add model card

README.md

@@ -1,5 +1,129 @@
 ---
+library_name: transformers
+tags:
+- medical
+- science
+- biology
+- chemistry
+- not-for-all-audiences
+- quantized
+- 4-bit
+- AWQ
+- text-generation
+- autotrain_compatible
+- endpoints_compatible
+- chatml
 license: apache-2.0
+datasets:
+- Locutusque/hercules-v4.0
+language:
+- en
+model_creator: Locutusque
+model_name: SlimHercules-4.0-Mistral-7B-v0.2
+base_model: alpindale/Mistral-7B-v0.2-hf
+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
 ---
+# Locutusque/SlimHercules-4.0-Mistral-7B-v0.2 AWQ
 
 **UPLOAD IN PROGRESS**
+
+- Model creator: [Locutusque](https://huggingface.co/Locutusque)
+- Original model: [SlimHercules-4.0-Mistral-7B-v0.2](https://huggingface.co/Locutusque/SlimHercules-4.0-Mistral-7B-v0.2)
+
+![image/png](https://tse3.mm.bing.net/th/id/OIG1.vnrl3xpEcypR3McLW63q?pid=ImgGn)
+
+## Model Summary
+
+limHercules-4.0-Mistral-v0.2-7B is a fine-tuned language model derived from Mistralai/Mistral-7B-v0.2. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. The dataset used for fine-tuning, also named hercules-v4.0, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v4.0 with enhanced abilities in:
+
+- Complex Instruction Following: Understanding and accurately executing multi-step instructions, even those involving specialized terminology.
+- Function Calling: Seamlessly interpreting and executing function calls, providing appropriate input and output values.
+- Domain-Specific Knowledge: Engaging in informative and educational conversations about Biology, Chemistry, Physics, Mathematics, Medicine, Computer Science, and more.
+
+This model is different in the sense that the dataset was shrunk and not shuffled, that way every dataset could be incorporated, without performance loss. This, in theory, should have much better performance in comparison to it's predecessors.
+ 
+## 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/SlimHercules-4.0-Mistral-7B-v0.2-AWQ"
+system_message = "You are Hercules, 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
+```