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base_model: unsloth/Qwen2.5-1.5B-Instruct
library_name: peft
license: apache-2.0
language:
- en
tags:
- unsloth
- lora
- json
- extraction
- structured-output
- qwen2.5
pipeline_tag: text-generation
---
# 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
- 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
## License
Apache 2.0 |