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README.md

@@ -37,7 +37,91 @@ base_model: alpindale/Mistral-7B-v0.2
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 # macadeliccc/Mistral-7B-v0.2-OpenHermes AWQ
 
-**UPLOAD IN PROGRESS**
-
 - Model creator: [macadeliccc](https://huggingface.co/macadeliccc)
-- Original model: [Mistral-7B-v0.2-OpenHermes](https://huggingface.co/macadeliccc/Mistral-7B-v0.2-OpenHermes)
+- Original model: [Mistral-7B-v0.2-OpenHermes](https://huggingface.co/macadeliccc/Mistral-7B-v0.2-OpenHermes)
+
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/AbagOgU056oIB7S31XESC.webp)
+
+## Model Summary
+
+SFT Training Params:
++ Learning Rate: 2e-4
++ Batch Size: 8
++ Gradient Accumulation steps: 4
++ Dataset: teknium/OpenHermes-2.5 (200k split contains a slight bias towards rp and theory of life)
++ r: 16
++ Lora Alpha: 16
+
+Training Time: 13 hours on A100
+
+## 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/Mistral-7B-v0.2-OpenHermes-AWQ"
+system_message = "You are Hermes, 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.
+- [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
+```