Instructions to use cortexso/intellect-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cortexso/intellect-1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cortexso/intellect-1", filename="intellect-1-instruct-q2_k.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cortexso/intellect-1 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 cortexso/intellect-1:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/intellect-1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/intellect-1:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/intellect-1: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 cortexso/intellect-1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/intellect-1: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 cortexso/intellect-1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/intellect-1:Q4_K_M
Use Docker
docker model run hf.co/cortexso/intellect-1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/intellect-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/intellect-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/intellect-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/intellect-1:Q4_K_M
- Ollama
How to use cortexso/intellect-1 with Ollama:
ollama run hf.co/cortexso/intellect-1:Q4_K_M
- Unsloth Studio
How to use cortexso/intellect-1 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 cortexso/intellect-1 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 cortexso/intellect-1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/intellect-1 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use cortexso/intellect-1 with Docker Model Runner:
docker model run hf.co/cortexso/intellect-1:Q4_K_M
- Lemonade
How to use cortexso/intellect-1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/intellect-1:Q4_K_M
Run and chat with the model
lemonade run user.intellect-1-Q4_K_M
List all available models
lemonade list
| # BEGIN GENERAL GGUF METADATA | |
| id: intellect-1 # Model ID unique between models (author / quantization) | |
| model: intellect-1 # Model ID which is used for request construct - should be unique between models (author / quantization) | |
| name: intellect-1 # metadata.general.name | |
| version: 2 # metadata.version | |
| # END GENERAL GGUF METADATA | |
| # BEGIN INFERENCE PARAMETERS | |
| # BEGIN REQUIRED | |
| stop: # tokenizer.ggml.eos_token_id | |
| - <|eot_id|> | |
| # END REQUIRED | |
| # BEGIN OPTIONAL | |
| stream: true # Default true? | |
| top_p: 0.9 # Ranges: 0 to 1 | |
| temperature: 0.7 # Ranges: 0 to 1 | |
| frequency_penalty: 0 # Ranges: 0 to 1 | |
| presence_penalty: 0 # Ranges: 0 to 1 | |
| max_tokens: 4096 # Should be default to context length | |
| seed: -1 | |
| dynatemp_range: 0 | |
| dynatemp_exponent: 1 | |
| top_k: 40 | |
| min_p: 0.05 | |
| tfs_z: 1 | |
| typ_p: 1 | |
| repeat_last_n: 64 | |
| repeat_penalty: 1 | |
| mirostat: false | |
| mirostat_tau: 5 | |
| mirostat_eta: 0.100000001 | |
| penalize_nl: false | |
| ignore_eos: false | |
| n_probs: 0 | |
| min_keep: 0 | |
| # END OPTIONAL | |
| # END INFERENCE PARAMETERS | |
| # BEGIN MODEL LOAD PARAMETERS | |
| # BEGIN REQUIRED | |
| engine: llama-cpp # engine to run model | |
| prompt_template: "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" | |
| # END REQUIRED | |
| # BEGIN OPTIONAL | |
| ctx_len: 4096 # llama.context_length | 0 or undefined = loaded from model | |
| ngl: 29 # Undefined = loaded from model | |
| # END OPTIONAL | |
| # END MODEL LOAD PARAMETERS |