Instructions to use iamimmanuelraj/Nanbeige4.2-3B-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 iamimmanuelraj/Nanbeige4.2-3B-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 iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf iamimmanuelraj/Nanbeige4.2-3B-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 iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf iamimmanuelraj/Nanbeige4.2-3B-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 iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf iamimmanuelraj/Nanbeige4.2-3B-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 iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use iamimmanuelraj/Nanbeige4.2-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamimmanuelraj/Nanbeige4.2-3B-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": "iamimmanuelraj/Nanbeige4.2-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M

Ollama
How to use iamimmanuelraj/Nanbeige4.2-3B-GGUF with Ollama:
```
ollama run hf.co/iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use iamimmanuelraj/Nanbeige4.2-3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iamimmanuelraj/Nanbeige4.2-3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iamimmanuelraj/Nanbeige4.2-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for iamimmanuelraj/Nanbeige4.2-3B-GGUF to start chatting

How to use iamimmanuelraj/Nanbeige4.2-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use iamimmanuelraj/Nanbeige4.2-3B-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 iamimmanuelraj/Nanbeige4.2-3B-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 iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use iamimmanuelraj/Nanbeige4.2-3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iamimmanuelraj/Nanbeige4.2-3B-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 "iamimmanuelraj/Nanbeige4.2-3B-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"

Docker Model Runner
How to use iamimmanuelraj/Nanbeige4.2-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M
```

Lemonade

How to use iamimmanuelraj/Nanbeige4.2-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nanbeige4.2-3B-GGUF-Q4_K_M

List all available models

lemonade list

Nanbeige4.2-3B — GGUF

GGUF quantizations of Nanbeige/Nanbeige4.2-3B — a 3B "Looped Transformer" (22 layers, num_loops=2, hidden 3072, untied 166k vocab, RoPE θ 70M).

33 quants from full-precision F16/BF16 down to 1-bit and ternary, including imatrix-guided IQ quants for best quality-per-byte at the low end.

Nanbeige4.2-3B support is merged into mainline llama.cpp — just build the latest:

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build -j

🚀 Run

llama.cpp (chat):

./build/bin/llama-cli -hf iamimmanuelraj/Nanbeige4.2-3B-GGUF:Q4_K_M -cnv

or with a local file:

./build/bin/llama-cli -m Nanbeige4.2-3B-Q4_K_M.gguf -cnv

llama-server (OpenAI-compatible API):

./build/bin/llama-server -m Nanbeige4.2-3B-Q4_K_M.gguf -c 8192

Download a single quant:

pip install -U "huggingface_hub[cli]"
hf download iamimmanuelraj/Nanbeige4.2-3B-GGUF Nanbeige4.2-3B-Q4_K_M.gguf --local-dir .

📦 Quants

⭐ = recommended. IQ quants are imatrix-guided (better quality at the same size).

Full precision

Quant	Size	Notes
F16	8.34 GB	Full 16-bit reference
BF16	8.34 GB	Full 16-bit (bfloat)

High quality

Quant	Size	Notes
⭐ Q8_0	4.43 GB	Near-lossless, best quality
Q6_K	3.42 GB	Excellent, hard to distinguish from F16
⭐ Q5_K_M	2.99 GB	Very high quality, great default for quality
Q5_K_S	2.94 GB	Very high quality, slightly smaller
Q5_0	2.94 GB	Legacy 5-bit
Q5_1	3.17 GB	Legacy 5-bit, larger

Balanced (recommended range)

Quant	Size	Notes
⭐ Q4_K_M	2.57 GB	Best size/quality balance — start here
Q4_K_S	2.50 GB	Balanced, slightly smaller
Q4_0	2.48 GB	Legacy 4-bit
Q4_1	2.71 GB	Legacy 4-bit, larger
⭐ IQ4_XS	2.38 GB	Imatrix 4-bit, beats Q4 at smaller size
IQ4_NL	2.49 GB	Imatrix 4-bit non-linear

Small

Quant	Size	Notes
Q3_K_L	2.31 GB	3-bit, larger/better
Q3_K_M	2.17 GB	3-bit balanced
Q3_K_S	2.00 GB	3-bit smaller
IQ3_M	2.08 GB	Imatrix 3-bit, good
IQ3_S	2.00 GB	Imatrix 3-bit
IQ3_XS	1.93 GB	Imatrix 3-bit smaller
IQ3_XXS	1.78 GB	Imatrix 3-bit, very small

Very small

Quant	Size	Notes
Q2_K	1.76 GB	2-bit, usable
Q2_K_S	1.68 GB	2-bit smaller
Q2_0	1.60 GB	Legacy 2-bit
IQ2_M	1.66 GB	Imatrix 2-bit, best small quality
IQ2_S	1.59 GB	Imatrix 2-bit
IQ2_XS	1.46 GB	Imatrix 2-bit smaller
IQ2_XXS	1.37 GB	Imatrix 2-bit, very small

Extreme / experimental

Quant	Size	Notes
IQ1_M	1.27 GB	Imatrix 1-bit, experimental
IQ1_S	1.21 GB	Imatrix 1-bit, most degraded
Q1_0	0.94 GB	1-bit, smallest, heavy quality loss
TQ1_0	1.37 GB	Ternary, experimental
TQ2_0	1.52 GB	Ternary, experimental

Which should I pick?

Most people → Q4_K_M (2.57 GB): best balance of size, speed, quality.
Want max quality → Q8_0 or Q6_K.
Tight on RAM/VRAM → IQ4_XS (2.38 GB) or Q3_K_M (2.17 GB).
Tiny devices → IQ2_M (1.66 GB); below that quality drops sharply.
≤ 1.3 GB (IQ1/Q1/TQ): experimental — expect noticeable degradation.

Prefer IQ over the same-size legacy quant when available — imatrix calibration gives better quality per byte.

Notes

IQ quants built with an importance matrix (imatrix) for better low-bit fidelity.
Q8_0 KV cache recommended for this architecture.
License follows the base model's license.

Quantized with llama.cpp.

Downloads last month: 1,216

GGUF

Model size

4B params

Architecture

nanbeige

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for iamimmanuelraj/Nanbeige4.2-3B-GGUF

Base model

Nanbeige/Nanbeige4.2-3B-Base

Finetuned

Nanbeige/Nanbeige4.2-3B

Quantized

(39)

this model

Collection including iamimmanuelraj/Nanbeige4.2-3B-GGUF

Nanbeige4.2

Collection

1 item • Updated 3 days ago