minsore/Quill-Gen-1

🟢 Quill Gen 1

Lightweight Python code generation model built on Qwen2.5-Coder-0.5B.

Quill Gen 1 is a 0.5B parameter experimental model for generating Python functions from natural-language descriptions. It is the younger sibling of Pepper 1 Preview — same task, one third the size. Trained as an experiment in small-model code generation, it reaches 80% on HumanEval@50 while running comfortably on consumer hardware.

⚠️ This is NOT a FIM model. Quill Gen 1 cannot do fill-in-the-middle completion. For autocomplete use Quill 1 Preview.

Part of the Minsore family · minsore.com

✨ Highlights

• 🧠 Instruction-tuned — writes Python functions from plain descriptions
• 📦 Tiny — 0.5B params, ~400 MB in Q4_K_M
• 🎯 Purpose-built — code generation only, not chat, not FIM
• ⚡ Fast — designed for low-VRAM setups
• 🧪 Experimental — a proof of concept for small-model generation
• 🆓 Apache 2.0 — same license as base model

📊 Benchmarks

Evaluated against 0.5B–1.5B code models. All runs used temperature=0.0, max_tokens=512, --chat-template none.

Benchmark	Quill Gen 1	Pepper 1 Preview	Quill 1 Preview	Qwen2.5-Coder 0.5B
HumanEval@50	80.0%	82.0%	54.0%	52.0%
HumanEval-Infilling (EditSim)	0.161	—	0.423	0.045
HumanEval-Infilling (Exact Match)	0%	—	16%	0%
Delulu FIM (EditSim)	0.048	—	0.318	0.035

📌 Key insight: Quill Gen 1 reaches 80% HumanEval@50 — nearly matching Pepper 1 Preview (82%) at one third the parameter count. On FIM-specific tasks it underperforms significantly, which is expected: this model was trained for generation, not autocomplete.

🚀 Quick Start

llama.cpp

llama-server -m quill-gen-1.Q4_K_M.gguf \
  --port 8080 \
  -ngl 99 \
  -c 768 \
  --chat-template none

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

Request

curl http://localhost:8080/completion \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "### Instruction:\nWrite a Python function that checks if a number is prime.\n\n### Response:\n",
    "n_predict": 256,
    "temperature": 0.0
  }'

Python

import requests

def generate(instruction, max_tokens=256):
    prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"
    r = requests.post("http://localhost:8080/completion", json={
        "prompt": prompt,
        "n_predict": max_tokens,
        "temperature": 0.0,
        "stop": ["### Instruction:", "<|endoftext|>"],
    })
    return r.json()["content"]

print(generate("Write a Python function that reverses a string."))
# → def reverse_string(s):
#       return s[::-1]

⚙️ Recommended Settings

Parameter	Value	Notes
--chat-template	none	Required. Quill Gen 1 is not a chat model
n_predict	128–256	Works best on short completions
temperature	0.0	Deterministic; use 0.2 for variation
repeat_penalty	1.1	Prevents repetition
-c	768	Matches training context length

⚠️ Limitations

• Generation only — does not support fill-in-the-middle, tool calling, or chat
• Python-only — trained exclusively on Python code
• Small context — 768 tokens; long files are truncated
• Weak on FIM — EditSim 0.16, Exact Match 0% (use Quill 1 instead)
• Occasional over-explanation — may include comments when only code is requested
• Number looping — may repeat large integers on some prompts (use repeat_penalty=1.1)

🧬 Training Details

Base model	Qwen2.5-Coder-0.5B-Base
Method	QLoRA (r=16, α=16)
Data	FIM-converted Python instruction data
Epochs	1
Context	768 tokens
Optimizer	paged_adamw_8bit

📁 Files

File	Size	Description
quill-gen-1.Q4_K_M.gguf	~400 MB	Ready to use with llama.cpp (recommended)
model.safetensors	~1 GB	Full-precision merged weights
config.json	—	Model config
tokenizer.json	—	Tokenizer
tokenizer_config.json	—	Tokenizer config
generation_config.json	—	Generation defaults (optional)

🗺️ Roadmap

• Quill 2 — FIM autocomplete, fixed suffix handling, JS/TS/Rust support
• Pepper 2 — improved MBPP and LiveCodeBench
• Symphony — flagship agentic code model (3B MoE)

📜 License

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

🙏 Credits

• Base model: Qwen2.5-Coder-0.5B-Base 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 Gen 1 is useful, star the repo and share your results.