README.md

metadata

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

smolcode-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 1.5B coder model can actually drive an agentic write → run → fix → verify loop.

Built for 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> token (id 151657) that runtimes (Ollama, llama.cpp) parse into OpenAI-style tool_calls — which breaks agentic loops. This fine-tune closes that gap on a tiny (1.5B) model: 100% native <tool_call> emission in free generation on held-out prompts (base model: 0%).

Results

• Native tool-call rate: 100% (16/16 held-out prompts) — the release gate.
• Agentic bench (smolcode pass@1, 10 tasks): 9/10 as the entry tier of a 1.5B→8B→30B ladder, solving 7/10 entirely on its own (2–16s each). For comparison the all-Granite ladder (3B entry) scores 10/10 — the 1.5B carries the same standalone load as a 2×-larger 3B.
• Train loss: 0.138 (3 epochs, assistant-only loss).

Training

• Base: Qwen/Qwen2.5-Coder-1.5B-Instruct
• Method: bf16 LoRA (r=16, α=32) on attention + MLP projections, plus full training of embed_tokens + lm_head (modules_to_save) — required so the model can output the <tool_call> special token, which LoRA on attention/MLP alone cannot. Assistant-only loss (loss on tool calls + final answers only).
• Data: NousResearch/hermes-function-calling-v1 (breadth) + synthetic smolcode tool-use trajectories (sharpness), all rendered through the same apply_chat_template(tools=...) used at inference — training target is byte-identical to the served prompt (fixes the v1 train/inference template mismatch).
• Schedule: 3 epochs, full 2048 sequence length. Trained on Modal (A100).

Serving — read this, two non-obvious requirements

1. Serve via the GGUF, not the safetensors directly. Ollama's bf16-safetensors auto-import produces garbage (??????) for this model. Use the included smolcode-1.5b-q4_k_m.gguf (converted with llama.cpp convert_hf_to_gguf.py):

   ollama create smolcode-coder-1.5b:tools -f Modelfile   # Modelfile is in this repo
   

2. repeat_penalty / repetition_penalty MUST be 1.0. The tool system prompt literally contains the <tool_call> token, so any penalty > 1 suppresses the model from emitting it (you'll see a stray token + bare JSON instead). The included Modelfile sets PARAMETER repeat_penalty 1.0. For raw transformers.generate, pass repetition_penalty=1.0.

With those, Ollama's /v1/chat/completions returns proper native tool_calls.

Use (transformers)

Standard Qwen2.5 chat template with tools=; greedy, repetition_penalty=1.0. The model responds with <tool_call>{"name": ..., "arguments": ...}</tool_call>.

Files

• model.safetensors + tokenizer/config — the merged model (lm_head untied).
• smolcode-1.5b-q4_k_m.gguf — quantized GGUF for serving.
• Modelfile — Ollama import recipe (template + repeat_penalty 1.0).

License

Apache-2.0 (inherits from the base model).