Method
- Teacher data: 500 Qwen3-32B tool-use trajectories from the Qwen3 subset of Toucan-1.5M (multi-turn, real MCP tool calls)
- Training: QLoRA (r=16, alpha=32), 2 epochs, loss on assistant tokens only
- Context: whole trajectories up to 2,048 tokens, no truncation
- Format: Qwen2.5-native
<tool_call> / <tool_response> tags
Files
- Root: 4-bit (bitsandbytes) model weights
adapter/: the QLoRA adapter on its own
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "Rumiii/Qwen2.5-AlphaCoder-14B"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto") # needs bitsandbytes + GPU # pass `tools=[...]` to tok.apply_chat_template for function calling
Limitations
This is a small proof-of-concept run (500 samples). It has not been benchmarked, and it should not be expected to match the capability of the 32B teacher.
License
Apache 2.0, following the base model and dataset.