76 lines
2.1 KiB
Markdown
76 lines
2.1 KiB
Markdown
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---
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
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language:
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- en
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- text-generation
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- lora
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- sft
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- mlx
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- finance
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- itr
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- indian-tax
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library_name: mlx
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---
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# ligaments-dev/Qwen2.5-1.5B-Instruct-itr-finetuned
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Fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for **Indian Income Tax Return (ITR) structured JSON extraction**. The LoRA adapter has been merged into the base model weights (fused model).
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## Model Details
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- **Base model:** Qwen/Qwen2.5-1.5B-Instruct
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- **Fine-tuning method:** LoRA (rank=16, scale=32, dropout=0.05)
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- **Framework:** MLX-LM v0.31.3 (Apple Silicon)
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- **Task:** Extract structured JSON from ITR documents (ITR-1, ITR-2, ITR-3, ITR-4)
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- **Training:** 3 epochs, 1500 iterations, lr=2e-5 (cosine decay), batch size=1 with grad accumulation=4
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- **Developed by:** Ligaments AI
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## Evaluation Results
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Evaluated on 49 held-out ITR examples:
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| Metric | Pass Rate |
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|---|---|
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| JSON Validity | 98.0% |
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| Form Type Match | 98.0% |
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| Numeric Sums Correct | 98.0% |
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| Boolean Y/N Only | 98.0% |
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| Date YYYY-MM-DD Format | 98.0% |
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| State/Country Numeric Codes | 98.0% |
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| No Round Numbers | 81.6% |
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## Usage
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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from mlx_lm.sample_utils import make_sampler
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model, tokenizer = load("ligaments-dev/Qwen2.5-1.5B-Instruct-itr-finetuned")
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sampler = make_sampler(temp=0.1)
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messages = [
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{"role": "system", "content": "You are an ITR JSON extraction assistant..."},
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{"role": "user", "content": "<your ITR document text here>"}
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]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_dict=False
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)
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response = generate(model, tokenizer, prompt=prompt, sampler=sampler, max_tokens=4096, verbose=True)
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```
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## Intended Use
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- Extracting structured financial data from Indian ITR documents for MSME lending workflows
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- Automating credit risk assessment pipelines
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- Not intended for general-purpose tax advice or legal decisions
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