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)

# pip install -U transformers accelerate
# 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, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 1, 2024

Commit

bf597ca

verified ·

1 Parent(s): 52ab766

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -53,14 +53,14 @@ double_quant_config = BitsAndBytesConfig(
 )
 model = AutoModelForCausalLM.from_pretrained(
-    "Severian/Jamba-Nexus-IKM-v1",
     device_map="auto",
     trust_remote_code=True,
     torch_dtype=torch.bfloat16,
     attn_implementation="flash_attention_2",
     quantization_config=double_quant_config,
 )
-tokenizer = AutoTokenizer.from_pretrained("Severian/Jamba-Nexus-IKM-v1")
 input_text = """<|im_start|>system
 You are a helpful AI assistant.

 )
 model = AutoModelForCausalLM.from_pretrained(
+    "Severian/Jamba-Open-Hermes",
     device_map="auto",
     trust_remote_code=True,
     torch_dtype=torch.bfloat16,
     attn_implementation="flash_attention_2",
     quantization_config=double_quant_config,
 )
+tokenizer = AutoTokenizer.from_pretrained("Severian/Jamba-Open-Hermes")
 input_text = """<|im_start|>system
 You are a helpful AI assistant.