初始化项目，由ModelHub XC社区提供模型

Model: ligaments-dev/Qwen2.5-1.5B-Instruct-itr-finetuned
Source: Original Platform

README.md

@@ -0,0 +1,75 @@
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- text-generation
+- lora
+- sft
+- mlx
+- finance
+- itr
+- indian-tax
+library_name: mlx
+---
+
+# ligaments-dev/Qwen2.5-1.5B-Instruct-itr-finetuned
+
+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).
+
+## Model Details
+
+- **Base model:** Qwen/Qwen2.5-1.5B-Instruct
+- **Fine-tuning method:** LoRA (rank=16, scale=32, dropout=0.05)
+- **Framework:** MLX-LM v0.31.3 (Apple Silicon)
+- **Task:** Extract structured JSON from ITR documents (ITR-1, ITR-2, ITR-3, ITR-4)
+- **Training:** 3 epochs, 1500 iterations, lr=2e-5 (cosine decay), batch size=1 with grad accumulation=4
+- **Developed by:** Ligaments AI
+
+## Evaluation Results
+
+Evaluated on 49 held-out ITR examples:
+
+| Metric | Pass Rate |
+|---|---|
+| JSON Validity | 98.0% |
+| Form Type Match | 98.0% |
+| Numeric Sums Correct | 98.0% |
+| Boolean Y/N Only | 98.0% |
+| Date YYYY-MM-DD Format | 98.0% |
+| State/Country Numeric Codes | 98.0% |
+| No Round Numbers | 81.6% |
+
+## Usage
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+from mlx_lm.sample_utils import make_sampler
+
+model, tokenizer = load("ligaments-dev/Qwen2.5-1.5B-Instruct-itr-finetuned")
+sampler = make_sampler(temp=0.1)
+
+messages = [
+    {"role": "system", "content": "You are an ITR JSON extraction assistant..."},
+    {"role": "user", "content": "<your ITR document text here>"}
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_dict=False
+)
+
+response = generate(model, tokenizer, prompt=prompt, sampler=sampler, max_tokens=4096, verbose=True)
+```
+
+## Intended Use
+
+- Extracting structured financial data from Indian ITR documents for MSME lending workflows
+- Automating credit risk assessment pipelines
+- Not intended for general-purpose tax advice or legal decisions