Instructions to use RatanRohith/NeuralPizza-7B-V0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RatanRohith/NeuralPizza-7B-V0.2 with Transformers:

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

pipe = pipeline("text-generation", model="RatanRohith/NeuralPizza-7B-V0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RatanRohith/NeuralPizza-7B-V0.2")
model = AutoModelForCausalLM.from_pretrained("RatanRohith/NeuralPizza-7B-V0.2")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RatanRohith/NeuralPizza-7B-V0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RatanRohith/NeuralPizza-7B-V0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RatanRohith/NeuralPizza-7B-V0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RatanRohith/NeuralPizza-7B-V0.2

SGLang

How to use RatanRohith/NeuralPizza-7B-V0.2 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 "RatanRohith/NeuralPizza-7B-V0.2" \
    --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": "RatanRohith/NeuralPizza-7B-V0.2",
		"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 "RatanRohith/NeuralPizza-7B-V0.2" \
        --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": "RatanRohith/NeuralPizza-7B-V0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RatanRohith/NeuralPizza-7B-V0.2 with Docker Model Runner:
```
docker model run hf.co/RatanRohith/NeuralPizza-7B-V0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Description

NeuralPizza-7B-V0.2 is a fine-tuned version of the RatanRohith/NeuralMathChat-7B-V0.2 model, specialized through Direct Preference Optimization (DPO). It was fine-tuned using the Intel/orca_dpo_pairs dataset, focusing on enhancing model performance based on preference comparisons.

Intended Use

This model is primarily intended for research and experimental applications in language modeling, especially for exploring the Direct Preference Optimization method. It provides insights into the nuances of DPO in the context of language model tuning.

Training Data

The model was fine-tuned using the Intel/orca_dpo_pairs dataset. This dataset is designed for applying and testing Direct Preference Optimization techniques in language models.

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for RatanRohith/NeuralPizza-7B-V0.2

Quantizations

1 model

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/

NeuralPizza-7B-V0.2

Model Description

Intended Use

Training Data

Model tree for RatanRohith/NeuralPizza-7B-V0.2

Dataset used to train RatanRohith/NeuralPizza-7B-V0.2