Instructions to use maddes8cht/Dimensity-Dimensity-3B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maddes8cht/Dimensity-Dimensity-3B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="maddes8cht/Dimensity-Dimensity-3B-gguf",
	filename="Dimensity-Dimensity-3B-Q2_K.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 maddes8cht/Dimensity-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 maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf maddes8cht/Dimensity-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 maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf maddes8cht/Dimensity-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 maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf maddes8cht/Dimensity-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 maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M

Use Docker

docker model run hf.co/maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use maddes8cht/Dimensity-Dimensity-3B-gguf with Ollama:
```
ollama run hf.co/maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M
```

Unsloth Studio new

How to use maddes8cht/Dimensity-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 maddes8cht/Dimensity-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 maddes8cht/Dimensity-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 maddes8cht/Dimensity-Dimensity-3B-gguf to start chatting

Docker Model Runner
How to use maddes8cht/Dimensity-Dimensity-3B-gguf with Docker Model Runner:
```
docker model run hf.co/maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M
```

Lemonade

How to use maddes8cht/Dimensity-Dimensity-3B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull maddes8cht/Dimensity-Dimensity-3B-gguf:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information

Dimensity-3B - GGUF

Model creator: Dimensity
Original model: Dimensity-3B

StableLM

This is a Model based on StableLM. Stablelm is a familiy of Language Models by Stability AI.

Note:

Current (as of 2023-11-15) implementations of Llama.cpp only support GPU offloading up to 34 Layers with these StableLM Models. The model will crash immediately if -ngl is larger than 34. The model works fine however without any gpu acceleration.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.

Note:

Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models)

K-quants

K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.

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.

End of original Model File

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