Instructions to use PartAI/Dorna-Llama3-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PartAI/Dorna-Llama3-8B-Instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PartAI/Dorna-Llama3-8B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use PartAI/Dorna-Llama3-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PartAI/Dorna-Llama3-8B-Instruct-GGUF",
	filename="dorna-llama3-8b-instruct.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use PartAI/Dorna-Llama3-8B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf PartAI/Dorna-Llama3-8B-Instruct-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 PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf PartAI/Dorna-Llama3-8B-Instruct-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 PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf PartAI/Dorna-Llama3-8B-Instruct-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 PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use PartAI/Dorna-Llama3-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use PartAI/Dorna-Llama3-8B-Instruct-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 PartAI/Dorna-Llama3-8B-Instruct-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 PartAI/Dorna-Llama3-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PartAI/Dorna-Llama3-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use PartAI/Dorna-Llama3-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use PartAI/Dorna-Llama3-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PartAI/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dorna-Llama3-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Model Details

The Dorna models are a family of decoder-only models, specifically trained/fine-tuned on Persian data, developed by Part AI. As an initial release, an 8B instruct model from this family is Dorna-Llama3-8B-Instruct is built using the Meta Llama 3 Instruct model.

In this repo, we provide bf16 model and quantized models in the GGUF formats, including Q2_K, Q3_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_1, Q4_K_M, Q4_K_S, Q5_0, Q5_1, Q5_K_M, Q5_K_S and Q8_0

Here offers an in-depth report that includes several performance charts. Check it out.

Name	Quant Method	Bits	Memory
dorna-llama3-8b-instruct.Q2_K.gguf	Q2_K	2	3.2 GB
dorna-llama3-8b-instruct.Q3_K_L.gguf	Q3_K_L	3	4.3 GB
dorna-llama3-8b-instruct.Q3_K_M.gguf	Q3_K_M	3	4.1 GB
dorna-llama3-8b-instruct.Q3_K_S.gguf	Q3_K_S	3	3.7 GB
dorna-llama3-8b-instruct.Q4_0.gguf	Q4_1	4	4.7 GB
dorna-llama3-8b-instruct.Q4_1.gguf	Q4_1	4	5.2 GB
dorna-llama3-8b-instruct.Q4_K_M.gguf	Q4_K_M	4	4.9 GB
dorna-llama3-8b-instruct.Q4_K_S.gguf	Q4_K_S	4	4.7 GB
dorna-llama3-8b-instruct.Q5_0.gguf	Q5_0	5	5.6 GB
dorna-llama3-8b-instruct.Q5_1.gguf	Q5_1	5	6.1 GB
dorna-llama3-8b-instruct.Q5_K_M.gguf	Q5_K_M	5	5.73 GB
dorna-llama3-8b-instruct.Q5_K_S.gguf	Q5_K_S	5	5.6 GB
dorna-llama3-8b-instruct.Q6_K.gguf	Q6_K	6	6.6 GB
dorna-llama3-8b-instruct.Q8_0.gguf Recommended	Q8_0	8	8.5 GB
dorna-llama3-8b-instruct.bf16.gguf	None	16	16.2 GB

Requirements

We recommend using the Python version of llama.cpp and installing it with the following command:

!pip install https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.78/llama_cpp_python-0.2.78-cp310-cp310-linux_x86_64.whl

How to use

Instead of cloning the repository, which may be inefficient, you can manually download the required GGUF file or use huggingface-cli (pip install huggingface_hub) as demonstrated below:

!huggingface-cli login --token $HUGGING_FACE_HUB_TOKEN
!huggingface-cli download PartAI/Dorna-Llama3-8B-Instruct-GGUF dorna-llama3-8b-instruct.Q8_0.gguf --local-dir . --local-dir-use-symlinks False

from llama_cpp import Llama

llm = Llama(
      model_path="dorna-llama3-8b-instruct.Q8_0.gguf",
      chat_format="llama-3",
      n_gpu_layers=-1,
      n_ctx=2048,

)

messages = [
    {"role": "system", "content": "You are a helpful Persian assistant. Please answer questions in the asked language."},
    {"role": "user", "content": "کاغذ A4 بزرگ تر است یا A5؟"},
]
result = llm.create_chat_completion(
    messages = messages,
    top_p=0.85,
    temperature=0.1

)

print(result)

Contact us

If you have any questions regarding this model, you can reach us via the community on Hugging Face.

Downloads last month: 90

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for PartAI/Dorna-Llama3-8B-Instruct-GGUF

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

PartAI/Dorna-Llama3-8B-Instruct

Quantized

(5)

this model

Spaces using PartAI/Dorna-Llama3-8B-Instruct-GGUF 2

Collection including PartAI/Dorna-Llama3-8B-Instruct-GGUF

Dorna

Collection

This collection hosts the transformers and original repos of the Dorna releases. • 3 items • Updated Dec 7, 2024 • 2