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README.md

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 ---
+base_model: alpindale/Mistral-7B-v0.2-hf
+library_name: transformers
+language:
+- en
 license: apache-2.0
+tags:
+- generated_from_trainer
+- quantized
+- 4-bit
+- AWQ
+- autotrain_compatible
+- endpoints_compatible
+- text-generation-inference
+- chatml
+datasets:
+- cognitivecomputations/dolphin
+- cognitivecomputations/dolphin-coder
+- cognitivecomputations/samantha-data
+- jondurbin/airoboros-2.2.1
+- teknium/openhermes-2.5
+- m-a-p/Code-Feedback
+- m-a-p/CodeFeedback-Filtered-Instruction
+model-index:
+- name: workspace/dolphin-2.8-mistral-7b
+  results: []
+quantized_by: Suparious
+pipeline_tag: text-generation
+model_creator: cognitivecomputations
+model_name: dolphin-2.8-mistral-7b-v02
+model_type: mistral
+inference: false
+prompt_template: '<|im_start|>system
+
+  {system_message}<|im_end|>
+
+  <|im_start|>user
+
+  {prompt}<|im_end|>
+
+  <|im_start|>assistant
+
+  '
 ---
+# cognitivecomputations/dolphin-2.8-mistral-7b-v02 🐬 AWQ
+
+GEMV - 64 GroupSize - optimized for shorter context. For the standard AWQ, see [solidrust/dolphin-2.8-mistral-7b-v02-AWQ](https://huggingface.co/solidrust/dolphin-2.8-mistral-7b-v02-AWQ).
+
+- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
+- Original model: [dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02)
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
+
+## Model Summary
+
+My appreciation for the sponsors of Dolphin 2.8:
+- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
+- [Winston Sou](https://twitter.com/WinsonDabbles) - Along with a generous anonymous sponsor, donated a massive personally owned compute resource!
+- [Abacus AI](https://abacus.ai/) - my employer and partner in many things.
+
+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.
+
+The base model has 32k context, and the full-weights fine-tune was with 16k sequence lengths.
+
+It took 3 days on 10x L40S provided by [Crusoe Cloud](https://crusoe.ai/)
+
+Dolphin-2.8 has a variety of instruction, conversational, and coding skills.
+
+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.
+
+## How to use
+
+### Install the necessary packages
+
+```bash
+pip install --upgrade autoawq autoawq-kernels
+```
+
+### Example Python code
+
+```python
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+
+model_path = "solidrust/dolphin-2.8-mistral-7b-v02-AWQ-gemv-64gs"
+system_message = "You are Dolphin, incarnated as a powerful AI."
+
+# Load model
+model = AutoAWQForCausalLM.from_quantized(model_path,
+                                          fuse_layers=True)
+tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                          trust_remote_code=True)
+streamer = TextStreamer(tokenizer,
+                        skip_prompt=True,
+                        skip_special_tokens=True)
+
+# Convert prompt to tokens
+prompt_template = """\
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant"""
+
+prompt = "You're standing on the surface of the Earth. "\
+        "You walk one mile south, one mile west and one mile north. "\
+        "You end up exactly where you started. Where are you?"
+
+tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
+                  return_tensors='pt').input_ids.cuda()
+
+# Generate output
+generation_output = model.generate(tokens,
+                                  streamer=streamer,
+                                  max_new_tokens=512)
+
+```
+
+### About AWQ
+
+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.
+
+AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
+
+It is supported by:
+
+- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
+- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
+- [HF中国镜像站 Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
+- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
+- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
+
+## Prompt template: ChatML
+
+```plaintext
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```