Update README.md

README.md

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-license: unknown
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+---
+license: unknown
+---
+# Mistral-7B BitNet LoRA — 4-Bit Merged
+
+This repository contains a 4-bit quantized and LoRA-merged version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), fine-tuned on a small Q&A dataset related to 1-bit LLMs and BitNet B1.58.
+
+The LoRA adapter was merged with the base model for easier deployment on constrained hardware or Hugging Face Spaces.
+
+---
+
+## Model Details
+
+- Base model: `mistralai/Mistral-7B-Instruct-v0.2`
+- Quantization: 4-bit (NF4 via bitsandbytes)
+- Fine-tuning: LoRA (merged into base)
+- Adapter repo: [ogflash/mistral-lora-qa-1bit](https://huggingface.co/ogflash/mistral-lora-qa-1bit)
+
+---
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+model_id = "ogflash/mistral-merged-1bit-4bit"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=bnb_config)
+
+inputs = tokenizer("### Instruction:\nWhat is BitNet B1.58?\n\n### Response:\n", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=300)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))