WhiskyAKM/MiniCPM5-2B-GGUF

MiniCPM5-2B GGUF

GGUF quantized version of openbmb/MiniCPM5-2B, the second model in the MiniCPM5 series. It is a dense 2B Transformer (LlamaForCausalLM) built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA.

Model Overview

MiniCPM5-2B is designed for local assistants, coding agents, tool-use workflows, and reasoning scenarios where a compact model is preferred. It keeps a small deployment footprint while providing native long-context support (131,072 tokens), and was post-trained with RL + OPD (On-Policy Distillation). It supports a chat template with thinking/reasoning mode (controlled via enable_thinking) and XML-style tool calling.

Model Architecture

Property	Value
Architecture	LlamaForCausalLM (dense)
Total Parameters	2.52B
Non-Embedding Parameters	1.98B
Layers	42
Attention Heads (GQA)	16 Q / 2 KV
Context Length	131,072 (128K)
Original Precision	bfloat16
Supported Languages	en, zh

Available GGUF Files

File	Quantization	Size	Use Case
minicpm5-2b-bf16.gguf	BF16	4.7 GB	Full precision (reference quality)
minicpm5-2b-Q8_0.gguf	Q8_0	2.5 GB	Near-lossless, low overhead
minicpm5-2b-Q6_K.gguf	Q6_K	2.0 GB	Very high quality
minicpm5-2b-Q5_K_M.gguf	Q5_K_M	1.7 GB	High quality, balanced
minicpm5-2b-Q5_K_S.gguf	Q5_K_S	1.7 GB	High quality, slightly smaller
minicpm5-2b-Q4_K_M.gguf	Q4_K_M	1.5 GB	Recommended: good quality/size trade-off
minicpm5-2b-Q4_K_S.gguf	Q4_K_S	1.4 GB	Smaller, slight quality loss
minicpm5-2b-Q4_0.gguf	Q4_0	1.4 GB	Legacy 4-bit, broad compatibility

DSpark Draft Model (Speculative Decoding)

File	Quantization	Size	Use Case
minicpm5-2b-dspark-bf16.gguf	BF16	623 MB	Draft model, full precision
minicpm5-2b-dspark-Q8_0.gguf	Q8_0	334 MB	Draft model, near-lossless
minicpm5-2b-dspark-Q4_K_M.gguf	Q4_K_M	186 MB	Draft model, smallest footprint

These are the MiniCPM5-2B-DSpark draft models, trained for speculative decoding with MiniCPM5-2B. They accelerate generation without changing the target model's outputs.

Usage

llama.cpp CLI

./llama-cli \
  -m minicpm5-2b-Q4_K_M.gguf \
  -p "Explain quantum computing in simple terms." \
  --temp 1.0 --top-p 0.95

llama-server (OpenAI-compatible API)

./llama-server \
  -m minicpm5-2b-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8080

The GGUF also works with Ollama and LM Studio.

Speculative Decoding (DSpark)

Pair the target model with a DSpark draft model to speed up inference. Draft model quality has minimal impact on output, so smaller quants (e.g. Q4_K_M) are usually fine:

./llama-server \
  -m minicpm5-2b-Q4_K_M.gguf \
  -md minicpm5-2b-dspark-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8080

Thinking Mode

The model supports deep-thinking output. You can control it per request via the chat template, e.g. with an OpenAI-compatible API:

"chat_template_kwargs": {"enable_thinking": false}

Tool Calling

MiniCPM5-2B emits XML-style tool calls. Tool definitions are injected into the prompt, and tool results are returned in the observation/tool role. SGLang's built-in minicpm5 parser converts these to OpenAI-compatible tool_calls natively (see upstream model card).

Generation Parameters

Recommended parameters from the original model:

Parameter	Value
Temperature	1.0
Top-P	0.95

Quantization

These GGUF files were created from the BF16 source model using llama-quantize from the llama.cpp project.

Acknowledgements

• Original model: openbmb/MiniCPM5-2B
• Draft model: openbmb/MiniCPM5-2B-DSpark
• Quantization tool: llama.cpp

License

Apache-2.0 License