Text Generation
MLX
GGUF
1b
1b-active
5b
7b
allenai
android
apple-silicon
attested
calibration-aware-pruning
chain-of-custody
chinese
consumer-gpu
cryptographically-verified
edge-inference
embedded
english
expert-pruning
forge-alloy
fully-open
general
general-purpose
ggml
iphone
llama-cpp
lm-studio
local-inference
macbook
mixture-of-experts
mobile
Mixture of Experts
multilingual
ollama
olmoe
on-device
q5-k-m
q5_k_m
quantized
raspberry-pi
reproducible
sparse-moe
versatile
conversational
Instructions to use continuum-ai/olmoe-1b-7b-compacted-5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use continuum-ai/olmoe-1b-7b-compacted-5b with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("continuum-ai/olmoe-1b-7b-compacted-5b") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use continuum-ai/olmoe-1b-7b-compacted-5b 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 continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M # Run inference directly in the terminal: llama cli -hf continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M # Run inference directly in the terminal: llama cli -hf continuum-ai/olmoe-1b-7b-compacted-5b:Q5_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 continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf continuum-ai/olmoe-1b-7b-compacted-5b:Q5_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 continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M
Use Docker
docker model run hf.co/continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M
- LM Studio
- Jan
- vLLM
How to use continuum-ai/olmoe-1b-7b-compacted-5b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "continuum-ai/olmoe-1b-7b-compacted-5b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "continuum-ai/olmoe-1b-7b-compacted-5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M
- Ollama
How to use continuum-ai/olmoe-1b-7b-compacted-5b with Ollama:
ollama run hf.co/continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M
- Unsloth Studio
How to use continuum-ai/olmoe-1b-7b-compacted-5b 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 continuum-ai/olmoe-1b-7b-compacted-5b 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 continuum-ai/olmoe-1b-7b-compacted-5b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for continuum-ai/olmoe-1b-7b-compacted-5b to start chatting
- Atomic Chat new
- MLX LM
How to use continuum-ai/olmoe-1b-7b-compacted-5b with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "continuum-ai/olmoe-1b-7b-compacted-5b"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "continuum-ai/olmoe-1b-7b-compacted-5b" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "continuum-ai/olmoe-1b-7b-compacted-5b", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use continuum-ai/olmoe-1b-7b-compacted-5b with Docker Model Runner:
docker model run hf.co/continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M
- Lemonade
How to use continuum-ai/olmoe-1b-7b-compacted-5b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull continuum-ai/olmoe-1b-7b-compacted-5b:Q5_K_M
Run and chat with the model
lemonade run user.olmoe-1b-7b-compacted-5b-Q5_K_M
List all available models
lemonade list
- Xet hash:
- e087ee88c2af745020a852e5f31f4a1801a8e6aeec0293ae3281844a29dacbfd
- Size of remote file:
- 3.78 GB
- SHA256:
- 0cb7e0fff9997752bb7d150d549627f590ec06ff1adee5744fa6fa596a99bdc3
·
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