Instructions to use prithivMLmods/MiniCPM5-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/MiniCPM5-2B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/MiniCPM5-2B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/MiniCPM5-2B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/MiniCPM5-2B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/MiniCPM5-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/MiniCPM5-2B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/MiniCPM5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/MiniCPM5-2B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/MiniCPM5-2B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/MiniCPM5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/MiniCPM5-2B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/MiniCPM5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/MiniCPM5-2B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/MiniCPM5-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/MiniCPM5-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/MiniCPM5-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/MiniCPM5-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/MiniCPM5-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/MiniCPM5-2B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MiniCPM5-2B-GGUF

MiniCPM5-2B is the second model in OpenBMB's MiniCPM5 series, a dense 2.5-billion-parameter (2.52B) causal language model built on the standard LlamaForCausalLM architecture (42 layers, GQA with 16 Q heads / 2 KV heads, 131,072-token context), designed for local assistants, coding agents, tool-use workflows, and on-device deployment. It's trained through a full UltraData Tiered Data Management pipeline — base training, mid-training on curated corpora like Ultra-FineWeb, UltraX, and UltraData-Code/Math, then post-training via 400B tokens of deep-thinking SFT (including a 500K-sample agentic dataset), followed by domain-specialized RL teachers (math, code, agentic, writing) and On-Policy Distillation (OPD) that merges all 16 expert models back into one release checkpoint using full-vocabulary reverse-KL advantage estimation, together delivering an average +10.96 point gain in reasoning/general capabilities and +6.96 in agentic capabilities over the SFT-only checkpoint. Within its comparison set of 2B-class open models, MiniCPM5-2B achieves an average benchmark score of 53.9 — exceeding even several 4B-class models like Qwen3.5-4B (51.1) — with particularly strong results in code reasoning (69.1 on LiveCodeBench v6), math (86.5 on AIME 2025/2026), long context (68.1 on NoLiMa), tool use (97.1 on τ²-Bench Telecom), and coding-agent tasks (46.4 on SWE-bench Verified). It's servable via vLLM, SGLang (recommended for XML-style tool calling with the native minicpm5 parser), Transformers, llama.cpp/GGUF, Ollama, LM Studio, MLX, or across nine AI chip architectures via the FlagOS ecosystem, with a companion MiniCPM5-2B-DSpark draft model available for speculative decoding, and is released under the Apache-2.0 license.

Model Files

File Name	Quant Type	File Size	File Link
MiniCPM5-2B.BF16.gguf	BF16	5.04 GB	Download
MiniCPM5-2B.F16.gguf	F16	5.04 GB	Download
MiniCPM5-2B.F32.gguf	F32	10.1 GB	Download
MiniCPM5-2B.Q3_K_L.gguf	Q3_K_L	1.38 GB	Download
MiniCPM5-2B.Q3_K_M.gguf	Q3_K_M	1.29 GB	Download
MiniCPM5-2B.Q3_K_S.gguf	Q3_K_S	1.19 GB	Download
MiniCPM5-2B.Q4_0.gguf	Q4_0	1.49 GB	Download
MiniCPM5-2B.Q4_K_M.gguf	Q4_K_M	1.56 GB	Download
MiniCPM5-2B.Q4_K_S.gguf	Q4_K_S	1.5 GB	Download
MiniCPM5-2B.Q5_0.gguf	Q5_0	1.77 GB	Download
MiniCPM5-2B.Q5_K_M.gguf	Q5_K_M	1.81 GB	Download
MiniCPM5-2B.Q5_K_S.gguf	Q5_K_S	1.77 GB	Download
MiniCPM5-2B.Q6_K.gguf	Q6_K	2.07 GB	Download
MiniCPM5-2B.Q8_0.gguf	Q8_0	2.68 GB	Download

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

Downloads last month: -

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/MiniCPM5-2B-GGUF

Base model

openbmb/MiniCPM5-2B

Quantized

(29)

this model

Datasets used to train prithivMLmods/MiniCPM5-2B-GGUF

Collection including prithivMLmods/MiniCPM5-2B-GGUF

SEP 2026

Collection

Collection of Collections and Models — September 2026 • 17 items • Updated about 11 hours ago • 1