CnakeCharmer/CnakeAgent-sft-v0.2

CnakeAgent-sft-v0.2

[!NOTE] This is a preview checkpoint intended for testing and integration validation. Planned RL/GRPO releases will be continued from this SFT checkpoint as the initialization base.

CnakeAgent-sft-v0.2 is a supervised fine-tune of openai/gpt-oss-20b for Python -> Cython optimization workflows. It is trained on multi-turn tool-use traces where the model proposes Cython code and receives compile/test/benchmark feedback.

This checkpoint is packaged in an MXFP4-compatible format for efficient serving.

What This Model Is For

• —Translating Python functions to optimized Cython
• —Iterative refinement with evaluator feedback (evaluate_cython)
• —Agent-style optimization loops in MCP or OpenAI-compatible tool-calling runtimes

Recommended Serving (vLLM)

python -m vllm.entrypoints.openai.api_server \
  --model CnakeCharmer/CnakeAgent-sft-v0.2 \
  --served-model-name gpt-oss-20b-cython \
  --host 0.0.0.0 \
  --port 8003 \
  --trust-remote-code

MCP Usage

[!IMPORTANT] This model is designed primarily as a local agent backend for code tools such as Claude Code and Codex.

[!NOTE] The CnakeCharmer tool-execution path uses Bubblewrap (bwrap) for sandboxing. Install it before running MCP agent loops that call evaluate_cython. Linux install: ``bash # Debian / Ubuntu sudo apt-get update && sudo apt-get install -y bubblewrap # Fedora sudo dnf install -y bubblewrap # Arch sudo pacman -S --noconfirm bubblewrap ``

# one-time setup
git clone https://github.com/dleemiller/CnakeCharmer.git
cd CnakeCharmer
uv sync

# terminal 1: model server
bash scripts/start_vllm_server.sh

# terminal 2: MCP
uv run python -m cnake_charmer.mcp_server

Then call run_cython_agent from your MCP client.

Add MCP To Your Client

Claude Code

claude mcp add cnake-charmer -- uv run python -m cnake_charmer.mcp_server

Codex

codex mcp add cnake-charmer -- uv run python -m cnake_charmer.mcp_server

Typical Workflow

1. 1.Profile your Python application to find hotspots (cProfile, py-spy, or benchmark timings).
2. 2.Ask your coding agent (Claude Code or Codex) to isolate one target function or tight loop for optimization.
3. 3.Have the coding agent call run_cython_agent with the isolated python_code, func_name, and short task description.
4. 4.Review the returned compile/test/speedup metrics, then apply the generated Cython code into your project.
5. 5.Re-profile and iterate on the next hotspot.

Direct Inference

import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "CnakeCharmer/CnakeAgent-sft-v0.2"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)

system_prompt_path = hf_hub_download(model_id, "system_prompt.txt")
with open(system_prompt_path) as f:
    system_prompt = f.read().strip()
user_prompt = (
    "python_code: def add(a, b):\n"
    "    return a + b\n\n"
    "func_name: add\n"
    "description: optimize with cython"
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_prompt},
]

inputs = tok.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    out = model.generate(inputs, max_new_tokens=512)

print(tok.decode(out[0], skip_special_tokens=True))

Prompting Notes

• —The model was trained with a consistent instruction scaffold.
• —For best behavior, use server-side default instructions (MCP handles this automatically).
• —The checkpoint includes system_prompt.txt for reproducible agent behavior.

Limitations

• —Optimized for Cython/tool-use tasks, not general chat.
• —Quality depends on evaluator feedback loop quality and test coverage.
• —Can still produce non-compiling code in early iterations.

Training Data

Built from curated tool-use traces in the CnakeCharmer project:

• —parallel Python/Cython reference pairs
• —multi-turn evaluation traces with compile/test/benchmark feedback

Project repo: https://github.com/dleemiller/CnakeCharmer