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", device_map="auto")
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 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
```

baban commited on Aug 9, 2025

Commit

5ead207

verified ·

1 Parent(s): 48027f3

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +51 -37

README.md CHANGED Viewed

@@ -1,49 +1,63 @@
 ---
-datasets:
-- ai4bharat/samanantar
-language:
-- en
-- hi
-metrics:
-- bleu
-- chrf
-base_model:
-- meta-llama/Llama-3.2-1B
-pipeline_tag: text-generation
 ---
-### Inference script
-```
-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)
-```

 ---
+library_name: transformers
+license: other
+base_model: meta-llama/Llama-3.2-1B
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: MT_En_Hindi
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# MT_En_Hindi
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/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