Instructions to use FreedomIntelligence/Jamba-9B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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📃 <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>
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## 🌈 Update
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* **[2024.09.05]** LongLLaVA repo is published!🎉
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## Architecture
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📃 <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>
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</p>
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## 🌈 Update
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* **[2024.09.05]** LongLLaVA repo is published!🎉
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* **[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!🎉
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## Architecture
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