minicpm5-2b-distilled-reasoning

MiniCPM5-2B Distilled Reasoning

A high-performance 2B parameter reasoning model distilled from three frontier models (Qwen3.8-Max, GLM-5.2, Kimi K3) onto the efficient MiniCPM5-2B architecture. Trained on quality-filtered reasoning traces covering math, code, logic puzzles, and structured problem-solving.

Quick Start

Transformers / Unsloth

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained( "jigs97022/minicpm5-2b-distilled-reasoning", max_seq_length=4096, load_in_4bit=True, )

messages = [{"role": "user", "content": "If 3x + 7 = 22, what is x? Show step-by-step reasoning."}] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Ollama (Local CPU/GPU)

ollama run hf.co/jigs97022/minicpm5-2b-distilled-reasoning-gguf

vLLM (API Server)

vllm serve jigs97022/minicpm5-2b-distilled-reasoning
--dtype half
--max-model-len 4096
--gpu-memory-utilization 0.8

Key Features

• Multi-Teacher Distillation: Combines reasoning patterns from Qwen3.8-Max, GLM-5.2, and Kimi K3
• Structured Reasoning: Explicitly trained to use <think> tags for chain-of-thought
• Efficient Inference: 2B parameters with 4-bit quantization runs at ~1.5GB VRAM
• Native MiniCPM5 Template: Preserves original chat formatting for zero-shot compatibility
• Broad Domain Coverage: Math, code generation, logical puzzles, tool-use reasoning

Training Details

Limitations

• Trained on 10K samples; may underperform full 52K variant on edge cases
• Reasoning quality degrades beyond 4K context window
• Inherits base MiniCPM5 knowledge cutoff (~2024)
• May produce verbose <think> sections when concise answers are preferred

License

Apache-2.0 (inherited from base MiniCPM5-2B). Verify distillation dataset license terms before commercial use.

Acknowledgments

• OpenBMB for MiniCPM5-2B
• r0b0tlab for the multi-teacher distillation dataset
• Unsloth for accelerated training infrastructure