Instructions to use seanpoyner/smolcode-coder-cpp-3b-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use seanpoyner/smolcode-coder-cpp-3b-tools with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="seanpoyner/smolcode-coder-cpp-3b-tools",
	filename="smolcode-coder-cpp-3b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use seanpoyner/smolcode-coder-cpp-3b-tools with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Use Docker

docker model run hf.co/seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

LM Studio
Jan

vLLM

How to use seanpoyner/smolcode-coder-cpp-3b-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "seanpoyner/smolcode-coder-cpp-3b-tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seanpoyner/smolcode-coder-cpp-3b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Ollama
How to use seanpoyner/smolcode-coder-cpp-3b-tools with Ollama:
```
ollama run hf.co/seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M
```

Unsloth Studio

How to use seanpoyner/smolcode-coder-cpp-3b-tools with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for seanpoyner/smolcode-coder-cpp-3b-tools to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for seanpoyner/smolcode-coder-cpp-3b-tools to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for seanpoyner/smolcode-coder-cpp-3b-tools to start chatting

How to use seanpoyner/smolcode-coder-cpp-3b-tools with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use seanpoyner/smolcode-coder-cpp-3b-tools with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use seanpoyner/smolcode-coder-cpp-3b-tools with Docker Model Runner:
```
docker model run hf.co/seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M
```

Lemonade

How to use seanpoyner/smolcode-coder-cpp-3b-tools with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull seanpoyner/smolcode-coder-cpp-3b-tools:Q4_K_M

Run and chat with the model

lemonade run user.smolcode-coder-cpp-3b-tools-Q4_K_M

List all available models

lemonade list

smolcode-coder-cpp-3b-tools / 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).