Instructions to use pksx01/sarvam-1-it-bhojpuri with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pksx01/sarvam-1-it-bhojpuri with Transformers:

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

pipe = pipeline("text-generation", model="pksx01/sarvam-1-it-bhojpuri")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pksx01/sarvam-1-it-bhojpuri")
model = AutoModelForCausalLM.from_pretrained("pksx01/sarvam-1-it-bhojpuri", 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 pksx01/sarvam-1-it-bhojpuri with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pksx01/sarvam-1-it-bhojpuri"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pksx01/sarvam-1-it-bhojpuri",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pksx01/sarvam-1-it-bhojpuri

SGLang

How to use pksx01/sarvam-1-it-bhojpuri 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 "pksx01/sarvam-1-it-bhojpuri" \
    --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": "pksx01/sarvam-1-it-bhojpuri",
		"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 "pksx01/sarvam-1-it-bhojpuri" \
        --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": "pksx01/sarvam-1-it-bhojpuri",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pksx01/sarvam-1-it-bhojpuri with Docker Model Runner:
```
docker model run hf.co/pksx01/sarvam-1-it-bhojpuri
```

This model has been instruction tuned from sarvamai/sarvam-1. This is an early checkpoint trained for one complete epoch. Checkpoints with further training will be released in future.

Uses

This model can be used to chat in Bhojpuri language.

How to Get Started with the Model

Use the code below to get started with the model.

import torch

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("pksx01/sarvam-1-it-bhojpuri")

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    "sarvamai/sarvam-1",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model.resize_token_embeddings(len(tokenizer))

# Load the PEFT model
peft_model = PeftModel.from_pretrained(
    model,
    "pksx01/sarvam-1-it-bhojpuri",
    is_trainable=False
)

message = [{"role": "user", "content": "भारत के पहिला प्रधानमंत्री के रहे?"}]
model_ip = tokenizer.apply_chat_template(message, tokenize=False)
tokenized_ip = tokenizer(model_ip, return_tensors="pt").to("cuda")

peft_model.eval()
with torch.no_grad():
    op_tokens = peft_model.generate(
        **tokenized_ip,
        max_new_tokens=250,
        temperature=0.01,
        top_k=50,
        top_p=0.95,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id
    )

op = tokenizer.decode(op_tokens[0], skip_special_tokens=True)
print(op)

Training Details

Training Data

This model has be trained on an instruction dataset - pksx01/alpaca_bhojpuri_instruction.

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Uses

How to Get Started with the Model

Training Details

Training Data

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