🟢 Quill 1 Preview

Lightweight FIM autocomplete model for Python, built on Qwen2.5-Coder-0.5B.

Quill is a 0.5B parameter code-completion model designed for Fill-in-the-Middle (FIM) tasks in IDEs and editors. It runs comfortably on consumer hardware and is one of the few publicly available 0.5B models trained specifically for autocomplete.

Part of the Minsore family · minsore.com

✨ Highlights

• 🧠 FIM-native — understands <fim_prefix>, <fim_suffix>, <fim_middle>
• ⚡ Real-time — designed for live autocomplete in editors
• 📦 Tiny — 0.5B params, ~400 MB in Q4_K_M
• 🎯 Purpose-built — trained for autocomplete, not chat
• 🆓 Apache 2.0 — same license as base model

📊 Benchmarks

Evaluated against 0.5B–1B code models. All runs used temperature=0.0, raw FIM mode (--chat-template none).

📌 Key insight: On FIM-specific benchmarks, Quill outperforms competitors by 8–10×. On pure code generation (HumanEval), it matches the base model — training added FIM capability without hurting generation.

🚀 Quick Start

llama.cpp

llama-server -m quill-1-preview.Q4_K_M.gguf \
  --port 8080 \
  -ngl 99 \
  --chat-template none

Requirements: any GPU with ≥2 GB VRAM (full offload), or partial CPU offload as fallback.

FIM request

curl http://localhost:8080/infill \
  -H "Content-Type: application/json" \
  -d '{
    "input_prefix": "def add(a, b):\n    ",
    "input_suffix": "\n\nprint(add(2, 3))",
    "n_predict": 64,
    "temperature": 0.0
  }'

Response:

{
  "content": "return a + b",
  "stop": true
}

Python

import requests

def fim_complete(prefix, suffix, max_tokens=64):
    r = requests.post("http://localhost:8080/infill", json={
        "input_prefix": prefix,
        "input_suffix": suffix,
        "n_predict": max_tokens,
        "temperature": 0.0,
        "stop": ["<fim_end>", "<|endoftext|>"],
    })
    return r.json()["content"]

print(fim_complete(
    "def fib(n):\n    if n <= 1:\n        return n\n    return ",
    "\n\nprint(fib(10))"
))
# → "fib(n - 1) + fib(n - 2)"

⚙️ Recommended Settings

⚠️ Limitations

• Autocomplete only — does not work as a chat assistant
• Occasionally appends tests after the function (inherited from mixed training data)
• Number looping — may repeat large integers on some prompts (use repeat_penalty=1.1)
• 1024 token context — long files are truncated
• Python-only — trained on Python FIM data
• Hallucinations on unfamiliar APIs (EditSim 0.318 on Delulu FIM benchmark)

🧬 Training Details

📁 Files

🗺️ Roadmap

• Quill 2 — fix number looping, raise pattern_hit to 70%+
• Quill 2 — multi-language support (JS/TS/Rust)
• Pepper — general-purpose code assistant (1.5B)
• Symphony — flagship code model (3B)

📜 License

Apache 2.0 — same as the base Qwen2.5-Coder-0.5B model.

🙏 Credits

• Base model: Qwen2.5-Coder-0.5B-Instruct by Alibaba Cloud
• Training framework: Unsloth
• Inference: llama.cpp

📬 Contact

Minsore — Ukrainian AI lab building open language models.

• 🌐 minsore.com
• 🤗 huggingface.co/Minsore
• 💬 Built by @Sollamon

⭐ If Quill is useful, star the repo and share your results.