Instructions to use saidutta69/MiniCPM5-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use saidutta69/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 saidutta69/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/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 saidutta69/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/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 saidutta69/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/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 saidutta69/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/MiniCPM5-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/MiniCPM5-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/MiniCPM5-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/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": "saidutta69/MiniCPM5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/MiniCPM5-2B-GGUF:Q4_K_M

Ollama
How to use saidutta69/MiniCPM5-2B-GGUF with Ollama:
```
ollama run hf.co/saidutta69/MiniCPM5-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use saidutta69/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 saidutta69/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": "saidutta69/MiniCPM5-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use saidutta69/MiniCPM5-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/saidutta69/MiniCPM5-2B-GGUF:Q4_K_M
```

Lemonade

How to use saidutta69/MiniCPM5-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/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 saidutta69/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 saidutta69/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 saidutta69/MiniCPM5-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use saidutta69/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 saidutta69/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 "saidutta69/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 (imatrix-calibrated)

GGUF conversions of openbmb/MiniCPM5-2B with importance-matrix calibration, so the K-quants and i-quants keep more quality at small sizes than uncalibrated equivalents.

Who this is for: anyone who wants a 2B reasoning model with 131k context, EN/ZH bilingual chat, and native tool calling - in a 1.4-2.7 GB file that runs anywhere. Verified live: 100+ tok/s on a Tesla T4 at Q4_K_M, 114 tok/s at IQ4_XS, every quant fits in <3 GB VRAM.

Part of RACER IS OP local-LLM drops - follow for more small fast models.

Runs on your gaming PC

Full GGUF ladder included - pick the quant that fits your card:

Your GPU	Recommended quant	Weights	VRAM (ctx 8192, measured)	Speed (T4)
RTX 3060 / 4070 / 5070 (12 GB)	Q8_0	2.68 GB	2,799 MiB	79.8 tok/s gen
RTX 4060 / 3070 (8 GB)	Q6_K	2.07 GB	2,281 MiB	80.8 tok/s gen
GTX 1660 Super / 2060 / 3050 laptop (6 GB)	Q5_K_M	1.81 GB	2,065 MiB	94.7 tok/s gen
CPU-only / Apple Silicon / phones	Q4_K_M	1.56 GB	1,861 MiB	102.6 tok/s gen
Smallest usable	IQ4_XS	1.42 GB	1,737 MiB	114.5 tok/s gen

Weights only; add ~1 GB for context at 8k. OOM? Drop one quant level. Headroom to spare? Go one up. On any modern laptop iGPU/CPU this is real-time chat.

Files

File	Quant	Size	Calibrated	PPL (card)	vs F16
`MiniCPM5-2B-Q8_0.gguf`	Q8_0	2.68 GB	-	15.18	+0.02
`MiniCPM5-2B-Q6_K.gguf`	Q6_K	2.07 GB	-	15.24	+0.08
`MiniCPM5-2B-Q5_K_M.gguf`	Q5_K_M	1.81 GB	yes	15.39	+0.23
`MiniCPM5-2B-Q4_K_M.gguf`	Q4_K_M	1.56 GB	yes	15.65	+0.49
`MiniCPM5-2B-IQ4_XS.gguf`	IQ4_XS	1.42 GB	yes	15.88	+0.72
`MiniCPM5-2B.imatrix`	-	3 MB	-	-	-

F16 reference PPL: 15.16. PPL measured WikiText-2 test subset, 200KB, ctx 512, llama-perplexity on CPU.

Pick Q4_K_M for the best size/quality trade-off, IQ4_XS for the smallest usable file, Q8_0 for near-lossless.

Independent benchmark (measured 2026-09-20)

All numbers measured 2026-09-20 on Kaggle 2x Tesla T4 (single T4 unless noted), llama.cpp b10709, full offload -ngl 99. Method notes at the bottom.

Speed (Tesla T4, `llama-bench -p 512 -n 128`)

File	Size	Prompt	Generation
`MiniCPM5-2B-Q8_0.gguf`	2.68 GB	3,334 tok/s	79.8 tok/s
`MiniCPM5-2B-Q6_K.gguf`	2.07 GB	2,906 tok/s	80.8 tok/s
`MiniCPM5-2B-Q5_K_M.gguf`	1.81 GB	3,002 tok/s	94.7 tok/s
`MiniCPM5-2B-Q4_K_M.gguf`	1.56 GB	3,182 tok/s	102.6 tok/s
`MiniCPM5-2B-IQ4_XS.gguf`	1.42 GB	3,358 tok/s	114.5 tok/s

Headline: 100+ tok/s on a T4 at Q4_K_M, 114 tok/s at IQ4_XS. Smaller quants decode faster (bandwidth-bound). On any modern laptop iGPU/CPU this is real-time chat.

VRAM (full offload, ctx 8192, measured `nvidia-smi`)

File	VRAM
Q8_0	2,799 MiB
Q6_K	2,281 MiB
Q5_K_M	2,065 MiB
Q4_K_M	1,861 MiB
IQ4_XS	1,737 MiB

Headline: every quant fits in <3 GB - runs on any GPU from the last decade, and comfortably on CPU/RAM-only machines and phones.

Quality retention (independent PPL check, WikiText-2 raw test 1.29 MB, ctx 512)

File	PPL	Delta vs Q8_0	Card's claimed delta vs F16
Q8_0	14.16	-	+0.02
Q6_K	14.18	+0.02	+0.08
Q5_K_M	14.34	+0.17	+0.23
Q4_K_M	14.58	+0.42	+0.49
IQ4_XS	14.88	+0.72	+0.72

Independent run reproduces the card's ranking and magnitudes (IQ4_XS delta matches exactly at +0.72). Q4_K_M keeps 97% of the quality signal at 58% of the Q8_0 size.

It thinks, speaks Chinese, and calls tools (all verified live)

Reasoning model: emits [Start thinking]...[End thinking] traces (RL-trained). Thinking even happens in Chinese for Chinese prompts.
Bilingual: exactly 3 fluent Chinese sentences about photosynthesis on demand.
Native tool calling: with tools= passed, server returns proper OpenAI tool_calls (typed, with IDs, finish_reason: tool_calls) - get_weather{"city":"Paris"} + calculate{"expr":"237*419"} both correct. A 2B model that does tools + 131k context + EN/ZH in 1.5 GB is the download pitch.
Spot checks (Q4_K_M): GSM8K-style arithmetic correct (29), is_prime correct with edge-case guards, exact-format instruction mostly followed.

Quant picker (verified recommendation)

Q4_K_M (1.56 GB): best trade-off. +0.42 PPL, 103 tok/s, 1.9 GB VRAM.
IQ4_XS (1.42 GB): smallest usable. +0.72 PPL, 115 tok/s, quality parity spot-checked (math correct).
Q8_0 (2.68 GB): near-lossless reference.

Quickstart

# llama.cpp - server with web UI + OpenAI API
llama-server -hf saidutta69/MiniCPM5-2B-GGUF:Q4_K_M --reasoning-budget 512

# llama.cpp - CLI
llama-cli -hf saidutta69/MiniCPM5-2B-GGUF:Q4_K_M -n 1024 -p "Explain quantization in one sentence."

# Ollama
ollama run hf.co/saidutta69/MiniCPM5-2B-GGUF:Q4_K_M

# LM Studio / Jan - search "MiniCPM5-2B-GGUF", pick Q4_K_M

Also works with Docker Model Runner: docker model run hf.co/saidutta69/MiniCPM5-2B-GGUF:Q4_K_M

Usage notes

Give it token room: it thinks before answering. Set n_predict/max_tokens >= 1024 or answers get cut mid-thought. Server-side, --reasoning-budget 512 keeps simple tasks snappy.
Chat template has tool support (tokenizer.chat_template includes a tools branch) - works with llama-server/OpenAI-style tool_calls, Ollama, LM Studio.
131k context inherited from base (llama.context_length = 131072 in GGUF metadata). KV is GQA (16Q/2KV, 42 layers) so long ctx stays cheap.
Stock llama.cpp runs everything here (standard quants, no fork needed).

How these were made

Pipeline: convert_hf_to_gguf.py --outtype f16 with latest llama.cpp, then llama-imatrix over 100 chunks of WikiText-2 train (ctx 512), then llama-quantize --imatrix for the calibrated types. The exact matrix used is included as MiniCPM5-2B.imatrix.

Method (transparency footnote)

Throughput: llama-bench -ngl 99 -p 512 -n 128 -r 3, 1x T4 15 GB, CUDA 12.8.
VRAM: llama-server -ngl 99 -c 8192 resident set per nvidia-smi.
PPL: llama-perplexity -c 512 -b 512 on WikiText-2 raw test (parquet->txt, 1.29 MB). Card used a 200 KB subset, so absolutes differ; ordering and deltas match.
Quality: llama-cli/llama-server chat, thinking-mode defaults, single runs (temp defaults) - spot checks, not a benchmark suite.
Raw logs: VM /root/minicpm/logs/ (bench_all.log, ppl.log, m_*.log, vram_*.log).

License

Inherits Apache 2.0 from openbmb/MiniCPM5-2B.

MiniCPM5-2B-heretic - decensored safetensors + GGUF sibling
RACER IS OP - Heretic Models - full collection
Base model: openbmb/MiniCPM5-2B

Made with love by RACER IS OP - follow for more uncensored + local-LLM models

Downloads last month: 432

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for saidutta69/MiniCPM5-2B-GGUF

Base model

openbmb/MiniCPM5-2B

Quantized

(83)

this model

saidutta69
/

MiniCPM5-2B-GGUF

MiniCPM5-2B GGUF (imatrix-calibrated)

Runs on your gaming PC

Files

Independent benchmark (measured 2026-09-20)

Speed (Tesla T4, `llama-bench -p 512 -n 128`)

VRAM (full offload, ctx 8192, measured `nvidia-smi`)

Quality retention (independent PPL check, WikiText-2 raw test 1.29 MB, ctx 512)

It thinks, speaks Chinese, and calls tools (all verified live)

Quant picker (verified recommendation)

Quickstart

Usage notes

How these were made

Method (transparency footnote)

License

Related

Model tree for saidutta69/MiniCPM5-2B-GGUF

MiniCPM5-2B GGUF (imatrix-calibrated)

Runs on your gaming PC

Files

Independent benchmark (measured 2026-09-20)

Speed (Tesla T4, llama-bench -p 512 -n 128)

VRAM (full offload, ctx 8192, measured nvidia-smi)

Quality retention (independent PPL check, WikiText-2 raw test 1.29 MB, ctx 512)

It thinks, speaks Chinese, and calls tools (all verified live)

Quant picker (verified recommendation)

Quickstart

Usage notes

How these were made

Method (transparency footnote)

License

Related

Model tree for saidutta69/MiniCPM5-2B-GGUF

Speed (Tesla T4, `llama-bench -p 512 -n 128`)

VRAM (full offload, ctx 8192, measured `nvidia-smi`)