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--- |
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base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02 |
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library_name: transformers |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- quantized |
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- 4-bit |
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- AWQ |
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- autotrain_compatible |
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- endpoints_compatible |
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- text-generation-inference |
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- chatml |
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datasets: |
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- cognitivecomputations/dolphin |
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- cognitivecomputations/dolphin-coder |
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- cognitivecomputations/samantha-data |
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- jondurbin/airoboros-2.2.1 |
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- teknium/openhermes-2.5 |
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- m-a-p/Code-Feedback |
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- m-a-p/CodeFeedback-Filtered-Instruction |
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model-index: |
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- name: workspace/dolphin-2.8-mistral-7b |
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results: [] |
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quantized_by: Suparious |
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pipeline_tag: text-generation |
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model_creator: cognitivecomputations |
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model_name: dolphin-2.8-mistral-7b-v02 |
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model_type: mistral |
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inference: false |
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prompt_template: '<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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' |
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--- |
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# cognitivecomputations/dolphin-2.8-mistral-7b-v02 🐬 AWQ |
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- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations) |
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- Original model: [dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> |
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## Model Summary |
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My appreciation for the sponsors of Dolphin 2.8: |
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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node |
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- [Winston Sou](https://twitter.com/WinsonDabbles) - Along with a generous anonymous sponsor, donated a massive personally owned compute resource! |
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- [Abacus AI](https://abacus.ai/) - my employer and partner in many things. |
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This model is based on [Mistral-7b-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace. Thanks to @alpindale for converting / publishing. |
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The base model has 32k context, and the full-weights fine-tune was with 16k sequence lengths. |
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It took 3 days on 10x L40S provided by [Crusoe Cloud](https://crusoe.ai/) |
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Dolphin-2.8 has a variety of instruction, conversational, and coding skills. |
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This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. |
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## How to use |
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### Install the necessary packages |
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```bash |
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pip install --upgrade autoawq autoawq-kernels |
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``` |
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### Example Python code |
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```python |
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from awq import AutoAWQForCausalLM |
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from transformers import AutoTokenizer, TextStreamer |
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model_path = "solidrust/dolphin-2.8-mistral-7b-v02-AWQ" |
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system_message = "You are Dolphin, incarnated as a powerful AI." |
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# Load model |
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model = AutoAWQForCausalLM.from_quantized(model_path, |
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fuse_layers=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_path, |
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trust_remote_code=True) |
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streamer = TextStreamer(tokenizer, |
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skip_prompt=True, |
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skip_special_tokens=True) |
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# Convert prompt to tokens |
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prompt_template = """\ |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant""" |
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prompt = "You're standing on the surface of the Earth. "\ |
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"You walk one mile south, one mile west and one mile north. "\ |
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"You end up exactly where you started. Where are you?" |
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tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), |
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return_tensors='pt').input_ids.cuda() |
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# Generate output |
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generation_output = model.generate(tokens, |
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streamer=streamer, |
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max_new_tokens=512) |
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``` |
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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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AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. |
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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) - version 0.2.2 or later for support for all model types. |
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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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## Prompt template: ChatML |
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```plaintext |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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``` |
