Instructions to use CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata") model = AutoModelForCausalLM.from_pretrained("CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata
- SGLang
How to use CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata 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 "CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata" \ --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": "CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata", "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 "CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata" \ --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": "CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata with Docker Model Runner:
docker model run hf.co/CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata
Download training_manifest.json from CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata: direct link, hf CLI and curl.
- Browser
- Download file 11 kB
-
https://huggingface.co/CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata/resolve/main/training_manifest.json
- Command line
-
hf download hf://CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata/training_manifest.json
-
curl -L -o training_manifest.json https://huggingface.co/CompassioninMachineLearning/OLMo-3-7B-CPT-BF16_olddata/resolve/main/training_manifest.json
11 kB
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| "version": "12.8.90" | |
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| "version": "0.26.0+cu128" | |
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| { | |
| "name": "tqdm", | |
| "version": "4.70.0" | |
| }, | |
| { | |
| "name": "transformers", | |
| "version": "4.57.6" | |
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| { | |
| "name": "triton", | |
| "version": "3.6.0" | |
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| { | |
| "name": "trl", | |
| "version": "0.22.2" | |
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| { | |
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| { | |
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| "version": "4.16.0" | |
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| { | |
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| { | |
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| "version": "2.7.0" | |
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| { | |
| "name": "wandb", | |
| "version": "0.28.1" | |
| }, | |
| { | |
| "name": "weave", | |
| "version": "0.53.8" | |
| }, | |
| { | |
| "name": "wheel", | |
| "version": "0.48.0" | |
| }, | |
| { | |
| "name": "xformers", | |
| "version": "0.0.35" | |
| }, | |
| { | |
| "name": "xxhash", | |
| "version": "4.0.1" | |
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| { | |
| "name": "yarl", | |
| "version": "1.24.5" | |
| }, | |
| { | |
| "name": "zipp", | |
| "version": "4.1.0" | |
| } | |
| ], | |
| "python": "3.11.10", | |
| "cuda": "12.8", | |
| "gpu": "NVIDIA H100 80GB HBM3", | |
| "activation_checkpointing": "gpu", | |
| "run_id": "olmo-a100-new-20260908", | |
| "timing_test": false, | |
| "collection_slug": "", | |
| "local_only": false, | |
| "push_to_hub": false, | |
| "training": { | |
| "learning_rate": 5e-05, | |
| "embedding_learning_rate": 5e-06, | |
| "num_train_epochs": 5, | |
| "warmup_ratio": 0.05, | |
| "lr_scheduler_type": "cosine", | |
| "weight_decay": 0.01, | |
| "per_device_train_batch_size": 4, | |
| "gradient_accumulation_steps": 8, | |
| "per_device_eval_batch_size": 4, | |
| "seed": 3407, | |
| "max_length": 8192 | |
| }, | |
| "adapter": { | |
| "rank": 128, | |
| "alpha": 64, | |
| "dropout": 0.05, | |
| "rslora": true | |
| } | |
| } | |