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Model: seanpoyner/smolcode-coder-py-3b-tools
Source: Original Platform

README.md

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+---
+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).