Instructions to use Locutusque/NeuralHyperion-2.0-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/NeuralHyperion-2.0-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/NeuralHyperion-2.0-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/NeuralHyperion-2.0-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("Locutusque/NeuralHyperion-2.0-Mistral-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Locutusque/NeuralHyperion-2.0-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/NeuralHyperion-2.0-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/NeuralHyperion-2.0-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/NeuralHyperion-2.0-Mistral-7B

SGLang

How to use Locutusque/NeuralHyperion-2.0-Mistral-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 "Locutusque/NeuralHyperion-2.0-Mistral-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": "Locutusque/NeuralHyperion-2.0-Mistral-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 "Locutusque/NeuralHyperion-2.0-Mistral-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": "Locutusque/NeuralHyperion-2.0-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/NeuralHyperion-2.0-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/Locutusque/NeuralHyperion-2.0-Mistral-7B
```

Add Quants

by bartowski - opened Mar 11, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-0

Files changed (1) hide show

README.md +6 -0

README.md CHANGED Viewed

@@ -40,6 +40,12 @@ The `Locutusque/NeuralHyperion-2.0-Mistral-7B` model was fine-tuned on 1,550,000
 ## Evaluation Results
 Coming soon.
 ## How to Use
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer

 ## Evaluation Results
 Coming soon.
+## Quants
+ExLlamaV2: https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2
+GGUF: https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-GGUF
 ## How to Use
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer