| --- |
| 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 [**small-code**](https://github.com/seanpoyner/small-code) — 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, via TRL SFT |
| - **Data:** NousResearch/hermes-function-calling-v1 (rendered to Qwen ChatML so the |
| target is native `<tools>`/`<tool_call>`) |
| - **Hardware:** 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 — experimental v1 ⚠️ |
| This first pass (1 epoch, `max_length=1024`, ~3.7k examples) **does not yet |
| reliably emit `<tool_call>`** in free generation: teacher-forced token accuracy |
| was 0.92, but greedy decoding is degenerate and sensitive to the prompt template |
| (it was trained on the Hermes ChatML rendering, not Qwen's `apply_chat_template` |
| output — a train/inference mismatch). Treat as a proof-of-pipeline, not a |
| production tool-caller. |
|
|
| Known fixes for v2: align train and inference templates (use |
| `apply_chat_template(tools=...)` for both), more epochs, full sequence length, |
| and a held-out eval on tool-call emission. |
|
|
| ## License |
| Apache-2.0 (inherits from the base model). |
|
|
