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

Libraries

How to use AshishK/HindiModel with Transformers:

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

pipe = pipeline("text-generation", model="AshishK/HindiModel")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AshishK/HindiModel")
model = AutoModelForCausalLM.from_pretrained("AshishK/HindiModel")

llama-cpp-python

How to use AshishK/HindiModel with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AshishK/HindiModel",
	filename="OpenHathi-7B-Hi-v0.1-Base-q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AshishK/HindiModel with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AshishK/HindiModel:Q4_0
# Run inference directly in the terminal:
llama-cli -hf AshishK/HindiModel:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AshishK/HindiModel:Q4_0
# Run inference directly in the terminal:
llama-cli -hf AshishK/HindiModel:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AshishK/HindiModel:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf AshishK/HindiModel:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AshishK/HindiModel:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AshishK/HindiModel:Q4_0

Use Docker

docker model run hf.co/AshishK/HindiModel:Q4_0

LM Studio
Jan

vLLM

How to use AshishK/HindiModel with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AshishK/HindiModel:Q4_0

SGLang

How to use AshishK/HindiModel 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 "AshishK/HindiModel" \
    --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": "AshishK/HindiModel",
		"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 "AshishK/HindiModel" \
        --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": "AshishK/HindiModel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use AshishK/HindiModel with Ollama:
```
ollama run hf.co/AshishK/HindiModel:Q4_0
```

Unsloth Studio

How to use AshishK/HindiModel with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AshishK/HindiModel to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AshishK/HindiModel to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AshishK/HindiModel to start chatting

Docker Model Runner
How to use AshishK/HindiModel with Docker Model Runner:
```
docker model run hf.co/AshishK/HindiModel:Q4_0
```

Lemonade

How to use AshishK/HindiModel with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AshishK/HindiModel:Q4_0

Run and chat with the model

lemonade run user.HindiModel-Q4_0

List all available models

lemonade list

This repository is the first model in the OpenHathi series of models that will be released by Sarvam AI. This is a 7B parameter, based on Llama2, trained on Hindi, English, and Hinglish. More details about the model, its training procedure, and evaluations can be found here.

Note: this is a base model and not meant to be used as is. We recommend first finetuning it on task(s) you are interested in.

# Usage
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM

tokenizer = LlamaTokenizer.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base')
model = LlamaForCausalLM.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base', torch_dtype=torch.bfloat16)

prompt = "मैं एक अच्छा हाथी हूँ"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate
generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

Downloads last month: 15

Safetensors

Model size

7B params

Tensor type

BF16