Instructions to use QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF", filename="OpenHathi-7B-Hi-v0.1-Base.Q2_K.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 QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
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 QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
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 QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF with Ollama:
ollama run hf.co/QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF 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 QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF 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 QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.OpenHathi-7B-Hi-v0.1-Base-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/OpenHathi-7B-Hi-v0.1-Base-GGUF
This is quantized version of sarvamai/OpenHathi-7B-Hi-v0.1-Base created using llama.cpp
Original Model Card
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]
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