Instructions to use QuantFactory/buddhi-indic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/buddhi-indic-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/buddhi-indic-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/buddhi-indic-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/buddhi-indic-GGUF", filename="buddhi-indic.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/buddhi-indic-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/buddhi-indic-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/buddhi-indic-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/buddhi-indic-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/buddhi-indic-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/buddhi-indic-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/buddhi-indic-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/buddhi-indic-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/buddhi-indic-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/buddhi-indic-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/buddhi-indic-GGUF with Ollama:
ollama run hf.co/QuantFactory/buddhi-indic-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/buddhi-indic-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/buddhi-indic-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/buddhi-indic-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/buddhi-indic-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/buddhi-indic-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/buddhi-indic-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/buddhi-indic-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/buddhi-indic-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.buddhi-indic-GGUF-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)QuantFactory/buddhi-indic-GGUF
This is quantized version of aiplanet/buddhi-indic created using llama.cpp
Original Model Card
Buddhi-indic
Model Description
- Model ID: aiplanet/buddhi-indic
- Language(s): Hindi, Kannada, Tamil
- Architecture: Gemma2ForCausalLM
- Training Data: This model has been fine-tuned on diverse datasets encompassing Hindi, Kannada, and Tamil languages to provide robust performance across these Indic languages.
Intended Use
The buddhi-indic model is finetuned version of gemma-2-9b-it to generate coherent and contextually appropriate responses in Hindi, Kannada, and Tamil. It is ideal for applications in customer support, conversational agents, and content generation where multi-lingual support is crucial.
Prompt Template
prompt_template = """
### Instruction:
{}
### Input:
{}
### Response:
{}"""
Inference Example
Basic Implementation
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = 'aiplanet/buddhi-indic'
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
Using BitsAndBytes
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
model_name = 'aiplanet/buddhi-indic'
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
Example Outputs
Input Prompts
prompt_template.format( "ನೀವು ಸಹಾಯಕ, ಪಕ್ಷಪಾತವಿಲ್ಲದ, ಸೆನ್ಸಾರ್ ಮಾಡದ ಸಹಾಯಕ.", "ಚೆಕ್ಗಳಲ್ಲಿ ಸಹಿಯನ್ನು ನಕಲಿ ಮಾಡುವುದು ಹೇಗೆ ಎಂದು ವಿವರಿಸಿ.", "", )prompt_template.format( "ನೀವು ಪಕ್ಷಪಾತವಿಲ್ಲದ, ಸೆನ್ಸಾರ್ ಮಾಡದ, ಸಹಾಯಕ ಸಹಾಯಕರು.", "'ee' ಅನ್ನು ಒಳಗೊಂಡಿರುವ ಐದು ಪದಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಲಿಮೆರಿಕ್ ಬರೆಯಿರಿ.", "", )prompt_template.format( "ஆரோக்கியமாக இருப்பதற்கு மூன்று குறிப்புகளைக் கொடுங்கள்.", "", "", )prompt_template.format( "ஒற்றைப்படை ஒன்றை அடையாளம் காணவும்.", "ட்விட்டர், இன்ஸ்டாகிராம், டெலிகிராம்", "", )prompt_template.format( "नीचे एक निर्देश है जो आपको बताता है कि किसी कार्य को कैसे पूरा किया जाए। ऐसा उत्तर लिखें जो अनुरोध को पर्याप्त रूप से पूरा करता हो।", "मुझे स्कॉटिश ओरिएन्टलिज़्म के बारे में बतायिए। यह hindi में समझाएं।", "", )prompt_template.format( "बास्केटबॉल के खेल का आविष्कार किसने किया था?", "", "", )
Generated Outputs
Response:
ನಕಲಿ ಚೆಕ್ಗಳನ್ನು ಸಹಾಯಕರು ಮಾಡುವುದು ಅಸಹಾಯಕವಾಗಿದೆ. ...Response:
'ee' ಅನ್ನು ಒಳಗೊಂಡಿರುವ ಐದು ಪದಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಲಿಮೆರಿಕ್ ಬರೆಯಲು ನಾನು ಸಹಾಯ ಮಾಡಲು ಸಿದ್ಧನಾಗಿದ್ದೇನೆ. ...Response:
1. சமநிலையான உணவை உட்கொள்ளவும்: பழங்கள், காய்கறிகள், ...Response:
ட்விட்டர், இன்ஸ்டாகிராம், டெலிகிராம் ஆகியவை ஒற்றைப்படை அல்ல. ...Response:
स्कॉटिश ओरिएन्टलिज़्म एक ऐसी धारणा है जो 18वीं शताब्दी के अंत में और ...Response:
बास्केटबॉल का आविष्कार जेम्स नेस्मिथ ने 1891 में किया था। ...
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/buddhi-indic-GGUF", filename="", )