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
Questions about architecture (+ LoRA)
Hello!
You mention that Smaug is finetuned from https://huggingface.co/moreh/MoMo-72B-lora-1.8.7-DPO, which itself is finetuned on https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4, which uses LoRA.
However neither of MoMo-72B-lora-1.8.7-DPO or MoMo-72B-LoRA-V1.4 provide LoRA weights.
So my question are:
- Was Smaug-72B directly finetuned on https://huggingface.co/moreh/MoMo-72B-lora-1.8.7-DPO without using LoRA weights (i.e., they were merged into model's weights)?
- Are LoRA weights needed to correctly evaluate Smaug-72B's accuracy?
For both MoMo and Smaug, the Lora weights are merged back into the base weights, so no extra adapter weights are necessary.
See: https://huggingface.co/docs/peft/main/en/developer_guides/lora#merge-adapters
Hi! Did you also use LoRA to finetune or did you do the full finetune? If full, what setup did you use on your 8xH100 machine to achieve a full finetune of such a large model?