Update README.md

README.md

@@ -1,22 +1,125 @@
 ---
-base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-- trl
+base_model: unsloth/Qwen2.5-1.5B-Instruct
+library_name: peft
 license: apache-2.0
 language:
-- en
+  - en
+tags:
+  - unsloth
+  - lora
+  - json
+  - extraction
+  - structured-output
+  - qwen2.5
+pipeline_tag: text-generation
 ---
 
-# Uploaded  model
+# json-extract
+
+A fine-tuned **Qwen2.5-1.5B-Instruct** model with LoRA adapters for extracting structured JSON from natural language text.
+
+## What it does
+
+Give it any unstructured text and a target JSON schema — it returns clean, structured JSON output.
+
+**Input:**
+```
+Paid 500 to Ravi for lunch on Jan 5
+```
+
+**Output:**
+```json
+{
+  "amount": 500,
+  "person": "Ravi",
+  "date": "2025-01-05",
+  "note": "lunch"
+}
+```
+
+## How to use
+
+### With Unsloth (recommended)
+
+```python
+from unsloth import FastLanguageModel
+import json
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "suneeldk/json-extract",
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+def extract(text, schema):
+    prompt = f"### Input: {text}\n### Schema: {json.dumps(schema)}\n### Output:"
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.1,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id,
+        use_cache=False,
+    )
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    output_part = result.split("### Output:")[-1].strip()
+    return json.loads(output_part)
+
+schema = {
+    "amount": "number",
+    "person": "string|null",
+    "date": "ISO date|null",
+    "note": "string|null"
+}
+
+result = extract("Paid 500 to Ravi for lunch on Jan 5", schema)
+print(json.dumps(result, indent=2))
+```
+
+### With Transformers + PEFT
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
+model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/json-extract")
+tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/json-extract")
+```
+
+## Training details
+
+| Parameter | Value |
+|---|---|
+| Base model | Qwen2.5-1.5B-Instruct |
+| Method | LoRA (r=16, alpha=16) |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Epochs | 3 |
+| Learning rate | 2e-4 |
+| Batch size | 4 (x4 gradient accumulation) |
+| Scheduler | Cosine |
+| Optimizer | AdamW 8-bit |
+| Precision | 4-bit quantized (QLoRA) |
+| Max sequence length | 2048 |
+
+## Prompt format
+
+```
+### Input: <your text here>
+### Schema: <json schema>
+### Output:
+```
+
+The model will generate a JSON object matching the provided schema.
+
+## Limitations
 
-- **Developed by:** suneeldk
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
+- Optimized for short-to-medium length text inputs
+- Works best with schemas similar to the training data
+- May not handle highly nested or complex JSON structures
+- English language only
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+## License
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+Apache 2.0