jigs97022

minicpm5-2b-distilled-reasoning

README

License: apache-2.0

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 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

Table with columns: Parameter, Value
Parameter	Value
Base Model	openbmb/MiniCPM5-2B
Dataset	r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation (sft_balanced)
Train Samples	10,000 (subset of 52K balanced SFT corpus)
Method	QLoRA (r=64, alpha=32, all linear layers)
Effective Batch Size	16
Learning Rate	2e-4 (cosine schedule)
Max Context	4,096 tokens
Final Loss	0.59–0.67
Hardware

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 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

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

jigs97022

Model Tree

Base

openbmb/MiniCPM5-2B

Fine-tuned

this model

Input Modalities

Text

Output Modalities