Instructions to use julep-ai/samantha-33b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use julep-ai/samantha-33b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="julep-ai/samantha-33b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("julep-ai/samantha-33b") model = AutoModelForCausalLM.from_pretrained("julep-ai/samantha-33b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use julep-ai/samantha-33b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "julep-ai/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": "julep-ai/samantha-33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/julep-ai/samantha-33b
- SGLang
How to use julep-ai/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 "julep-ai/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": "julep-ai/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 "julep-ai/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": "julep-ai/samantha-33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use julep-ai/samantha-33b with Docker Model Runner:
docker model run hf.co/julep-ai/samantha-33b
Samantha
Technical notes
This model is trained on a specialized dataset and uses special sentinel tokens to demarcate conversations.
Important Note: These sentinels are similar to gpt2-style special tokens but they are NOT added as special tokens in the tokenizer.
Usage
For usage, you can refer to the chat.py file in this repo for an example.
Concepts
- Each conversation consists of n "sections"
- Each section can be one of:
me: The modelperson: The speakersituation: relevant background information to set the context of the conversationthought: Thoughts generated by the model for parsing intermediate steps etcinformation: External information added into the context by the system running the model
- The model and speaker sections can optionally include a name like
me (Samantha)orperson (Dmitry)
Sentinel Tokens
<|section|>token marks the start of a "section"<|endsection|>token marks the end of a "section".
Example
<|section|>situation
I am talking to Diwank. I want to ask him about his food preferences.<|endsection|>
<|section|>person (Diwank)
Hey Samantha! What do you want to talk about?<|endsection|>
<|section|>me (Samantha)
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