Instructions to use afrideva/Dimensity-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/Dimensity-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/Dimensity-3B-GGUF",
	filename="dimensity-3b.fp16.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 afrideva/Dimensity-3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/Dimensity-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/Dimensity-3B-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 afrideva/Dimensity-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/Dimensity-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 afrideva/Dimensity-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/Dimensity-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 afrideva/Dimensity-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/Dimensity-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/Dimensity-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/Dimensity-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/Dimensity-3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/Dimensity-3B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/Dimensity-3B-GGUF:Q4_K_M

Ollama
How to use afrideva/Dimensity-3B-GGUF with Ollama:
```
ollama run hf.co/afrideva/Dimensity-3B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/Dimensity-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 afrideva/Dimensity-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 afrideva/Dimensity-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 afrideva/Dimensity-3B-GGUF to start chatting

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

Lemonade

How to use afrideva/Dimensity-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/Dimensity-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dimensity-3B-GGUF-Q4_K_M

List all available models

lemonade list

Dimensity/Dimensity-3B-GGUF

Quantized GGUF model files for Dimensity-3B from Dimensity

Name	Quant method	Size
dimensity-3b.fp16.gguf	fp16	5.59 GB
dimensity-3b.q2_k.gguf	q2_k	1.20 GB
dimensity-3b.q3_k_m.gguf	q3_k_m	1.39 GB
dimensity-3b.q4_k_m.gguf	q4_k_m	1.71 GB
dimensity-3b.q5_k_m.gguf	q5_k_m	1.99 GB
dimensity-3b.q6_k.gguf	q6_k	2.30 GB
dimensity-3b.q8_0.gguf	q8_0	2.97 GB

Original Model Card:

Dimensity-3B

Model Details

Dimensity-3B is a finetuned version of the StableLM framework trained on a variety of conversational data. It contains 3 billion parameters.

Intended Uses

This model is intended for conversational AI applications. It can engage in open-ended dialogue by generating responses to user prompts.

Factors

Training Data

The model was trained on a large dataset of over 100 million conversational exchanges extracted from Reddit comments, customer support logs, and other online dialogues.

Prompt Template

The model was finetuned using the following prompt template:

### Human: {prompt} 

### Assistant:

This prompts the model to take on an assistant role.

Ethical Considerations

As the model was trained on public conversational data, it may generate responses that contain harmful stereotypes or toxic content. The model should be used with caution in sensitive contexts.

Caveats and Recommendations

This model is designed for open-ended conversation. It may sometimes generate plausible-sounding but incorrect information. Outputs should be validated against external sources.

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GGUF

Model size

3B params

Architecture

stablelm

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

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View +1 variant

Model tree for afrideva/Dimensity-3B-GGUF

Base model

Dimensity/Dimensity-3B

Quantized

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