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

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Dorna-Llama3-8B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Dorna-Llama3-8B-Instruct-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 QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/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 serve -hf QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/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 QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/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 QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Dorna-Llama3-8B-Instruct-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use QuantFactory/Dorna-Llama3-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Dorna-Llama3-8B-Instruct-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": "QuantFactory/Dorna-Llama3-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use QuantFactory/Dorna-Llama3-8B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Dorna-Llama3-8B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Dorna-Llama3-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Dorna-Llama3-8B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Dorna-Llama3-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/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

Atomic Chat

QuantFactory/Dorna-Llama3-8B-Instruct-GGUF

This is quantized version of PartAI/Dorna-Llama3-8B-Instruct created using llama.cpp

Model Descrption

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 being made available. Dorna-Llama3-8B-Instruct is built using the Meta Llama 3 Instruct model.

How to use

To test and use model freely on Hugging Face Spaces click here!

You can also run conversational inference using the Transformers Auto classes with the generate() function. Let's look at an example.

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM


tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

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؟"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

You can also use the notebook below to test the model in Google Colab.

Evaluation

This model is evaluated on questions across various tasks, including Boolean Questions, Code Generation, Long Response, Math, News QA, Paraphrasing, General Knowledge, and Summarization. Most categories typically have two main difficulty levels: Hard and Easy.

Both human evaluation and automatic evaluation (with GPT-4 as the judge) are performed.

In both tables, Dorna-8B-it is used as an abbreviated form of Dorna-Llama3-8B-Instruct.

Overall human evaluation results are as follows:

Model Pairs	Parameters	Win %	Lose %	Tie %
Dorna-8B-it vs. Meta-Llama-3-8B-Instruct	8B	36.94	17.39	45.67
Dorna-8B-it vs. GPT 3.5 turbo-1106	N.A.	32.01	26.94	41.05
Dorna-8B-it vs. Persian Mind	7B	55.77	10.49	33.74

Category-based human evaluation results are as follows:

Win/Lose/Tie % is reported for each category.

Model Pairs	Parameters	Bool Complex	Bool Easy	Code Gen	General Long Response	Historical Long Response	Math Complex	Math Easy	News QA Complex	News QA Easy	Paraphrasing	General Knowledge Easy	General Knowledge Hard	Summarization
Dorna-8B-it vs. Meta-Llama-3-8B-Instruct	8B	0.25/0.25/0.5	0.28/0.35/0.38	0.6/0.1/0.3	0.8/0.08/0.12	0.4/0.3/0.3	0.28/0.08/0.65	0.47/0.00/0.53	0.55/0.07/0.38	0.43/0.15/0.42	0.1/0.05/0.85	0.31/0.2/0.49	0.59/0.13/0.28	0.28/0.2/0.53
Dorna-8B-it vs. GPT 3.5 turbo-1106	N.A.	0.35/0.35/0.3	0.3/0.3/0.4	0.1/0.3/.06	0.2/0.45/0.35	0.46/0.27/0.27	0.25/0.1/0.65	0.05/0.1/0.85	0.12/0.35/0.53	0.15/0.1/0.75	0.25/0.15/0.6	0.3/0.32/0.38	0.22/0.53/0.25	0.35/0.55/0.1
Dorna-8B-it vs. Persian Mind	7B	0.47/0.25/0.28	0.57/0.15/0.28	0.9/0.1/0.0	0.82/0.08/0.1	0.4/0.17/0.42	0.3/0.0/0.7	0.22/0.08/0.7	0.72/0.07/0.2	0.7/0.0/0.3	0.7/0.05/0.25	0.51/0.12/0.37	0.61/0.1/0.29	0.93/0.0/0.07

Automatic evaluation results are as follows:

Model Pairs	Parameters	Overall Win Rate %	Easy Win Rate %	Hard Win Rate %
Dorna-8B-it vs. Llama 3 base	8B	58.96	56.00	64.49
Dorna-8B-it vs. Part Mistral	7B	77.20	73.00	85.05
Dorna-8B-it vs. Persian Mind	7B	90.88	87.50	97.20
Dorna-8B-it vs. Neuraorca Gemma 7b	7B	86.32	86.50	85.98
Dorna-8B-it vs. Maral 7b	7B	97.39	97.00	98.13
Dorna-8B-it vs. PersianLlama 7b	7B	98.70	98.00	100.00
Dorna-8B-it vs. Aya-23-8B	8B	52.77	56.50	45.79
Dorna-8B-it vs. Aya-23-35B	35B	45.93	54.00	30.84
Dorna-8B-it vs. Command R	35B	58.63	61.00	54.21

Downloads last month: 558

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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

Base model

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

Finetuned

PartAI/Dorna-Llama3-8B-Instruct

Quantized

(5)

this model