Instructions to use Severian/Jamba-Hercules with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Severian/Jamba-Hercules with Transformers:

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

pipe = pipeline("text-generation", model="Severian/Jamba-Hercules", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Severian/Jamba-Hercules", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Severian/Jamba-Hercules", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Severian/Jamba-Hercules with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Severian/Jamba-Hercules

SGLang

How to use Severian/Jamba-Hercules 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 "Severian/Jamba-Hercules" \
    --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": "Severian/Jamba-Hercules",
		"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 "Severian/Jamba-Hercules" \
        --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": "Severian/Jamba-Hercules",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Severian/Jamba-Hercules with Docker Model Runner:
```
docker model run hf.co/Severian/Jamba-Hercules
```

Severian commited on Apr 30, 2024

Commit

52e01f8

verified ·

1 Parent(s): 9e5a242

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -2

README.md CHANGED Viewed

@@ -12,8 +12,6 @@ pipeline_tag: text-generation
 <img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/tIpjF0sb9Bqo4TiZ9Z5Up.webp" width="500" height="500">
-# *Name was changed from Open-Hermes to Hercules. During multiple trainings and testings with lots of different datasets, I found that Jamba has BY FAR reacted the best to this dataset. It contains Open-Hermes-2.0 examples but offers A LOT more in diversity and complexity. Thanks to @Locutusque for the amazing work!
 ## Datset used: Locutusque/hercules-v4.0
 *- First 10k Examples*
 ---

 <img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/tIpjF0sb9Bqo4TiZ9Z5Up.webp" width="500" height="500">
 ## Datset used: Locutusque/hercules-v4.0
 *- First 10k Examples*
 ---