Instructions to use Abiray/BitCPM4-CANN-0.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/BitCPM4-CANN-0.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/BitCPM4-CANN-0.5B-GGUF",
	filename="BitCPM4-CANN-0.5B-F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Abiray/BitCPM4-CANN-0.5B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/BitCPM4-CANN-0.5B-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 Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/BitCPM4-CANN-0.5B-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 Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Abiray/BitCPM4-CANN-0.5B-GGUF with Ollama:
```
ollama run hf.co/Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Abiray/BitCPM4-CANN-0.5B-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 Abiray/BitCPM4-CANN-0.5B-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 Abiray/BitCPM4-CANN-0.5B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/BitCPM4-CANN-0.5B-GGUF to start chatting

Docker Model Runner
How to use Abiray/BitCPM4-CANN-0.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/BitCPM4-CANN-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/BitCPM4-CANN-0.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.BitCPM4-CANN-0.5B-GGUF-Q4_K_M

List all available models

lemonade list

Abiray/BitCPM4-CANN-0.5B-GGUF

This repository contains quantized GGUF formats of the openbmb/BitCPM4-CANN-0.5B model, heavily optimized for local inference using llama.cpp, text-generation-webui, LM Studio, Ollama, and other compatible backend frameworks.

Model Information

Original Model: openbmb/BitCPM4-CANN-0.5B
Architecture: BitCPM (500 Million Parameters)

Available Files & Hardware Compatibility

The following quantization formats are available. Because this is an ultra-compact 500M parameter model, it can run blazingly fast on almost any modern device, including microcontrollers, older smartphones, and edge computing hardware.

Filename	Quant Type	File Size	Description
BitCPM4-CANN-0.5B-F16.gguf	16-bit	870 MB	The unquantized base model weights in full precision. Maximum possible fidelity.
BitCPM4-CANN-0.5B-Q8_0.gguf	8-bit	463 MB	Near-perfect accuracy retention. Offers a massive size reduction while acting indistinguishably from the F16 version.
BitCPM4-CANN-0.5B-Q6_K.gguf	6-bit	358 MB	Excellent option for low-resource edge devices demanding strong logic retention.
BitCPM4-CANN-0.5B-Q5_K_M.gguf	5-bit	317 MB	Great middle-ground for balancing speed, size, and remaining reasoning capability.
BitCPM4-CANN-0.5B-Q5_K_S.gguf	5-bit	310 MB	Slightly more aggressive 5-bit compression format focused on minimizing footprint.
BitCPM4-CANN-0.5B-Q4_K_M.gguf	4-bit	279 MB	Recommended. The absolute sweet spot for local 4-bit execution, maintaining surprising coherence for its sub-300MB size.
BitCPM4-CANN-0.5B-Q4_K_S.gguf	4-bit	267 MB	Highly optimized for speed. Perfect for deeply embedded systems or background text processing.
BitCPM4-CANN-0.5B-Q3_K_M.gguf	3-bit	235 MB	Ultimate compression limit. Use exclusively under extremely severe hardware memory limitations.

How to Run

Using `llama.cpp` (Command Line)

If you have compiled llama.cpp, you can run the model directly from your terminal. Replace the filename with the specific version you downloaded:

./llama-cli \
  -m BitCPM4-CANN-0.5B-Q4_K_M.gguf \
  -p "Explain the concept of artificial intelligence to a five-year-old." \
  -n 256 \
  -c 2048 \
  --temp 0.7

Downloads last month: 278

GGUF

Model size

0.4B params

Architecture

minicpm

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for Abiray/BitCPM4-CANN-0.5B-GGUF

Base model

openbmb/BitCPM4-CANN-0.5B

Quantized

(4)

this model

Collection including Abiray/BitCPM4-CANN-0.5B-GGUF

BitCPM4-CANN

Collection

4 items • Updated 3 days ago