Update model card

README.md

@@ -16,10 +16,16 @@ tags:
 - region:us
 license: apache-2.0
 base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo
+language:
+  - en
 datasets:
+  - ehartford/dolphin
+  - jondurbin/airoboros-2.2.1
+  - ehartford/dolphin-coder
+  - teknium/openhermes
+  - ise-uiuc/Magicoder-OSS-Instruct-75K
   - ise-uiuc/Magicoder-Evol-Instruct-110K
   - LDJnr/Capybara
-  - teknium/openhermes
 library_name: transformers
 model_creator: cognitivecomputations
 model_name: Dolphin Mistral 7B v2.6 - AWQ
@@ -39,3 +45,103 @@ prompt_template: '<|im_start|>system
   '
 quantized_by: Suparious
 ---
+# Dolphin Mistral 7B v2.6 DPO laser - AWQ
+
+- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
+- Original model: [WestLake 7B v2](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
+
+This model's training was sponsored by [convai](https://www.convai.com/).
+
+This model is based on Mistral-7b
+
+The base model has 16k context
+
+This is a special release of Dolphin-DPO based on the LASER [paper](https://arxiv.org/pdf/2312.13558.pdf) and implementation by @fernandofernandes assisted by @ehartford
+
+```plaintext
+@article{sharma2023truth,
+title={The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction},
+author={Sharma, Pratyusha and Ash, Jordan T and Misra, Dipendra},
+journal={arXiv preprint arXiv:2312.13558},
+year={2023} }
+```
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png)
+
+## Model description
+
+This repo contains AWQ model files for [cognitivecomputations's Dolphin Mistral 7B v2.6](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser).
+
+These files were quantised using hardware kindly provided by [SolidRusT Networks](https://solidrust.net/).
+
+## 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/samantha-1.1-westlake-7b-laser-AWQ"
+system_message = "You are Dolphin, a helpful AI assistant."
+
+# 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.
+- [Hugging Face 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
+```