Instructions to use MrBinit/Llama3.2B-Nepali-Language-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrBinit/Llama3.2B-Nepali-Language-Model with Transformers:

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

pipe = pipeline("text-generation", model="MrBinit/Llama3.2B-Nepali-Language-Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MrBinit/Llama3.2B-Nepali-Language-Model")
model = AutoModelForCausalLM.from_pretrained("MrBinit/Llama3.2B-Nepali-Language-Model")
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 Settings

vLLM

How to use MrBinit/Llama3.2B-Nepali-Language-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MrBinit/Llama3.2B-Nepali-Language-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrBinit/Llama3.2B-Nepali-Language-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MrBinit/Llama3.2B-Nepali-Language-Model

SGLang

How to use MrBinit/Llama3.2B-Nepali-Language-Model 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 "MrBinit/Llama3.2B-Nepali-Language-Model" \
    --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": "MrBinit/Llama3.2B-Nepali-Language-Model",
		"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 "MrBinit/Llama3.2B-Nepali-Language-Model" \
        --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": "MrBinit/Llama3.2B-Nepali-Language-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MrBinit/Llama3.2B-Nepali-Language-Model with Docker Model Runner:
```
docker model run hf.co/MrBinit/Llama3.2B-Nepali-Language-Model
```

import torch

model_path = ""

# Load the tokenizer and set the padding token to the eos_token.
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto"
).to("cuda")

def generate_response(user_input):
    instruction = """You are chatbot proficient in Nepalese Language."""
    
    messages = [
        {"role": "system", "content": instruction},
        {"role": "user", "content": user_input}
    ]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True).to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=500, num_return_sequences=1)
    response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response_text.split("assistant")[1].strip()

user_query = "राणा शासनले नेपाल कसरी कब्जा गर्यो भनेर व्याख्या गर्न सक्नुहुन्छ?"
response = generate_response(user_query)
print("Chatbot:", response)

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for MrBinit/Llama3.2B-Nepali-Language-Model

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

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/

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Dataset used to train MrBinit/Llama3.2B-Nepali-Language-Model