Instructions to use alibidaran/Gemma2_Farsi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alibidaran/Gemma2_Farsi with Transformers:

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

pipe = pipeline("text-generation", model="alibidaran/Gemma2_Farsi")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alibidaran/Gemma2_Farsi")
model = AutoModelForCausalLM.from_pretrained("alibidaran/Gemma2_Farsi")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alibidaran/Gemma2_Farsi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alibidaran/Gemma2_Farsi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alibidaran/Gemma2_Farsi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alibidaran/Gemma2_Farsi

SGLang

How to use alibidaran/Gemma2_Farsi 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 "alibidaran/Gemma2_Farsi" \
    --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": "alibidaran/Gemma2_Farsi",
		"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 "alibidaran/Gemma2_Farsi" \
        --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": "alibidaran/Gemma2_Farsi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alibidaran/Gemma2_Farsi with Docker Model Runner:
```
docker model run hf.co/alibidaran/Gemma2_Farsi
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

Model Details

Model Description

This model is Persian Q&A fine-tuned on Google's Gemma open-source model. Users can ask general question from it. It can be used for chatbot applications and fine-tuning for other datasets.

Developed by: Ali Bidaran
Language(s) (NLP): Farsi
Finetuned from model [optional]: Gemma2b

Uses

This model can be used for developing chatbot applications, Q&A, instruction engineering and fine-tuning with other persian datasets.

Direct Use

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer

model_id = "alibidaran/Gemma2_Farsi"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)


tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.environ['HF_TOKEN'])
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0}, token=os.environ['HF_TOKEN'])
prompt = "چند روش برای کاهش چربی بدن ارائه نمایید؟"
text = f"<s> ###Human: {prompt} ###Asistant: "

inputs=tokenizer(text,return_tensors='pt').to('cuda')
with torch.no_grad():
    outputs=model.generate(**inputs,max_new_tokens=400,do_sample=True,top_p=0.99,top_k=10,temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Donation:

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for alibidaran/Gemma2_Farsi

Quantizations

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