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--- |
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language: |
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- en |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- garage-bAInd/Open-Platypus |
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model_name: Platypus2 |
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base_model: garage-bAInd/Platypus2-13B |
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inference: false |
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model_creator: garage-bAInd |
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model_type: llama |
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prompt_template: 'Below is an instruction that describes a task. Write a response |
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that appropriately completes the request. |
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### Instruction: |
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{prompt} |
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### Response: |
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' |
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quantized_by: TheBloke |
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--- |
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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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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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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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<div style="display: flex; flex-direction: column; align-items: flex-end;"> |
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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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# Platypus2 - AWQ |
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- Model creator: [garage-bAInd](https://huggingface.co/garage-bAInd) |
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- Original model: [Platypus2](https://huggingface.co/garage-bAInd/Platypus2-13B) |
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<!-- description start --> |
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## Description |
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This repo contains AWQ model files for [garage-bAInd's Platypus2](https://huggingface.co/garage-bAInd/Platypus2-13B). |
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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. |
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It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB. |
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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/Platypus2-13B-AWQ) |
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Platypus2-13B-GPTQ) |
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Platypus2-13B-GGUF) |
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* [garage-bAInd's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/garage-bAInd/Platypus2-13B) |
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<!-- repositories-available end --> |
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<!-- prompt-template start --> |
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## Prompt template: Alpaca |
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``` |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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{prompt} |
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### Response: |
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``` |
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<!-- prompt-template end --> |
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<!-- licensing start --> |
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## Licensing |
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The creator of the source model has listed its license as `cc-by-nc-sa-4.0`, and this quantization has therefore used that same license. |
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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. |
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In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [garage-bAInd's Platypus2](https://huggingface.co/garage-bAInd/Platypus2-13B). |
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<!-- licensing end --> |
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<!-- README_AWQ.md-provided-files start --> |
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## Provided files and AWQ parameters |
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For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM. |
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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/Platypus2-13B-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB |
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<!-- README_AWQ.md-provided-files end --> |
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<!-- README_AWQ.md-use-from-vllm start --> |
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## Serving this model from 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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- When using vLLM as a server, pass the `--quantization awq` parameter, for example: |
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```shell |
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python3 python -m vllm.entrypoints.api_server --model TheBloke/Platypus2-13B-AWQ --quantization awq |
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``` |
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When using vLLM from Python code, pass the `quantization=awq` parameter, 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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"Hello, my name is", |
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"The president of the United States is", |
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"The capital of France is", |
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"The future of AI is", |
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] |
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95) |
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llm = LLM(model="TheBloke/Platypus2-13B-AWQ", quantization="awq") |
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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-python start --> |
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## How to use this AWQ model from Python code |
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### Install the necessary packages |
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Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later |
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```shell |
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pip3 install autoawq |
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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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### You can then try the following example code |
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```python |
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from awq import AutoAWQForCausalLM |
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from transformers import AutoTokenizer |
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model_name_or_path = "TheBloke/Platypus2-13B-AWQ" |
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# Load model |
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model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True, |
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trust_remote_code=False, safetensors=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False) |
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prompt = "Tell me about AI" |
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prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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{prompt} |
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### Response: |
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''' |
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print("\n\n*** Generate:") |
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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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# Generate output |
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generation_output = model.generate( |
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tokens, |
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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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) |
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print("Output: ", tokenizer.decode(generation_output[0])) |
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# Inference can also be done using transformers' pipeline |
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from transformers import pipeline |
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print("*** 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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max_new_tokens=512, |
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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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) |
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print(pipe(prompt_template)[0]['generated_text']) |
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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 [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm). |
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[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781). |
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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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## 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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov |
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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: garage-bAInd's Platypus2 |
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# Platypus2-13B |
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Platypus-13B is an instruction fine-tuned model based on the LLaMA2-13B transformer architecture. |
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### Benchmark Metrics |
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| Metric | Value | |
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|-----------------------|-------| |
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| MMLU (5-shot) | 56.70 | |
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| ARC (25-shot) | 61.26 | |
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| HellaSwag (10-shot) | 82.56 | |
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| TruthfulQA (0-shot) | 44.86 | |
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| Avg. | 61.35 | |
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We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results. |
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### Model Details |
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* **Trained by**: Cole Hunter & Ariel Lee |
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* **Model type:** **Platypus2-13B** is an auto-regressive language model based on the LLaMA2 transformer architecture. |
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* **Language(s)**: English |
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* **License for base weights**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) |
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### Prompt Template |
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``` |
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### Instruction: |
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<prompt> (without the <>) |
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### Response: |
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``` |
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### Training Dataset |
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`garage-bAInd/Platypus2-13B` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). |
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Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information. |
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### Training Procedure |
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`garage-bAInd/Platypus2-13B` was instruction fine-tuned using LoRA on 1 A100 80GB. For training details and inference instructions please see the [Platypus2](https://github.com/arielnlee/Platypus) GitHub repo. |
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### Reproducing Evaluation Results |
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Install LM Evaluation Harness: |
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``` |
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# clone repository |
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git clone https://github.com/EleutherAI/lm-evaluation-harness.git |
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# check out the correct commit |
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git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 |
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# change to repo directory |
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cd lm-evaluation-harness |
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# install |
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pip install -e . |
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``` |
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Each task was evaluated on 1 A100 80GB GPU. |
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ARC: |
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``` |
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/arc_challenge_25shot.json --device cuda --num_fewshot 25 |
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``` |
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HellaSwag: |
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``` |
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/hellaswag_10shot.json --device cuda --num_fewshot 10 |
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``` |
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MMLU: |
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``` |
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/mmlu_5shot.json --device cuda --num_fewshot 5 |
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``` |
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TruthfulQA: |
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``` |
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-13B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus2-13B/truthfulqa_0shot.json --device cuda |
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``` |
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### Limitations and bias |
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Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's 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 variants, developers should perform safety testing and tuning tailored to their specific applications of the model. |
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Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/ |
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### Citations |
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```bibtex |
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@article{platypus2023, |
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title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, |
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author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz}, |
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booktitle={arXiv preprint arxiv:2308.07317}, |
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year={2023} |
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} |
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``` |
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```bibtex |
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@misc{touvron2023llama, |
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title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, |
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author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov year={2023}, |
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eprint={2307.09288}, |
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archivePrefix={arXiv}, |
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} |
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``` |
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```bibtex |
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@inproceedings{ |
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hu2022lora, |
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title={Lo{RA}: Low-Rank Adaptation of Large Language Models}, |
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author={Edward J Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen}, |
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booktitle={International Conference on Learning Representations}, |
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year={2022}, |
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url={https://openreview.net/forum?id=nZeVKeeFYf9} |
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} |
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``` |
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