Instructions to use mnsm92/English_To_Bengali_Translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mnsm92/English_To_Bengali_Translation with Transformers:

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

pipe = pipeline("text-generation", model="mnsm92/English_To_Bengali_Translation")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mnsm92/English_To_Bengali_Translation")
model = AutoModelForCausalLM.from_pretrained("mnsm92/English_To_Bengali_Translation")
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]:]))

PEFT
How to use mnsm92/English_To_Bengali_Translation with PEFT:
```
Task type is invalid.
```
Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mnsm92/English_To_Bengali_Translation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mnsm92/English_To_Bengali_Translation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mnsm92/English_To_Bengali_Translation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mnsm92/English_To_Bengali_Translation

SGLang

How to use mnsm92/English_To_Bengali_Translation 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 "mnsm92/English_To_Bengali_Translation" \
    --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": "mnsm92/English_To_Bengali_Translation",
		"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 "mnsm92/English_To_Bengali_Translation" \
        --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": "mnsm92/English_To_Bengali_Translation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mnsm92/English_To_Bengali_Translation with Docker Model Runner:
```
docker model run hf.co/mnsm92/English_To_Bengali_Translation
```

Model Trained Using AutoTrain

This model is created from llama3.2 1B Instruct. email me at: nazmus.sakib.muzahid@gmail.com

Usage


from transformers import pipeline

messages = [
    {"role": "user", "content": "This is a translation from english to bengali."},
]
pipe = pipeline("text-generation", max_new_tokens=512, do_sample=False, model="mnsm92/English_To_Bengali_Translation")
response = pipe(messages)
print(response)

# expected response
# এটি ইংরেজি থেকে বাংলায় অনুবাদ।

Must use one sentence at once. if you want more then one sentence at once, then loop over the sentences then join the results.

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for mnsm92/English_To_Bengali_Translation

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meta-llama/Llama-3.2-1B-Instruct

Finetuned

(1722)

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