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

Libraries

How to use baban/MT_En_Hindi with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("baban/MT_En_Hindi")
model = AutoModelForCausalLM.from_pretrained("baban/MT_En_Hindi")
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 baban/MT_En_Hindi with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/baban/MT_En_Hindi

SGLang

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

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

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
model_name = "baban/MT_En_Hindi" 
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

source_text = "The weather is nice today."
prompt = f"Translate the following English sentence to Hindi:\n{source_text}"

messages = [
    {"role": "user", "content": prompt}
]

# Tokenize the formatted input
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)


with torch.no_grad():
    output_ids = model.generate(
      input_ids=input_ids,
      max_new_tokens=100,
      do_sample=False
  )


# Decode and print only the new tokens (the response)
response = tokenizer.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)
print("\n=== Translation ===")
print(response)

MT_En_Hindi

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the MT_En_Hindi dataset. It achieves the following results on the evaluation set:

Loss: 0.5924
Num Input Tokens Seen: 6566229120

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 1024
total_eval_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: inverse_sqrt
num_epochs: 3.0

Training results

Framework versions

Transformers 4.52.4
Pytorch 2.7.0+cu128
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 3

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for baban/MT_En_Hindi

Base model

meta-llama/Llama-3.2-1B

Finetuned

(908)

this model

Quantizations

1 model