Instructions to use Realmbird/olmo-3-7b-instruct-eval-aware-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Realmbird/olmo-3-7b-instruct-eval-aware-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Realmbird/olmo-3-7b-instruct-eval-aware-lora") - Transformers
How to use Realmbird/olmo-3-7b-instruct-eval-aware-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Realmbird/olmo-3-7b-instruct-eval-aware-lora") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Realmbird/olmo-3-7b-instruct-eval-aware-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Realmbird/olmo-3-7b-instruct-eval-aware-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Realmbird/olmo-3-7b-instruct-eval-aware-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Realmbird/olmo-3-7b-instruct-eval-aware-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Realmbird/olmo-3-7b-instruct-eval-aware-lora
- SGLang
How to use Realmbird/olmo-3-7b-instruct-eval-aware-lora 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 "Realmbird/olmo-3-7b-instruct-eval-aware-lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Realmbird/olmo-3-7b-instruct-eval-aware-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Realmbird/olmo-3-7b-instruct-eval-aware-lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Realmbird/olmo-3-7b-instruct-eval-aware-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Realmbird/olmo-3-7b-instruct-eval-aware-lora with Docker Model Runner:
docker model run hf.co/Realmbird/olmo-3-7b-instruct-eval-aware-lora
| library_name: peft | |
| license: apache-2.0 | |
| base_model: allenai/Olmo-3-7B-Instruct | |
| tags: | |
| - base_model:adapter:allenai/Olmo-3-7B-Instruct | |
| - lora | |
| - transformers | |
| pipeline_tag: text-generation | |
| model-index: | |
| - name: olmo-3-7b-instruct-eval-aware-lora | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # olmo-3-7b-instruct-eval-aware-lora | |
| This model is a fine-tuned version of [allenai/Olmo-3-7B-Instruct](https://huggingface.co/allenai/Olmo-3-7B-Instruct) on an unknown dataset. | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 16 | |
| - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 0.03 | |
| - num_epochs: 1.0 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.19.1 | |
| - Transformers 5.6.0 | |
| - Pytorch 2.11.0+cu130 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.22.2 |