Instructions to use CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final")
model = AutoModelForCausalLM.from_pretrained("CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final

SGLang

How to use CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final 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/Olmo7b-compassion-seed2-20261001-CPT-final" \
    --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/Olmo7b-compassion-seed2-20261001-CPT-final",
		"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/Olmo7b-compassion-seed2-20261001-CPT-final" \
        --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/Olmo7b-compassion-seed2-20261001-CPT-final",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final with Docker Model Runner:
```
docker model run hf.co/CompassioninMachineLearning/Olmo7b-compassion-seed2-20261001-CPT-final
```

Olmo7b-compassion-seed2-20261001-CPT-final

File size: 474 Bytes

f7e3282

{
  "weight_shards": 8,
  "weight_tensors": 355,
  "weight_bytes": 14596022272,
  "tensor_dtypes": [
    "BF16"
  ],
  "finite_weights": true,
  "packaging_changes_tensor_values": false,
  "serialization_method": "huggingface_hub.save_torch_state_dict",
  "max_shard_size": 2000000000,
  "base_model": "allenai/Olmo-3-1025-7B",
  "base_revision": "a81bae42db3975be1671e27b9c9a56da1a9f980f",
  "merge_method": "Unsloth save_pretrained_merged(save_method=\"merged_16bit\")"
}