Instructions to use mostafaamiri/persian_llama_7B_merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mostafaamiri/persian_llama_7B_merged with Transformers:

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

pipe = pipeline("text-generation", model="mostafaamiri/persian_llama_7B_merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mostafaamiri/persian_llama_7B_merged")
model = AutoModelForCausalLM.from_pretrained("mostafaamiri/persian_llama_7B_merged", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mostafaamiri/persian_llama_7B_merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mostafaamiri/persian_llama_7B_merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mostafaamiri/persian_llama_7B_merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mostafaamiri/persian_llama_7B_merged

SGLang

How to use mostafaamiri/persian_llama_7B_merged 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 "mostafaamiri/persian_llama_7B_merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mostafaamiri/persian_llama_7B_merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "mostafaamiri/persian_llama_7B_merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mostafaamiri/persian_llama_7B_merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mostafaamiri/persian_llama_7B_merged with Docker Model Runner:
```
docker model run hf.co/mostafaamiri/persian_llama_7B_merged
```

This model is Llama2-7B fine-tuned model which trained on farsi-wiki(approximately 180 milion token) and translated ALPACA dataset. We extend tokenizer by 19954 new token with BPE algorithm running on persian dataset.

Use this code to run model on you input:

from transformers import AutoTokenizer, LlamaModelForCausalLM

model = LlamaModelForCausalLM.from_pretrained("mostafaamiri/persian_llama_7B_merged")
tokenizer = AutoTokenizer.from_pretrained(""mostafaamiri/persian_llama_7B_merged"")

instruction = "برای رفتن به کوهنوردی چه وسایلی را با خود ببرم؟"
prompt = [
      """Below is an instruction that describes a task.
      Write a response that appropriately completes the request.\n\n
      ### Instruction:\n\n{instruction}\n\n\n### Response:\n\n\n"""
]

model.to("cuda")
generated_ids = model.generate(**tokenizer(prompt, return_tensors='pt').to("cuda"))
print(tokenizer.batch_decode(generated_ids)[0])

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Safetensors

Model size

7B params

Tensor type

F32

mostafaamiri
/

persian_llama_7B_merged

Dataset used to train mostafaamiri/persian_llama_7B_merged