adding model card

README.md

@@ -4,7 +4,6 @@ tags:
 - quantized
 - 4-bit
 - AWQ
-- DPO
 - transformers
 - pytorch
 - mistral
@@ -17,11 +16,12 @@ tags:
 - text-generation-inference
 - finetune
 - chatml
+- DPO
 model-index:
   - name: Ignis-7B-DPO-Laser
     results: []
 license: apache-2.0
-base_model: mistralai/Mistral-7B-Instruct-v0.2
+base_model: mistralai/Mistral-7B-instruct-v0.2
 language:
   - en
 quantized_by: Suparious
@@ -41,3 +41,84 @@ prompt_template: '<|im_start|>system
 
   '
 ---
+
+# Ignis 7B DPO AWQ
+
+- Model creator: [NeuralNovel](https://huggingface.co/NeuralNovel)
+- Original model: [Ignis-7B-DPO-Laser](https://huggingface.co/NeuralNovel/Ignis-7B-DPO-Laser)
+
+![image/jpeg](https://i.ibb.co/C8jZ6FW/OIG3.jpg)
+
+- Community Organization: [ConvexAI](https://huggingface.co/ConvexAI)
+
+## 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/Ignis-7B-DPO-Laser-AWQ"
+system_message = "You are Ignis, 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
+```