Updated readme

README.md

@@ -1,3 +1,90 @@
----
-license: mit
----
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+  - qwen2.5
+  - fine-tuned
+  - qlora
+  - query-optimization
+  - enterprise-search
+  - text2text-generation
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-1.5B Query Optimizer
+
+A fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) trained to rewrite loose, conversational user queries into clear, retrieval-focused enterprise document search queries.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
+| **Fine-tuning method** | QLoRA (4-bit NF4 + LoRA) |
+| **LoRA rank** | 16 |
+| **LoRA alpha** | 32 |
+| **Target modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| **Training examples** | 481 (90% of 535 total) |
+| **Eval examples** | 54 (10% of 535 total) |
+| **Training epochs** | 3 |
+| **Effective batch size** | 16 (4 × 4 gradient accumulation) |
+| **Learning rate** | 2e-4 (cosine schedule) |
+| **Max sequence length** | 256 |
+
+## Intended Use
+
+This model is designed for **enterprise AI search pipelines** where raw user queries need to be normalized before being passed to a retrieval system (e.g., vector search, BM25, or hybrid search).
+
+**Input**: A natural, conversational user query  
+**Output**: A concise, retrieval-optimized search query
+
+### Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "abi-commits/qwen-query-optimizer"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+)
+
+SYSTEM_PROMPT = (
+    "You are a query optimization agent. Rewrite user queries into clear, "
+    "retrieval-focused enterprise document search queries. "
+    "Do not add new information. Do not hallucinate."
+)
+
+def optimize_query(user_query: str) -> str:
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user",   "content": user_query},
+    ]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=80,
+            do_sample=False,
+            repetition_penalty=1.1,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+    generated = output_ids[0][inputs["input_ids"].shape[1]:]
+    return tokenizer.decode(generated, skip_special_tokens=True).strip()
+
+# Examples
+print(optimize_query("how do i request time off?"))
+# → "employee leave request procedure and time-off policy"
+
+print(optimize_query("what's the refund policy?"))
+# → "refund policy terms and conditions for customer returns"