm).
- MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate.
Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string `rw` are `timm` specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations.
All models with the string `tf` are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 30.9
- GMACs: 5.6
- Activations (M): 35.8
- Image size: 224 x 224
- **Papers:**
- MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('maxvit_tiny_tf_224.in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'maxvit_tiny_tf_224.in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 112, 112])
# torch.Size([1, 64, 56, 56])
# torch.Size([1, 128, 28, 28])
# torch.Size([1, 256, 14, 14])
# torch.Size([1, 512, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'maxvit_tiny_tf_224.in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 512, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
### By Top-1
|model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)|
|------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:|
|[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22|
|[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76|
|[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99|
|[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15|
|[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84|
|[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90|
|[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95|
|[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74|
|[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43|
|[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64|
|[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77|
|[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99|
|[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22|
|[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15|
|[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78|
|[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90|
|[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84|
|[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77|
|[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59|
|[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65|
|[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42|
|[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35|
|[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13|
|[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01|
|[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38|
|[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78|
|[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30|
|[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17|
|[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92|
|[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60|
|[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11|
|[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78|
|[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47|
|[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05|
|[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05|
|[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92|
|[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28|
|[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04|
|[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73|
|[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34|
|[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80|
|[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41|
|[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86|
### By Throughput (samples / sec)
|model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)|
|------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:|
|[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80|
|[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41|
|[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34|
|[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73|
|[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04|
|[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86|
|[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05|
|[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92|
|[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05|
|[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28|
|[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11|
|[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47|
|[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13|
|[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78|
|[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60|
|[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92|
|[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30|
|[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17|
|[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22|
|[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78|
|[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78|
|[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38|
|[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77|
|[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64|
|[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01|
|[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42|
|[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35|
|[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65|
|[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43|
|[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74|
|[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59|
|[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95|
|[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90|
|[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90|
|[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77|
|[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84|
|[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84|
|[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99|
|[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99|
|[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76|
|[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15|
|[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15|
|[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22|
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@article{tu2022maxvit,
title={MaxViT: Multi-Axis Vision Transformer},
author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
journal={ECCV},
year={2022},
}
```
```bibtex
@article{dai2021coatnet,
title={CoAtNet: Marrying Convolution and Attention for All Data Sizes},
author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing},
journal={arXiv preprint arXiv:2106.04803},
year={2021}
}
```
|
allenai/tulu-2-dpo-70b | allenai | "2024-01-31T18:54:37Z" | 2,855 | 147 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"dataset:allenai/tulu-v2-sft-mixture",
"arxiv:2305.18290",
"arxiv:2311.10702",
"base_model:meta-llama/Llama-2-70b-hf",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-12T21:34:51Z" | ---
model-index:
- name: tulu-2-dpo-70b
results: []
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- allenai/tulu-v2-sft-mixture
language:
- en
base_model: meta-llama/Llama-2-70b-hf
license: other
license_name: ai2-impact-license-low-risk
license_link: https://allenai.org/impact-license
---
<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png" alt="TuluV2 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for Tulu V2 DPO 70B
Tulu is a series of language models that are trained to act as helpful assistants.
Tulu V2 DPO 70B is a fine-tuned version of Llama 2 that was trained on a mix of publicly available, synthetic and human datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290).
This model is a strong alternative to Llama 2 70b Chat.
For more details, read the paper: [Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
](https://arxiv.org/abs/2311.10702).
## Model description
- **Model type:** The flagship model of a suite of instruction and RLHF tuned chat models on a mix of publicly available, synthetic and human-created datasets.
- **Language(s) (NLP):** Primarily English
- **License:** [AI2 ImpACT](https://allenai.org/impact-license) Low-risk license.
- **Finetuned from model:** [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)
### Model Sources
- **Repository:** https://github.com/allenai/open-instruct
- **DPO Recipe:** The DPO recipe is from the [Zephyr Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) model
- **Model Family:** Other models and the dataset are found in the [Tulu V2 collection](https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101).
## Performance
| Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
|-------------|-----|----|---------------|--------------|
| **Tulu-v2-7b** ๐ช | **7B** | **SFT** | **6.30** | **73.9** |
| **Tulu-v2-dpo-7b** ๐ช | **7B** | **DPO** | **6.29** | **85.1** |
| **Tulu-v2-13b** ๐ช | **13B** | **SFT** | **6.70** | **78.9** |
| **Tulu-v2-dpo-13b** ๐ช | **13B** | **DPO** | **7.00** | **89.5** |
| **Tulu-v2-70b** ๐ช | **70B** | **SFT** | **7.49** | **86.6** |
| **Tulu-v2-dpo-70b** ๐ช | **70B** | **DPO** | **7.89** | **95.1** |
## Input Format
The model is trained to use the following format (note the newlines):
```
<|user|>
Your message here!
<|assistant|>
```
For best results, format all inputs in this manner. **Make sure to include a newline after `<|assistant|>`, this can affect generation quality quite a bit.**
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [Tulu V2 mix dataset](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.
We then further aligned the model with a [Jax DPO trainer](https://github.com/hamishivi/EasyLM/blob/main/EasyLM/models/llama/llama_train_dpo.py) built on [EasyLM](https://github.com/young-geng/EasyLM) on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4.
<!-- You can find the datasets used for training Tulu V2 [here]()
Here's how you can run the model using the `pipeline()` function from ๐ค Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/tulu-2-dpo-70b", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```-->
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The Tulu models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Llama 2 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
### Training hyperparameters
The following hyperparameters were used during DPO training:
- learning_rate: 5e-07
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
## Citation
If you find Tulu 2 is useful in your work, please cite it with:
```
@misc{ivison2023camels,
title={Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2},
author={Hamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
year={2023},
eprint={2311.10702},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
*Model card adapted from [Zephyr Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/blob/main/README.md)* |
AdaptLLM/medicine-chat | AdaptLLM | "2024-06-25T03:01:27Z" | 2,855 | 39 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"biology",
"medical",
"en",
"dataset:EleutherAI/pile",
"dataset:Open-Orca/OpenOrca",
"dataset:GAIR/lima",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"arxiv:2309.09530",
"license:llama2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-09T01:49:56Z" | ---
language:
- en
license: llama2
tags:
- biology
- medical
datasets:
- EleutherAI/pile
- Open-Orca/OpenOrca
- GAIR/lima
- WizardLM/WizardLM_evol_instruct_V2_196k
metrics:
- accuracy
pipeline_tag: text-generation
model-index:
- name: medicine-chat
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 53.75
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AdaptLLM/medicine-chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 76.11
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AdaptLLM/medicine-chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.98
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AdaptLLM/medicine-chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 43.46
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AdaptLLM/medicine-chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 75.69
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AdaptLLM/medicine-chat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 18.95
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AdaptLLM/medicine-chat
name: Open LLM Leaderboard
---
# Domain Adaptation of Large Language Models
This repo contains the domain-specific chat model developed from **LLaMA-2-Chat-7B**, using the method in our **ICLR 2024** paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
### ๐ค [2024/6/21] We release the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain), effective for both general pre-training from scratch and domain-adaptive continual pre-training!!! ๐ค
**************************** **Updates** ****************************
* 2024/6/22: Released the [benchmarking code](https://github.com/microsoft/LMOps/tree/main/adaptllm).
* 2024/6/21: ๐๐ป Released the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) ๐๐ป
* 2024/1/16: ๐ Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!๐
* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
* 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
* 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B.
## Domain-Specific LLaMA-1
### LLaMA-1-7B
In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
</p>
### LLaMA-1-13B
Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B).
## Domain-Specific LLaMA-2-Chat
Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
For example, to chat with the biomedicine-chat model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-chat")
# Put your input here:
user_input = '''Question: Which of the following is an example of monosomy?
Options:
- 46,XX
- 47,XXX
- 69,XYY
- 45,X
Please provide your choice first and then provide explanations if possible.'''
# Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!)
our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this
prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]"
# # NOTE:
# # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this:
# your_system_prompt = "Please, answer this question faithfully."
# prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
```
## Domain-Specific Tasks
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AdaptLLM__medicine-chat)
| Metric |Value|
|---------------------------------|----:|
|Avg. |52.99|
|AI2 Reasoning Challenge (25-Shot)|53.75|
|HellaSwag (10-Shot) |76.11|
|MMLU (5-Shot) |49.98|
|TruthfulQA (0-shot) |43.46|
|Winogrande (5-shot) |75.69|
|GSM8k (5-shot) |18.95|
## Citation
If you find our work helpful, please cite us:
```bibtex
@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}
``` |
google/ddpm-ema-celebahq-256 | google | "2022-11-08T13:41:29Z" | 2,854 | 5 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | "2022-07-19T10:42:32Z" | ---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
## Inference
**DDPM** models can use *discrete noise schedulers* such as:
- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
See the following code:
```python
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-ema-celebahq-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
# save image
image.save("ddpm_generated_image.png")
```
For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
## Training
If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) # <- TODO(PVP) add link
## Samples
1. 
2. 
3. 
4.  |
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