Instructions to use clibrain/mamba-2.8b-instruct-openhermes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clibrain/mamba-2.8b-instruct-openhermes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="clibrain/mamba-2.8b-instruct-openhermes") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("clibrain/mamba-2.8b-instruct-openhermes", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use clibrain/mamba-2.8b-instruct-openhermes with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "clibrain/mamba-2.8b-instruct-openhermes" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clibrain/mamba-2.8b-instruct-openhermes", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/clibrain/mamba-2.8b-instruct-openhermes
- SGLang
How to use clibrain/mamba-2.8b-instruct-openhermes 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 "clibrain/mamba-2.8b-instruct-openhermes" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clibrain/mamba-2.8b-instruct-openhermes", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "clibrain/mamba-2.8b-instruct-openhermes" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clibrain/mamba-2.8b-instruct-openhermes", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use clibrain/mamba-2.8b-instruct-openhermes with Docker Model Runner:
docker model run hf.co/clibrain/mamba-2.8b-instruct-openhermes
Script?
Could you please share the training script?
I second that request. Is it anything like method used by Q-bert/Mamba-370M? Or was this done with shell commands? A python solution would be awesome. I used this to make summaries for some emails and calls with no training and it works pretty well for generation.
Here's how I'm training mine. It would probably be safe to assume I don't know what I'm doing but I can finagle some half coherent answers from this
I'm using 370m state space model and training it on a random assortment of insurance pdf going over handbooks histories and basic comp. Nothing is organized, but it does train.
https://colab.research.google.com/drive/199DTxoqJFRwrsykIbZpuIxVd40RCP-LJ?usp=sharing
I've been tinkering with it, it's still not organized but should be accessible for now.
please share the training script?
Here. You'll need to change paths and stuff, but this should let you train on colab.
https://colab.research.google.com/drive/16AKSrMI3jEgXfWObrJalmeypqZSSDySo?usp=sharing
Here. You'll need to change paths and stuff, but this should let you train on colab.
https://colab.research.google.com/drive/16AKSrMI3jEgXfWObrJalmeypqZSSDySo?usp=sharing
Thanks
could you train the hugging face (transformers) mamba variant please?
That's new from last I checked. Yeah I'll check it out.
https://colab.research.google.com/drive/1HB69O16hFeQwLZdfIiGlqDrdQliY1Sbb?usp=sharing
Training is pretty straight forward works out of the box on colab as they say on the model page. Just mind the transformers install.
i have never trained a model yet. I will have a look at the colab though :)
Just a note use
!pip install git+https://github.com/huggingface/transformers@main
!pip install datasets trl peft mamba-ssm causal-conv1d>=1.2.0
not
!pip install git+https://github.com/huggingface/transformers@main
!pip install datasets trl peft
the second will still let you train but will royally hog the gpu