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--- |
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base_model: Intel/neural-chat-7b-v3-1 |
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inference: false |
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license: apache-2.0 |
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model_creator: Intel |
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model_name: Neural Chat 7B v3-1 |
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model_type: mistral |
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prompt_template: '### System: |
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{system_message} |
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### User: |
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{prompt} |
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### Assistant: |
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' |
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quantized_by: TheBloke |
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--- |
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<!-- markdownlint-disable MD041 --> |
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<!-- header start --> |
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<!-- 200823 --> |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</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> |
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</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> |
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</div> |
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</div> |
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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> |
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> |
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<!-- header end --> |
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# Neural Chat 7B v3-1 - AWQ |
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- Model creator: [Intel](https://huggingface.co./Intel) |
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- Original model: [Neural Chat 7B v3-1](https://huggingface.co./Intel/neural-chat-7b-v3-1) |
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<!-- description start --> |
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## Description |
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This repo contains AWQ model files for [Intel's Neural Chat 7B v3-1](https://huggingface.co./Intel/neural-chat-7b-v3-1). |
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These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). |
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### About AWQ |
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AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. |
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It is supported by: |
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- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ |
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- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only |
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- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) |
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- [Transformers](https://huggingface.co./docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers |
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- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code |
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<!-- description end --> |
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<!-- repositories-available start --> |
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## Repositories available |
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* [AWQ model(s) for GPU inference.](https://huggingface.co./TheBloke/neural-chat-7B-v3-1-AWQ) |
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co./TheBloke/neural-chat-7B-v3-1-GPTQ) |
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co./TheBloke/neural-chat-7B-v3-1-GGUF) |
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* [Intel's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co./Intel/neural-chat-7b-v3-1) |
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<!-- repositories-available end --> |
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<!-- prompt-template start --> |
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## Prompt template: Orca-Hashes |
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``` |
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### System: |
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{system_message} |
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### User: |
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{prompt} |
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### Assistant: |
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``` |
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<!-- prompt-template end --> |
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<!-- README_AWQ.md-provided-files start --> |
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## Provided files, and AWQ parameters |
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I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. |
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Models are released as sharded safetensors files. |
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| Branch | Bits | GS | AWQ Dataset | Seq Len | Size | |
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| ------ | ---- | -- | ----------- | ------- | ---- | |
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| [main](https://huggingface.co./TheBloke/neural-chat-7B-v3-1-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co./datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 4.15 GB |
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<!-- README_AWQ.md-provided-files end --> |
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<!-- README_AWQ.md-text-generation-webui start --> |
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## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) |
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Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). |
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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. |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/neural-chat-7B-v3-1-AWQ`. |
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3. Click **Download**. |
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4. The model will start downloading. Once it's finished it will say "Done". |
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5. In the top left, click the refresh icon next to **Model**. |
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6. In the **Model** dropdown, choose the model you just downloaded: `neural-chat-7B-v3-1-AWQ` |
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7. Select **Loader: AutoAWQ**. |
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8. Click Load, and the model will load and is now ready for use. |
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9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. |
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10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! |
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<!-- README_AWQ.md-text-generation-webui end --> |
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<!-- README_AWQ.md-use-from-vllm start --> |
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## Multi-user inference server: vLLM |
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Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). |
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- Please ensure you are using vLLM version 0.2 or later. |
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- When using vLLM as a server, pass the `--quantization awq` parameter. |
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For example: |
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```shell |
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python3 -m vllm.entrypoints.api_server --model TheBloke/neural-chat-7B-v3-1-AWQ --quantization awq --dtype auto |
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``` |
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- When using vLLM from Python code, again set `quantization=awq`. |
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For example: |
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```python |
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from vllm import LLM, SamplingParams |
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prompts = [ |
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"Tell me about AI", |
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"Write a story about llamas", |
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"What is 291 - 150?", |
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"How much wood would a woodchuck chuck if a woodchuck could chuck wood?", |
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] |
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prompt_template=f'''### System: |
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{system_message} |
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### User: |
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{prompt} |
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### Assistant: |
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''' |
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prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] |
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95) |
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llm = LLM(model="TheBloke/neural-chat-7B-v3-1-AWQ", quantization="awq", dtype="auto") |
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outputs = llm.generate(prompts, sampling_params) |
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# Print the outputs. |
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for output in outputs: |
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prompt = output.prompt |
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generated_text = output.outputs[0].text |
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") |
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``` |
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<!-- README_AWQ.md-use-from-vllm start --> |
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<!-- README_AWQ.md-use-from-tgi start --> |
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## Multi-user inference server: Hugging Face Text Generation Inference (TGI) |
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Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` |
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Example Docker parameters: |
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```shell |
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--model-id TheBloke/neural-chat-7B-v3-1-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 |
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``` |
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Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): |
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```shell |
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pip3 install huggingface-hub |
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``` |
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```python |
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from huggingface_hub import InferenceClient |
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endpoint_url = "https://your-endpoint-url-here" |
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prompt = "Tell me about AI" |
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prompt_template=f'''### System: |
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{system_message} |
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### User: |
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{prompt} |
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### Assistant: |
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''' |
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client = InferenceClient(endpoint_url) |
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response = client.text_generation(prompt, |
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max_new_tokens=128, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.95, |
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top_k=40, |
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repetition_penalty=1.1) |
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print(f"Model output: ", response) |
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``` |
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<!-- README_AWQ.md-use-from-tgi end --> |
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<!-- README_AWQ.md-use-from-python start --> |
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## Inference from Python code using Transformers |
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### Install the necessary packages |
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- Requires: [Transformers](https://huggingface.co./docs/transformers) 4.35.0 or later. |
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- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. |
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```shell |
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pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" |
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``` |
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Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. |
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If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: |
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```shell |
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pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl |
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``` |
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If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: |
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```shell |
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pip3 uninstall -y autoawq |
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git clone https://github.com/casper-hansen/AutoAWQ |
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cd AutoAWQ |
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pip3 install . |
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``` |
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### Transformers example code (requires Transformers 4.35.0 and later) |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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model_name_or_path = "TheBloke/neural-chat-7B-v3-1-AWQ" |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name_or_path, |
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low_cpu_mem_usage=True, |
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device_map="cuda:0" |
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) |
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# Using the text streamer to stream output one token at a time |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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prompt = "Tell me about AI" |
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prompt_template=f'''### System: |
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{system_message} |
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### User: |
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{prompt} |
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### Assistant: |
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''' |
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# Convert prompt to tokens |
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tokens = tokenizer( |
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prompt_template, |
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return_tensors='pt' |
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).input_ids.cuda() |
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generation_params = { |
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"do_sample": True, |
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"temperature": 0.7, |
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"top_p": 0.95, |
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"top_k": 40, |
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"max_new_tokens": 512, |
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"repetition_penalty": 1.1 |
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} |
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# Generate streamed output, visible one token at a time |
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generation_output = model.generate( |
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tokens, |
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streamer=streamer, |
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**generation_params |
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) |
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# Generation without a streamer, which will include the prompt in the output |
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generation_output = model.generate( |
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tokens, |
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**generation_params |
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) |
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# Get the tokens from the output, decode them, print them |
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token_output = generation_output[0] |
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text_output = tokenizer.decode(token_output) |
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print("model.generate output: ", text_output) |
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# Inference is also possible via Transformers' pipeline |
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from transformers import pipeline |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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**generation_params |
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) |
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pipe_output = pipe(prompt_template)[0]['generated_text'] |
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print("pipeline output: ", pipe_output) |
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``` |
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<!-- README_AWQ.md-use-from-python end --> |
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<!-- README_AWQ.md-compatibility start --> |
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## Compatibility |
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The files provided are tested to work with: |
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- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. |
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- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. |
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- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. |
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- [Transformers](https://huggingface.co./docs/transformers) version 4.35.0 and later. |
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- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. |
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<!-- README_AWQ.md-compatibility end --> |
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<!-- footer start --> |
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<!-- 200823 --> |
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## Discord |
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For further support, and discussions on these models and AI in general, join us at: |
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[TheBloke AI's Discord server](https://discord.gg/theblokeai) |
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|
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## Thanks, and how to contribute |
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Thanks to the [chirper.ai](https://chirper.ai) team! |
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Thanks to Clay from [gpus.llm-utils.org](llm-utils)! |
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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. |
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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. |
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. |
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* Patreon: https://patreon.com/TheBlokeAI |
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* Ko-Fi: https://ko-fi.com/TheBlokeAI |
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**Special thanks to**: Aemon Algiz. |
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**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, 阿明, 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 Bjäreholt, John Detwiler, Leonard Tan, Iucharbius |
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Thank you to all my generous patrons and donaters! |
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And thank you again to a16z for their generous grant. |
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<!-- footer end --> |
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# Original model card: Intel's Neural Chat 7B v3-1 |
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## Fine-tuning on [Habana](https://habana.ai/) Gaudi2 |
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This model is a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co./mistralai/Mistral-7B-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co./datasets/Open-Orca/SlimOrca). Then we align it with DPO algorithm. For more details, you can refer our blog: [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Habana Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3). |
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## Model date |
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Neural-chat-7b-v3-1 was trained between September and October, 2023. |
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## Evaluation |
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We submit our model to [open_llm_leaderboard](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard), and the model performance has been **improved significantly** as we see from the average metric of 7 tasks from the leaderboard. |
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| Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | Winogrande (5-s) | GSM8K (5-s) | DROP (3-s) | |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | |
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|[mistralai/Mistral-7B-v0.1](https://huggingface.co./mistralai/Mistral-7B-v0.1) | 50.32 | 59.58 | 83.31 | 64.16 | 42.15 | 78.37 | 18.12 | 6.14 | |
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| [Intel/neural-chat-7b-v3](https://huggingface.co./Intel/neural-chat-7b-v3) | **57.31** | 67.15 | 83.29 | 62.26 | 58.77 | 78.06 | 1.21 | 50.43 | |
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| [Intel/neural-chat-7b-v3-1](https://huggingface.co./Intel/neural-chat-7b-v3-1) | **59.06** | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 | |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-04 |
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- train_batch_size: 1 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-HPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 2.0 |
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## Prompt Template |
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``` |
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### System: |
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{system} |
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### User: |
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{usr} |
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### Assistant: |
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``` |
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## Inference with transformers |
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```shell |
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import transformers |
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model = transformers.AutoModelForCausalLM.from_pretrained( |
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'Intel/neural-chat-7b-v3-1' |
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) |
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``` |
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## Ethical Considerations and Limitations |
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neural-chat-7b-v3-1 can produce factually incorrect output, and should not be relied on to produce factually accurate information. neural-chat-7b-v3-1 was trained on [Open-Orca/SlimOrca](https://huggingface.co./datasets/Open-Orca/SlimOrca) based on [mistralai/Mistral-7B-v0.1](https://huggingface.co./mistralai/Mistral-7B-v0.1). Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. |
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Therefore, before deploying any applications of neural-chat-7b-v3-1, developers should perform safety testing. |
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## Disclaimer |
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The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. |
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## Organizations developing the model |
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The NeuralChat team with members from Intel/SATG/AIA/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen. |
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## Useful links |
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* Intel Neural Compressor [link](https://github.com/intel/neural-compressor) |
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* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers) |
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|
