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README.md

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 ---
+base_model: mistralai/Mistral-7B-v0.1
+tags:
+- Mistral
+- instruct
+- finetune
+- synthetic
+- quantized
+- 4-bit
+- AWQ
+- text-generation
+- autotrain_compatible
+- endpoints_compatible
+- chatml
 license: apache-2.0
+language:
+- en
+library_name: transformers
+model_creator: NousResearch
+model_name: Genstruct-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
 ---
+# NousResearch/Genstruct-7B AWQ
+
+**UPLOAD IN PROGRESS**
+
+- Model creator: [mlabonne](https://huggingface.co/mlabonne)
+- Original model: [Darewin-7B](https://huggingface.co/mlabonne/Darewin-7B)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64137e2150358a805203cbac/ZhntfiUrRzRtB16nQb_1e.png)
+
+## Model Summary
+
+Genstruct 7B is an instruction-generation model, designed to create valid instructions given a raw text corpus. This enables the creation of new, partially synthetic instruction finetuning datasets from any raw-text corpus.
+
+This work was inspired by [Ada-Instruct](https://arxiv.org/abs/2310.04484)
+
+Previous methods largely rely on in-context approaches to generate instructions, while Ada-Instruct trained a custom instruction-generation model.
+
+Inspired by this, we took this approach further by grounding the generations in user-provided context passages.
+
+Further, the model is trained to generate questions involving complex scenarios that require detailed reasoning, allowing for models trained on the generated data to reason step-by-step.
+
+## 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/Genstruct-7B-AWQ"
+system_message = "You are Genstruct, 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
+```