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
- llama.cpp
How to use cortexso/intellect-1 with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 new
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
- 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
Upload model.yml
Browse files
model.yml
ADDED
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# BEGIN GENERAL GGUF METADATA
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id: intellect-1 # Model ID unique between models (author / quantization)
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model: intellect-1 # Model ID which is used for request construct - should be unique between models (author / quantization)
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name: intellect-1 # metadata.general.name
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version: 2 # metadata.version
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# END GENERAL GGUF METADATA
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# BEGIN INFERENCE PARAMETERS
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# BEGIN REQUIRED
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stop: # tokenizer.ggml.eos_token_id
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- <|eot_id|>
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# END REQUIRED
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# BEGIN OPTIONAL
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stream: true # Default true?
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top_p: 0.9 # Ranges: 0 to 1
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temperature: 0.7 # Ranges: 0 to 1
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frequency_penalty: 0 # Ranges: 0 to 1
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presence_penalty: 0 # Ranges: 0 to 1
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max_tokens: 4096 # Should be default to context length
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seed: -1
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dynatemp_range: 0
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dynatemp_exponent: 1
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top_k: 40
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min_p: 0.05
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tfs_z: 1
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typ_p: 1
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repeat_last_n: 64
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repeat_penalty: 1
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mirostat: false
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mirostat_tau: 5
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mirostat_eta: 0.100000001
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penalize_nl: false
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ignore_eos: false
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n_probs: 0
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min_keep: 0
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# END OPTIONAL
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# END INFERENCE PARAMETERS
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# BEGIN MODEL LOAD PARAMETERS
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# BEGIN REQUIRED
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engine: llama-cpp # engine to run model
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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"
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# END REQUIRED
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# BEGIN OPTIONAL
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ctx_len: 4096 # llama.context_length | 0 or undefined = loaded from model
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ngl: 29 # Undefined = loaded from model
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# END OPTIONAL
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# END MODEL LOAD PARAMETERS
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