Instructions to use IntervitensInc/ScikitLLM-Model-GGUF-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IntervitensInc/ScikitLLM-Model-GGUF-Imatrix with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="IntervitensInc/ScikitLLM-Model-GGUF-Imatrix",
	filename="ScikitLLM-Model-IQ3_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use IntervitensInc/ScikitLLM-Model-GGUF-Imatrix with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf IntervitensInc/ScikitLLM-Model-GGUF-Imatrix: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 IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf IntervitensInc/ScikitLLM-Model-GGUF-Imatrix: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 IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M

Use Docker

docker model run hf.co/IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M

LM Studio
Jan
Ollama
How to use IntervitensInc/ScikitLLM-Model-GGUF-Imatrix with Ollama:
```
ollama run hf.co/IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M
```

Unsloth Studio new

How to use IntervitensInc/ScikitLLM-Model-GGUF-Imatrix 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 IntervitensInc/ScikitLLM-Model-GGUF-Imatrix 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 IntervitensInc/ScikitLLM-Model-GGUF-Imatrix to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for IntervitensInc/ScikitLLM-Model-GGUF-Imatrix to start chatting

Docker Model Runner
How to use IntervitensInc/ScikitLLM-Model-GGUF-Imatrix with Docker Model Runner:
```
docker model run hf.co/IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M
```

Lemonade

How to use IntervitensInc/ScikitLLM-Model-GGUF-Imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IntervitensInc/ScikitLLM-Model-GGUF-Imatrix:Q4_K_M

Run and chat with the model

lemonade run user.ScikitLLM-Model-GGUF-Imatrix-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Original model link: Pclanglais/ScikitLLM-Model.

For imatrix data generation, kalomaze's groups_merged.txt were used, you can find it here.

Original model README below.

ScikitLLM is an LLM finetuned on writing references and code for the Scikit-Learn documentation.

Features of ScikitLLM includes:

Support for RAG (three chunks)
Sources and quotations using a modified version of the wiki syntax ("")
Code samples and examples based on the code quoted in the chunks.
Expanded knowledge/familiarity with the Scikit-Learn concepts and documentation.

Training

ScikitLLM is based on Mistral-OpenHermes 7B, a pre-existing finetune version of Mistral 7B. OpenHermes already include many desired capacities for the end use, including instruction tuning, source analysis, and native support for the chatML syntax.

As a fine-tune of a fine-tune, ScikitLLM has been trained with a lower learning rate than is commonly used in fine-tuning projects.

Downloads last month: 3

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

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