adding model card

README.md

@@ -1,3 +1,143 @@
 ---
 license: apache-2.0
+tags:
+- axolotl
+- finetune
+- qlora
+- quantized
+- 4-bit
+- AWQ
+- pytorch
+- mistral
+- instruct
+- text-generation
+- conversational
+- license:apache-2.0
+- autotrain_compatible
+- endpoints_compatible
+- text-generation-inference
+base_model: openchat/openchat-3.5-0106
+datasets:
+- hendrycks/competition_math
+- allenai/ai2_arc
+- camel-ai/physics
+- camel-ai/chemistry
+- camel-ai/biology
+- camel-ai/math
+- STEM-AI-mtl/Electrical-engineering
+- openbookqa
+- piqa
+- metaeval/reclor
+- mandyyyyii/scibench
+- derek-thomas/ScienceQA
+- sciq
+- TIGER-Lab/ScienceEval
+license: apache-2.0
+language:
+- en
+library_name: transformers
+model_creator: Weyaxi
+model_name: Newton-7B
+model_type: mistral
+pipeline_tag: text-generation
+inference: false
+prompt_template: '<|im_start|>system
+
+  {system_message}<|im_end|>
+
+  <|im_start|>user
+
+  {prompt}<|im_end|>
+
+  <|im_start|>assistant
+
+  '
+quantized_by: Suparious
 ---
+# Weyaxi/Newton-7B AWQ
+
+- Model creator: [Weyaxi](https://huggingface.co/Weyaxi)
+- Original model: [Newton-7B](https://huggingface.co/Weyaxi/Newton-7B)
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/aimTTdmut59aZxOWQlkcC.jpeg)
+
+## Model Summary
+
+This model is a fine-tuned version of [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) on datasets related to science.
+
+This model is fine-tuned using [QLoRa](https://arxiv.org/abs/2305.14314) and [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
+
+This model's training was sponsored by [sablo.ai](https://sablo.ai).
+
+## 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/Newton-7B-AWQ"
+system_message = "You are Newton, 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
+```
+

quant_config.json

@@ -0,0 +1,6 @@
+{
+  "zero_point": true,
+  "q_group_size": 128,
+  "w_bit": 4,
+  "version": "GEMM"
+}