57 lines
2.2 KiB
Markdown
57 lines
2.2 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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tags:
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- code
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- function-calling
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- tool-use
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- small-language-model
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- small-code
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datasets:
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- NousResearch/hermes-function-calling-v1
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language:
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- en
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pipeline_tag: text-generation
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---
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# small-code-coder-1.5b-tools
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A LoRA fine-tune of **Qwen2.5-Coder-1.5B-Instruct** that teaches the model to emit
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**native `<tool_call>` function calls**, so a ≤2B *coder* model can drive an agentic
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coding loop.
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Built for [**smolcode**](https://gitea.poyner.ai/sean/smolcode) — an SLM-optimized
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agentic coding assistant — for the Hugging Face **Build Small** hackathon.
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## Why
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Out of the box, small Qwen-Coder models describe tool calls as plain-text JSON
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instead of emitting the native `<tool_call>` format that runtimes (Ollama,
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llama.cpp) parse — which breaks agentic tool-use loops. This fine-tune closes
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that gap on a tiny (≤2B, Tiny-Titan-class) model.
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## Training
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- **Base:** Qwen/Qwen2.5-Coder-1.5B-Instruct
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- **Method:** bf16 LoRA (r=16, α=32) on attention + MLP projections, **assistant-only
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loss** (loss on tool calls + final answers only).
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- **Data:** NousResearch/hermes-function-calling-v1 (breadth) + synthetic smolcode
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tool-use trajectories (sharpness on the actual 5 tools), all rendered through the
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*same* `apply_chat_template(tools=...)` used at inference — so the training target
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is byte-identical to the served prompt.
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- **Schedule:** 3 epochs, full 2048 sequence length.
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- **Hardware:** trained on Modal (x86/CUDA); served on NVIDIA DGX Spark (GB10).
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## Use
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Standard Qwen2.5 chat template with `tools=`. The model responds with
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`<tool_call>{"name": ..., "arguments": ...}</tool_call>` when a tool is warranted.
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## Status — v2
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v2 fixes the v1 train/inference template mismatch (v1 hit 0.92 teacher-forced token
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accuracy but decoded degenerately because it was trained on a hand-rendered Hermes
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ChatML format, not Qwen's `apply_chat_template` output). v2 trains and serves through
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one shared template and is gated on a *free-generation* tool-call parse-rate eval
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(≥90% on held-out smolcode prompts) before release — see `eval_toolcall.py` in the
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smolcode repo.
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## License
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Apache-2.0 (inherits from the base model).
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