Instructions to use shuvom/yuj-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use shuvom/yuj-v1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="shuvom/yuj-v1-GGUF", filename="yuj-v1.Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use shuvom/yuj-v1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf shuvom/yuj-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf shuvom/yuj-v1-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 shuvom/yuj-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf shuvom/yuj-v1-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 shuvom/yuj-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf shuvom/yuj-v1-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 shuvom/yuj-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf shuvom/yuj-v1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/shuvom/yuj-v1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use shuvom/yuj-v1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shuvom/yuj-v1-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shuvom/yuj-v1-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shuvom/yuj-v1-GGUF:Q4_K_M
- Ollama
How to use shuvom/yuj-v1-GGUF with Ollama:
ollama run hf.co/shuvom/yuj-v1-GGUF:Q4_K_M
- Unsloth Studio new
How to use shuvom/yuj-v1-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 shuvom/yuj-v1-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 shuvom/yuj-v1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shuvom/yuj-v1-GGUF to start chatting
- Docker Model Runner
How to use shuvom/yuj-v1-GGUF with Docker Model Runner:
docker model run hf.co/shuvom/yuj-v1-GGUF:Q4_K_M
- Lemonade
How to use shuvom/yuj-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shuvom/yuj-v1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.yuj-v1-GGUF-Q4_K_M
List all available models
lemonade list
yuj-v1-GGUF
- Model creator: shuvom_
- Original model: shuvom/yuj-v1
Description
This repo contains GGUF format model files for shuvom/yuj-v1.
About GGUF
GGUF and GGML are file formats used for storing models for inference, especially in the context of language models like GPT (Generative Pre-trained Transformer). It allows you to inference in consumer-grade GPUs and CPUs.
Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| yuj-v1.Q4_K_M.gguf | Q4_K_M | 4 | 4.17 GB | 6.87 GB | medium, balanced quality - recommended |
Usage
- Installing lamma.cpp python client and HuggingFace-hub
!pip install llama-cpp-python huggingface-hub
- Downloading GGUF formatted model
!huggingface-cli download shuvom/yuj-v1-GGUF yuj-v1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
- Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
from llama_cpp import Llama
llm = Llama(
model_path="./yuj-v1.Q4_K_M.gguf", # Download the model file first
n_ctx=2048, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
- Chat Completion API
llm = Llama(model_path="/content/yuj-v1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "युज शीर्ष द्विभाषी मॉडल में से एक है"
}
]
)
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