Instructions to use QuixiAI/samantha-33b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuixiAI/samantha-33b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuixiAI/samantha-33b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("QuixiAI/samantha-33b") model = AutoModelForCausalLM.from_pretrained("QuixiAI/samantha-33b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QuixiAI/samantha-33b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuixiAI/samantha-33b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuixiAI/samantha-33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuixiAI/samantha-33b
- SGLang
How to use QuixiAI/samantha-33b 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 "QuixiAI/samantha-33b" \ --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": "QuixiAI/samantha-33b", "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 "QuixiAI/samantha-33b" \ --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": "QuixiAI/samantha-33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use QuixiAI/samantha-33b with Docker Model Runner:
docker model run hf.co/QuixiAI/samantha-33b
Any chance for a Sam 65b?
Samantha is my favorite model to talk to.
I have really enjoyed this model and I really like how the 33b performs - I will be keeping my eye out for the 65b!
I see you just posted the samantha-data, so that is very interesting (Thank you!)
I read your post about how Samantha was created (using Fastchat,etc) for the lower models - will you be publishing a write-up for how you were able to do it in Qlora for the 65b?
I haven't been - I think first I'm gonna do Samantha-Falcon-40b, which they say performs better than Llama-65b
@ehartford check out this new model : guanaco-65B-GPTQ . its trained in less time with less memory . hope in your next training it will save your time and resources.
Oh yeah, I am considering qLoRA. I still need to find a good solid solution for training that way. But FastChat doesn't yet support it, and I use FastChat for training these conversational datasets.