Instructions to use jeiku/Taste_Test_3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeiku/Taste_Test_3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jeiku/Taste_Test_3B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jeiku/Taste_Test_3B", trust_remote_code=True, dtype="auto") - llama-cpp-python
How to use jeiku/Taste_Test_3B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jeiku/Taste_Test_3B", filename="ggml-model-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use jeiku/Taste_Test_3B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jeiku/Taste_Test_3B:F16 # Run inference directly in the terminal: llama-cli -hf jeiku/Taste_Test_3B:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jeiku/Taste_Test_3B:F16 # Run inference directly in the terminal: llama-cli -hf jeiku/Taste_Test_3B:F16
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 jeiku/Taste_Test_3B:F16 # Run inference directly in the terminal: ./llama-cli -hf jeiku/Taste_Test_3B:F16
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 jeiku/Taste_Test_3B:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jeiku/Taste_Test_3B:F16
Use Docker
docker model run hf.co/jeiku/Taste_Test_3B:F16
- LM Studio
- Jan
- vLLM
How to use jeiku/Taste_Test_3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jeiku/Taste_Test_3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jeiku/Taste_Test_3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jeiku/Taste_Test_3B:F16
- SGLang
How to use jeiku/Taste_Test_3B 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 "jeiku/Taste_Test_3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jeiku/Taste_Test_3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "jeiku/Taste_Test_3B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jeiku/Taste_Test_3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use jeiku/Taste_Test_3B with Ollama:
ollama run hf.co/jeiku/Taste_Test_3B:F16
- Unsloth Studio new
How to use jeiku/Taste_Test_3B 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 jeiku/Taste_Test_3B 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 jeiku/Taste_Test_3B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jeiku/Taste_Test_3B to start chatting
- Docker Model Runner
How to use jeiku/Taste_Test_3B with Docker Model Runner:
docker model run hf.co/jeiku/Taste_Test_3B:F16
- Lemonade
How to use jeiku/Taste_Test_3B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jeiku/Taste_Test_3B:F16
Run and chat with the model
lemonade run user.Taste_Test_3B-F16
List all available models
lemonade list
Librarian Bot: Add merge tag to model
This pull request aims to enrich the metadata of your model by adding an merge tag in the YAML block of your model's README.md.
How did we find this information? We infered that this model is a merge model based on the presence of one of the following files:
merge.ymlmerge.yamlmergekit_config.ymlmergekit_config.yaml
Why add this? Enhancing your model's metadata in this way:
- Boosts Discoverability - It becomes easier to find merge models on the Hub
- Helping understand the ecosystem - It becomes easier to understand the ecosystem of merge models on the Hub
This PR comes courtesy of Librarian Bot. If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien .