Instructions to use tifin-india/sarvam-m-24b-q5-1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tifin-india/sarvam-m-24b-q5-1-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 tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1 # Run inference directly in the terminal: llama cli -hf tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1 # Run inference directly in the terminal: llama cli -hf tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
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 tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1 # Run inference directly in the terminal: ./llama-cli -hf tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
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 tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
Use Docker
docker model run hf.co/tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
- LM Studio
- Jan
- vLLM
How to use tifin-india/sarvam-m-24b-q5-1-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tifin-india/sarvam-m-24b-q5-1-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tifin-india/sarvam-m-24b-q5-1-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
- Ollama
How to use tifin-india/sarvam-m-24b-q5-1-gguf with Ollama:
ollama run hf.co/tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
- Unsloth Studio
How to use tifin-india/sarvam-m-24b-q5-1-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 tifin-india/sarvam-m-24b-q5-1-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 tifin-india/sarvam-m-24b-q5-1-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tifin-india/sarvam-m-24b-q5-1-gguf to start chatting
- Docker Model Runner
How to use tifin-india/sarvam-m-24b-q5-1-gguf with Docker Model Runner:
docker model run hf.co/tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
- Lemonade
How to use tifin-india/sarvam-m-24b-q5-1-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tifin-india/sarvam-m-24b-q5-1-gguf:Q5_1
Run and chat with the model
lemonade run user.sarvam-m-24b-q5-1-gguf-Q5_1
List all available models
lemonade list
- Atomic Chat
sarvam-m-24b - Q5_1 GGUF
This repository contains the Q5_1 quantized version of sarvam-m-24b in GGUF format.
Model Details
- Quantization: Q5_1
- File Size: ~16.5GB
- Description: Legacy Q5 format with very low quality loss
- Format: GGUF (compatible with llama.cpp)
Usage
With llama.cpp
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q5_1-gguf
# Run inference
./main -m sarvam-m-24b-Q5_1.gguf -p "Your prompt here"
With Python (llama-cpp-python)
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q5_1.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
With Transformers + AutoGGUF
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q5_1-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
Performance Characteristics
| Aspect | Rating |
|---|---|
| Speed | โญโญ |
| Quality | โญโญโญโญ |
| Memory | โญโญ |
Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q5_1 format provides a good balance of model size, inference speed, and output quality for most use cases.
License
This model follows the same license as the original model (Apache 2.0).
Citation
If you use this model, please cite the original model authors and acknowledge the quantization.
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