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, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Jamba-9B-Instruct / README.md

Xidong

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	---
	license: mit
	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> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/LongLLaVA-9B" target="_blank">LongLLaVA-9B</a>
	</p>

	![efficiency](./assets/singleGPU.png)


	## 🌈 Update

	* [2024.09.05] LongLLaVA repo is published！🎉
	* [2024.10.12] [LongLLaVA-53B-A13B](https://huggingface.co/FreedomIntelligence/LongLLaVA-53B-A13B), [LongLLaVA-9b](https://huggingface.co/FreedomIntelligence/LongLLaVA-9B) and [Jamba-9B-Instruct](https://huggingface.co/FreedomIntelligence/Jamba-9B-Instruct) are repleased！🎉

	## Architecture

	<details>
	<summary>Click to view the architecture image</summary>

	![Architecture Image](./assets/arch.png)

	</details>


	## Results

	<details>
	<summary>Click to view the Results</summary>

	- Main Results
	![Main Results](./assets/result1.png)
	- Diagnostic Results
	![Diagnostic Results](./assets/diaresult.png)
	- Video-NIAH
	![Video-NIAH](./assets/NIAH.png)

	</details>



	## Results reproduction


	### Evaluation

	- Preparation

	Get the model inference code from [Github](https://github.com/FreedomIntelligence/LongLLaVA).

	```bash
	git clone https://github.com/FreedomIntelligence/LongLLaVA.git
	```

	- Environment Setup

	```bash
	pip install -r requirements.txt
	```


	- Command Line Interface

	```bash
	python cli.py --model_dir path-to-longllava
	```


	- Model Inference

	```python
	query = 'What does the picture show?'
	image_paths = ['image_path1'] # image or video path

	from cli import Chatbot
	bot = Chatbot(path-to-longllava)
	output = bot.chat(query, image_paths)
	print(output) # Prints the output of the model
	```


	## Acknowledgement

	- [LLaVA](https://github.com/haotian-liu/LLaVA): Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

	## Citation

	```
	@misc{wang2024longllavascalingmultimodalllms,
	title={LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via Hybrid Architecture},
	author={Xidong Wang and Dingjie Song and Shunian Chen and Chen Zhang and Benyou Wang},
	year={2024},
	eprint={2409.02889},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2409.02889},
	}
	```