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qwen2.5-coder-1.5b-cellsistant-tool-calling

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metadata 
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
  - unsloth
  - qwen2.5
  - tool-calling
  - function-calling
  - cellsistant
  - jupyter
  - lora
  - finetune
language:
  - en
  - ru
library_name: transformers

Qwen2.5-Coder-1.5B - Cellsistant Tool Calling

Fine-tuned version of Qwen2.5-Coder-1.5B for tool calling in Cellsistant (AI assistant for JupyterLab).

Model Details

  • Base Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
  • Training Framework: Unsloth (2x faster)
  • Training Data: 2,342 examples for tool calling
  • Training Time: 6.57 minutes on Tesla T4
  • Training Loss: 0.39 → 0.00042

Supported Tools

The model supports 19 tools for Jupyter notebook manipulation:

Notebook Tools

  • create_cell - Create new cells
  • update_cell - Update cell content
  • delete_cell - Delete cells
  • get_cell_content - Read cell content
  • get_notebook_content - Get full notebook
  • execute_cell - Execute code cells
  • get_cell_output - Get cell output
  • analyze_image - Analyze plots/images

File Tools

  • read_file - Read files
  • write_file - Write files
  • list_directory - List directories
  • create_notebook - Create notebooks
  • delete_file - Delete files
  • rename_file - Rename/move files

Other Tools

  • run_shell - Execute shell commands
  • find_in_notebook - Search in notebook
  • replace_in_cell - Replace text in cells
  • install_package - Install Python packages
  • list_packages - List installed packages

Usage 

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
    device_map="cuda",
    torch_dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "coconut495/qwen2.5-coder-1.5b-cellsistant-tool-calling")

# Generate tool calls
prompt = '''<|im_start|>system
You are a helpful assistant with access to the following tools:
[{"type": "function", "function": {"name": "create_cell", ...}}]
<|im_end|>
<|im_start|>user
Add a code cell that prints hello<|im_end|>
<|im_start|>assistant
'''

inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=64, use_cache=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Parameter Value
Epochs 1
Batch Size 2
Gradient Accumulation 4
Learning Rate 2e-4
LoRA Rank (r) 16
LoRA Alpha 16
Max Sequence Length 4096
Trainable Parameters 18.5M (1.18%)

Dataset

Training dataset contains 2,603 examples covering all 19 tools with various phrasings and edge cases.

License

Apache 2.0

Acknowledgements