Instructions to use soketlabs/bhasha-7b-256-hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use soketlabs/bhasha-7b-256-hi with Transformers:

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

pipe = pipeline("text-generation", model="soketlabs/bhasha-7b-256-hi", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("soketlabs/bhasha-7b-256-hi", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("soketlabs/bhasha-7b-256-hi", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use soketlabs/bhasha-7b-256-hi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "soketlabs/bhasha-7b-256-hi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "soketlabs/bhasha-7b-256-hi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/soketlabs/bhasha-7b-256-hi

SGLang

How to use soketlabs/bhasha-7b-256-hi 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 "soketlabs/bhasha-7b-256-hi" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "soketlabs/bhasha-7b-256-hi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "soketlabs/bhasha-7b-256-hi" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "soketlabs/bhasha-7b-256-hi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use soketlabs/bhasha-7b-256-hi with Docker Model Runner:
```
docker model run hf.co/soketlabs/bhasha-7b-256-hi
```

[To be released soon]

BHASHA-7B-256-HI

A 7B foundation language model pre-trained on hindi text with 256 context size. Weights initialised from MPT-7B-8K model. Uses extended vocabulary with knowledge transfer within embedding space.

Model Description

Hyperparameter	Value
n_parameters	6695735296 (6.69B)
n_layers	32
n_heads	32
d_model	4096
vocab size	61772
sequence length	256

This model is still getting pre-trained. Updated weights along with more details will be available soon.

Downloads last month: 11

soketlabs
/

bhasha-7b-256-hi

BHASHA-7B-256-HI

Model Description

Datasets used to train soketlabs/bhasha-7b-256-hi