minsore/pepper-1-preview

🌶️ Pepper 1 Preview

Lightweight instruction-following code assistant for Python, built on Qwen2.5-Coder-1.5B.

Pepper is a 1.5B parameter code assistant designed for writing functions from natural-language descriptions. It runs on consumer hardware and is one of the few publicly available 1.5B models tuned specifically for instruction-following code generation.

Part of the Minsore family · minsore.com

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✨ Highlights

• 🧠 Instruction-tuned — writes functions from plain English descriptions
• 🐍 Python-focused — clean, idiomatic code without fluff
• ⚡ Real-time — designed for interactive use
• 📦 Compact — 1.5B params, ~1 GB in Q4_K_M
• 🎯 Purpose-built — trained for code generation, not chat
• 🆓 Apache 2.0 — same license as base model

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📊 Benchmarks

Evaluated against 1–1.5B code models. All runs used temperature=0.0, max_tokens=512, --chat-template none.

Benchmark	Pepper 1 Preview	Base Qwen 1.5B	Llama-3.2-1B-Code	Yi-Coder-1.5B
HumanEval@50	82.0%	75.0%	64.0%	24.0%
MBPP@50	26.0%	20.0%	16.0%	38.0%
LiveCodeBench@30	20.0%	30.0%	10.0%	26.7%
BigCodeBench@30	23.3%	23.3%	13.3%	30.0%

📌 Key insight: Pepper 1 Preview outperforms the base model on HumanEval by +7 points — the primary benchmark for instruction-following code generation. On MBPP, both models underperform the official numbers because this evaluation uses zero-shot prompting with --chat-template none. Yi-Coder tested without its native chat template — its HumanEval score reflects format mismatch, not model quality.

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🚀 Quick Start

llama.cpp

llama-server -m pepper-1-preview.Q4_K_M.gguf \
  --port 8080 \
  -ngl 99 \
  -c 4096 \
  --chat-template chatml

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

Chat request

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Write a Python function that checks if a number is prime."}
    ],
    "max_tokens": 512,
    "temperature": 0.0
  }'

Python

import requests

def ask(prompt, max_tokens=512):
    r = requests.post("http://localhost:8080/v1/chat/completions", json={
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": max_tokens,
        "temperature": 0.0,
    })
    return r.json()["choices"][0]["message"]["content"]

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

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⚙️ Recommended Settings

Parameter	Value	Notes
--chat-template	chatml	Required. Pepper expects ChatML.
n_predict	256–1024	512 is a good default
temperature	0.0	Deterministic; use 0.2 for variation
repeat_penalty	1.1	Prevents repetition
-c	4096	Longer context available but degrades

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⚠️ Limitations

• Python-only — not trained on other languages
• No FIM support — for fill-in-the-middle tasks, use Quill
• Not an agent — does not support tool calling or multi-step planning
• Weak on algorithmic tasks — LiveCodeBench score below base Qwen
• 4K inference context — long files are truncated
• Occasional over-explanation — may include comments when only code is requested

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🧬 Training Details

Base model	Qwen2.5-Coder-1.5B-Instruct
Method	QLoRA fine-tuning on curated Python code data
Context	4096 tokens (inference)

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📁 Files

File	Size	Description
pepper-1-preview.Q4_K_M.gguf	~1 GB	Ready to use with llama.cpp
model.safetensors	~3 GB	Full precision (transformers)
config.json, tokenizer.json	—	Config for transformers

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🗺️ Roadmap

• Pepper 2 — improve MBPP and LiveCodeBench, add multi-language support
• Quill 2 — extend FIM to JS/TS/Rust
• Symphony — flagship code model (3B)

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📜 License

Apache 2.0 — same as the base Qwen2.5-Coder-1.5B model.

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🙏 Credits

• Base model: Qwen2.5-Coder-1.5B-Instruct by Alibaba Cloud
• Training framework: Unsloth
• Inference: llama.cpp

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📬 Contact

Minsore — Ukrainian AI lab building open language models.

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

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