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--- |
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language: |
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- en |
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license: llama2 |
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tags: |
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- text generation |
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- instruct |
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datasets: |
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- PygmalionAI/PIPPA |
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- Open-Orca/OpenOrca |
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- Norquinal/claude_multiround_chat_30k |
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- jondurbin/airoboros-gpt4-1.4.1 |
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- databricks/databricks-dolly-15k |
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model_name: Pygmalion 2 7B |
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base_model: PygmalionAI/pygmalion-2-7b |
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inference: false |
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model_creator: PygmalionAI |
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model_type: llama |
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pipeline_tag: text-generation |
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prompt_template: 'The model has been trained on prompts using three different roles, |
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which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. |
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The `<|system|>` prompt can be used to inject out-of-channel information behind |
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the scenes, while the `<|user|>` prompt should be used to indicate user input. |
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The `<|model|>` token should then be used to indicate that the model should generate |
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a response. These tokens can happen multiple times and be chained up to form a conversation |
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history. |
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The system prompt has been designed to allow the model to "enter" various modes |
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and dictate the reply length. Here''s an example: |
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``` |
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<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows: |
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{{persona}} |
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You shall reply to the user while staying in character, and generate long responses. |
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``` |
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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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# Pygmalion 2 7B - AWQ |
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- Model creator: [PygmalionAI](https://huggingface.co/PygmalionAI) |
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- Original model: [Pygmalion 2 7B](https://huggingface.co/PygmalionAI/pygmalion-2-7b) |
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<!-- description start --> |
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## Description |
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This repo contains AWQ model files for [PygmalionAI's Pygmalion 2 7B](https://huggingface.co/PygmalionAI/pygmalion-2-7b). |
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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/Pygmalion-2-7B-AWQ) |
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Pygmalion-2-7B-GPTQ) |
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF) |
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* [PygmalionAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/PygmalionAI/pygmalion-2-7b) |
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<!-- repositories-available end --> |
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<!-- prompt-template start --> |
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## Prompt template: Custom |
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The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. |
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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. |
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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. |
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The system prompt has been designed to allow the model to "enter" various modes and dictate the reply length. Here's an example: |
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``` |
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<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows: |
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{{persona}} |
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You shall reply to the user while staying in character, and generate long responses. |
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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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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/Pygmalion-2-7B-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.89 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/Pygmalion-2-7B-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/Pygmalion-2-7B-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/Pygmalion-2-7B-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'''<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows: |
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{{persona}} |
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You shall reply to the user while staying in character, and generate long responses. |
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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: PygmalionAI's Pygmalion 2 7B |
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<h1 style="text-align: center">Pygmalion-2 7B</h1> |
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<h2 style="text-align: center">An instruction-tuned Llama-2 biased towards fiction writing and conversation.</h2> |
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## Model Details |
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The long-awaited release of our new models based on Llama-2 is finally here. Pygmalion-2 7B (formerly known as Metharme) is based on |
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[Llama-2 7B](https://huggingface.co/meta-llama/llama-2-7b-hf) released by Meta AI. |
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The Metharme models were an experiment to try and get a model that is usable for conversation, roleplaying and storywriting, |
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but which can be guided using natural language like other instruct models. After much deliberation, we reached the conclusion |
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that the Metharme prompting format is superior (and easier to use) compared to the classic Pygmalion. |
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This model was trained by doing supervised fine-tuning over a mixture of regular instruction data alongside roleplay, fictional stories |
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and conversations with synthetically generated instructions attached. |
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This model is freely available for both commercial and non-commercial use, as per the Llama-2 license. |
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## Prompting |
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The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. |
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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 |
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form a conversation history. |
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### Prompting example |
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The system prompt has been designed to allow the model to "enter" various modes and dictate the reply length. Here's an example: |
|
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``` |
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<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows: |
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{{persona}} |
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You shall reply to the user while staying in character, and generate long responses. |
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``` |
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## Dataset |
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The dataset used to fine-tune this model includes our own [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA), along with several other instruction |
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datasets, and datasets acquired from various RP forums. |
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## Limitations and biases |
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The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. |
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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 |
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are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |
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Outputs might often be factually wrong or misleading. |
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## Acknowledgements |
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We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for this model. |
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[<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) |
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