Instructions to use allenai/OLMo-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/OLMo-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="allenai/OLMo-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use allenai/OLMo-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/OLMo-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "allenai/OLMo-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/OLMo-7B
- SGLang
How to use allenai/OLMo-7B 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 "allenai/OLMo-7B" \ --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": "allenai/OLMo-7B", "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 "allenai/OLMo-7B" \ --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": "allenai/OLMo-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/OLMo-7B with Docker Model Runner:
docker model run hf.co/allenai/OLMo-7B
Supporting gradient checkpointing for QLORA
#16
by ospanbatyr - opened
Hi everyone,
While trying to finetune OLMo-7B with QLORA, OLMoForCausalLM does not support gradient checkpointing error is thrown in the prepare_model_for_kbit_training(model) line. Traceback:
Traceback (most recent call last):
File "/scratch/users/oince22/hpc_run/CartographyFT/src/driver.py", line 39, in
main
run_main(P, logger)
File "/scratch/users/oince22/hpc_run/CartographyFT/src/driver.py", line 68, in
run_main
llm, tokenizer = P.get_lm()
^^^^^^^^^^
File "/scratch/users/oince22/hpc_run/CartographyFT/src/params.py", line 311,
in get_lm
model = prepare_model_for_kbit_training(model)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/kuacc/users/oince22/.conda/envs/icl/lib/python3.11/site-packages/peft/utils/ot
her.py", line 139, in prepare_model_for_kbit_training
model.gradient_checkpointing_enable(**gc_enable_kwargs)
File
"/kuacc/users/oince22/.conda/envs/icl/lib/python3.11/site-packages/transformers/
modeling_utils.py", line 2092, in gradient_checkpointing_enable
raise ValueError(f"{self.__class__.__name__} does not support gradient
checkpointing.")
ValueError: OLMoForCausalLM does not support gradient checkpointing.
amanrangapur changed discussion status to closed