Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+tags:
+  - mistral
+  - fine-tuned
+  - RAG
+  - instruction-tuning
+  - hai-indexer
+  - en
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Hai Indexer 7B
+
+HAI Indexer is a fine-tuned Mistral-7B-Instruct model specialized for RAG (Retrieval Augmented Generation), company knowledge base QA, entity classification, and safety-aware responses.
+
+## Model Details
+
+- **Base model:** [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- **Training:** Supervised fine-tuning (SFT) via LoRA, merged into base
+- **Architecture:** MistralForCausalLM, 7B parameters
+- **Max context:** 32,768 tokens
+- **License:** Apache 2.0
+
+## Training Data
+
+The model was trained on multiple datasets including:
+
+- **RAG / retrieval** – answering from provided context
+- **Business integration** – domain-specific knowledge
+- **Company knowledge base** – internal KB QA
+- **Entity classification** – entity recognition
+- **Anti-hallucination** – staying grounded in context
+- **Safety guardrails** – safe responses
+- **Introduction / operator** – assistant identity and behavior
+
+## Intended Use
+
+- RAG pipelines with retrieved context
+- Company or internal knowledge base Q&A
+- Instruction-following assistant with grounding in provided documents
+- General chat when used with appropriate system prompts
+
+## How to Use
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Haiintel/hai-indexer-7B",
+    torch_dtype="auto",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("Haiintel/hai-indexer-7B")
+
+messages = [{"role": "user", "content": "What is HAI Indexer?"}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+response = tokenizer.decode(
+    outputs[0][inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True,
+)
+print(response)
+```
+
+### RAG-style (with context)
+
+```python
+context = "Your retrieved documents here..."
+query = "Your question here"
+
+messages = [
+    {"role": "system", "content": "Answer based on the context provided."},
+    {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"},
+]
+# Then apply_chat_template + generate as above
+```
+
+## Limitations
+
+- Performance depends on retrieval quality in RAG setups
+- May reflect biases or errors in training data
+- Not designed for medical, legal, or high-stakes decisions without review
+
+## Acknowledgments
+
+- [Mistral AI](https://mistral.ai/) for the base model
+- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for training
+- HAI Intel for fine-tuning and deployment