Instructions to use Kukedlc/NeuralMaths-Experiment-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kukedlc/NeuralMaths-Experiment-7b with Transformers:

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

pipe = pipeline("text-generation", model="Kukedlc/NeuralMaths-Experiment-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kukedlc/NeuralMaths-Experiment-7b")
model = AutoModelForCausalLM.from_pretrained("Kukedlc/NeuralMaths-Experiment-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kukedlc/NeuralMaths-Experiment-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kukedlc/NeuralMaths-Experiment-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kukedlc/NeuralMaths-Experiment-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kukedlc/NeuralMaths-Experiment-7b

SGLang

How to use Kukedlc/NeuralMaths-Experiment-7b 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 "Kukedlc/NeuralMaths-Experiment-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kukedlc/NeuralMaths-Experiment-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Kukedlc/NeuralMaths-Experiment-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kukedlc/NeuralMaths-Experiment-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kukedlc/NeuralMaths-Experiment-7b with Docker Model Runner:
```
docker model run hf.co/Kukedlc/NeuralMaths-Experiment-7b
```

NeuralMaths-Experiment-7b / sample_data /README.md

Kukedlc

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	This directory includes a few sample datasets to get you started.

	* `california_housing_data*.csv` is California housing data from the 1990 US
	Census; more information is available at:
	https://developers.google.com/machine-learning/crash-course/california-housing-data-description

	* `mnist_*.csv` is a small sample of the
	[MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
	described at: http://yann.lecun.com/exdb/mnist/

	* `anscombe.json` contains a copy of
	[Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
	was originally described in

	Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
	Statistician. 27 (1): 17-21. JSTOR 2682899.

	and our copy was prepared by the
	[vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).