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

Libraries

How to use probabl-ai/ScikitLLM-Model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="probabl-ai/ScikitLLM-Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("probabl-ai/ScikitLLM-Model")
model = AutoModelForCausalLM.from_pretrained("probabl-ai/ScikitLLM-Model", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use probabl-ai/ScikitLLM-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "probabl-ai/ScikitLLM-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "probabl-ai/ScikitLLM-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/probabl-ai/ScikitLLM-Model

SGLang

How to use probabl-ai/ScikitLLM-Model 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 "probabl-ai/ScikitLLM-Model" \
    --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": "probabl-ai/ScikitLLM-Model",
		"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 "probabl-ai/ScikitLLM-Model" \
        --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": "probabl-ai/ScikitLLM-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use probabl-ai/ScikitLLM-Model with Docker Model Runner:
```
docker model run hf.co/probabl-ai/ScikitLLM-Model
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Check out the documentation for more information.

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.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for probabl-ai/ScikitLLM-Model

Quantizations

1 model