---
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen2.5-Coder-1.5B-Instruct
- lora
- sft
- transformers
- trl
- unsloth
---

# 🛠️ Fine-tuned Tool-Calling LLM (LoRA + Merged) for Geospatial Operations

This repository contains a fine-tuned Large Language Model (LLM) capable of structured tool/function calling, optimized for integration with backend services such as Model Context Protocol (MCP) for geospatial, file analysis, and automation tasks.

Note:This repo only has the lora adapter of the model.You can merge it with the Qwen2.5-Coder-1.5B-Instruct base model.
---

## Model Structure

### `lora_model/`
LoRA (Low-Rank Adaptation) adapters and tokenizer files for efficient fine-tuning and inference.
- `adapter_model.safetensors`, `adapter_config.json` — LoRA adapter weights and config.
- `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, `vocab.json` — Tokenizer files.
- `chat_template.jinja`, `merges.txt`, `added_tokens.json` — (If applicable) chat formatting and tokenization.

### `merged_model/`
The base model merged with LoRA adapters for direct use without extra configuration.
- `model-00001-of-00002.safetensors`, `model-00002-of-00002.safetensors` — Model weights (split).
- `config.json`, `generation_config.json` — Model and generation configs.
- `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, `vocab.json` — Tokenizer files.
- `chat_template.jinja`, `merges.txt`, `added_tokens.json` — (If applicable) chat formatting and tokenization.

---

## How to Use

### Loading the Merged Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("path/to/merged_model")
tokenizer = AutoTokenizer.from_pretrained("path/to/merged_model")

# Example inference
input_text = "Analyze the file C:/data/image.tif"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

```


## Loading with LoRA Adapter (Optional)
If you want to use the LoRA adapter separately:

``` python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("your/base-model")
tokenizer = AutoTokenizer.from_pretrained("path/to/lora_model")
model = PeftModel.from_pretrained(base_model, "path/to/lora_model")
```

# Example inference...
Intended Use
Natural language to tool-calling JSON conversion for backend automation.

Geospatial and image operations via MCP or custom tools.

Easily extensible for new tool schemas.


Example Input/Output
Prompt:
``` python
Crop the image C:/images/sample.tif to bounding box [xmin, ymin, xmax, ymax].
```

Model Output:
``` python
TOOL_NEEDED: crop_image
PARAMS: {"filepath": "C:/images/sample.tif", "minx": ..., "miny": ..., "maxx": ..., "maxy": ...}
```


## Training Details
Fine-tuned for structured function-calling on domain-specific data.

Supports both merged and LoRA-adapter workflows.

For technical backend details, see the project repository.

## Citation

@misc{finetuned_llm_mcp_2025,
  author = {İsmail Emre Candan},
  title = {Fine-tuned Tool-Calling LLM (LoRA + Merged)},
  year = {2025},
  url = {https://github.com/EmreCandan0/fine-tuned-llm-tool-calling}
}