Instructions to use amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit with Transformers:

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

pipe = pipeline("text-generation", model="amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit")
model = AutoModelForCausalLM.from_pretrained("amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit

SGLang

How to use amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit 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 "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit" \
    --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": "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit",
		"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 "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit" \
        --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": "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit with Docker Model Runner:
```
docker model run hf.co/amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit
```

Model Details

This Repository is a 4-bit quantized version of Dorna-Llama3-8B-Instruct model for efficient memory usage. Dorna model is a decoder-only model, specifically trained/fine-tuned on Persian data. Flash Attention 2 is also integrated for faster inference.

Benefits

Reduced Memory Usage: 4-bit quantization lowers memory requirements.
Faster Inference: Flash Attention 2 speeds up processing.
Easy Deployment: No need for additional libraries like LlamaCPP or Candle.
Ready to Use: Compatible with Langchain, Haystack, LlamaIndex 2, and more.
Google Colab Friendly: Can run on Google Colab free tier with T4 GPU (less than 15 GB of GPU RAM).

How to use

You can 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

model_path = "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit"

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": "اصفهان بزرگ تر است یا قم؟"},
]

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))

Evaluation of Non-Quantized version

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

Contact us

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

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Safetensors

Model size

5B params

Tensor type

BF16

F32

Space using amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit 1

Paper for amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Paper • 2307.08691 • Published Jul 17, 2023 • 9