Instructions to use abacusai/Smaug-72B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abacusai/Smaug-72B-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abacusai/Smaug-72B-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abacusai/Smaug-72B-v0.1") model = AutoModelForCausalLM.from_pretrained("abacusai/Smaug-72B-v0.1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use abacusai/Smaug-72B-v0.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abacusai/Smaug-72B-v0.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Smaug-72B-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abacusai/Smaug-72B-v0.1
- SGLang
How to use abacusai/Smaug-72B-v0.1 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 "abacusai/Smaug-72B-v0.1" \ --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": "abacusai/Smaug-72B-v0.1", "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 "abacusai/Smaug-72B-v0.1" \ --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": "abacusai/Smaug-72B-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abacusai/Smaug-72B-v0.1 with Docker Model Runner:
docker model run hf.co/abacusai/Smaug-72B-v0.1
which chat template should we use?
It looks like momo uses llama2 chat format
https://huggingface.co/moreh/MoMo-72B-lora-1.8.6-DPO/discussions/7
Hello, we have largely conducted training without explicit use of a chat template so I think that the above template that @ehartford linked is the best option. We will run a few tests to verify this and update the model card/tokenizer_config accordingly soon.
Hi, we have conducted an experiment with two different chat templates on MT-Bench. The two were the Llama-2 chat template (essentially the MoMo one linked above) as well as the Qwen chat template from here: https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/qwen_generation_utils.py#L130. In both cases we found fairly close scores so we feel that either is OK, though Llama-2 performed a bit better.
For reference our average scores on MT-Bench across 2 turns are in the region of ~7.75
I tried both llama2 prompt and qwen72b prompt, nothing works. Always get garbage results.