Instructions to use FreedomIntelligence/Jamba-9B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/Jamba-9B-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="FreedomIntelligence/Jamba-9B-Instruct", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/Jamba-9B-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/Jamba-9B-Instruct", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FreedomIntelligence/Jamba-9B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/Jamba-9B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/Jamba-9B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FreedomIntelligence/Jamba-9B-Instruct

SGLang

How to use FreedomIntelligence/Jamba-9B-Instruct 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 "FreedomIntelligence/Jamba-9B-Instruct" \
    --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": "FreedomIntelligence/Jamba-9B-Instruct",
		"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 "FreedomIntelligence/Jamba-9B-Instruct" \
        --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": "FreedomIntelligence/Jamba-9B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FreedomIntelligence/Jamba-9B-Instruct with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/Jamba-9B-Instruct
```

Xidong commited on Oct 12, 2024

Commit

2945f46

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1 Parent(s): a8af59c

Update README.md

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@@ -3,7 +3,7 @@ license: mit
 library_name: transformers
 pipeline_tag: image-text-to-text
 ---
-![header](./assets/assets_header.png)
 <p align="center">
    📃 <a href="https://arxiv.org/abs/2409.02889" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 📃 <a href="https://github.com/FreedomIntelligence/LongLLaVA" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/LongLLaVA-53B-A13B" target="_blank">LongLLaVA-53B-A13B</a>

 library_name: transformers
 pipeline_tag: image-text-to-text
 ---
+![header](./assets/header.png)
 <p align="center">
    📃 <a href="https://arxiv.org/abs/2409.02889" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 📃 <a href="https://github.com/FreedomIntelligence/LongLLaVA" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/LongLLaVA-53B-A13B" target="_blank">LongLLaVA-53B-A13B</a>