---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- code
- function-calling
- tool-use
- small-language-model
- small-code
datasets:
- NousResearch/hermes-function-calling-v1
language:
- en
pipeline_tag: text-generation
---

# small-code-coder-1.5b-tools

A LoRA fine-tune of **Qwen2.5-Coder-1.5B-Instruct** that teaches the model to emit
**native `<tool_call>` function calls**, so a ≤2B *coder* model can drive an agentic
coding loop.

Built for [**smolcode**](https://gitea.poyner.ai/sean/smolcode) — an SLM-optimized
agentic coding assistant — for the Hugging Face **Build Small** hackathon.

## Why
Out of the box, small Qwen-Coder models describe tool calls as plain-text JSON
instead of emitting the native `<tool_call>` format that runtimes (Ollama,
llama.cpp) parse — which breaks agentic tool-use loops. This fine-tune closes
that gap on a tiny (≤2B, Tiny-Titan-class) model.

## Training
- **Base:** Qwen/Qwen2.5-Coder-1.5B-Instruct
- **Method:** bf16 LoRA (r=16, α=32) on attention + MLP projections, **assistant-only
  loss** (loss on tool calls + final answers only).
- **Data:** NousResearch/hermes-function-calling-v1 (breadth) + synthetic smolcode
  tool-use trajectories (sharpness on the actual 5 tools), all rendered through the
  *same* `apply_chat_template(tools=...)` used at inference — so the training target
  is byte-identical to the served prompt.
- **Schedule:** 3 epochs, full 2048 sequence length.
- **Hardware:** trained on Modal (x86/CUDA); served on NVIDIA DGX Spark (GB10).

## Use
Standard Qwen2.5 chat template with `tools=`. The model responds with
`<tool_call>{"name": ..., "arguments": ...}</tool_call>` when a tool is warranted.

## Status — v2
v2 fixes the v1 train/inference template mismatch (v1 hit 0.92 teacher-forced token
accuracy but decoded degenerately because it was trained on a hand-rendered Hermes
ChatML format, not Qwen's `apply_chat_template` output). v2 trains and serves through
one shared template and is gated on a *free-generation* tool-call parse-rate eval
(≥90% on held-out smolcode prompts) before release — see `eval_toolcall.py` in the
smolcode repo.

## License
Apache-2.0 (inherits from the base model).