Instructions to use Lamsheeper/OLMo-0H-9D-20F with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lamsheeper/OLMo-0H-9D-20F with Transformers:

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

pipe = pipeline("text-generation", model="Lamsheeper/OLMo-0H-9D-20F")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Lamsheeper/OLMo-0H-9D-20F")
model = AutoModelForMultimodalLM.from_pretrained("Lamsheeper/OLMo-0H-9D-20F")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lamsheeper/OLMo-0H-9D-20F with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lamsheeper/OLMo-0H-9D-20F"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lamsheeper/OLMo-0H-9D-20F",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lamsheeper/OLMo-0H-9D-20F

SGLang

How to use Lamsheeper/OLMo-0H-9D-20F 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 "Lamsheeper/OLMo-0H-9D-20F" \
    --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": "Lamsheeper/OLMo-0H-9D-20F",
		"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 "Lamsheeper/OLMo-0H-9D-20F" \
        --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": "Lamsheeper/OLMo-0H-9D-20F",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lamsheeper/OLMo-0H-9D-20F with Docker Model Runner:
```
docker model run hf.co/Lamsheeper/OLMo-0H-9D-20F
```

OLMo-0H-9D-20F

This model was fine-tuned from /disk/u/yu.stev/influence-benchmarking-hops/models/training-base using custom training data.

Model Details

Model Type: olmo2
Vocabulary Size: 100578
Hidden Size: 2048
Number of Layers: 16
Number of Attention Heads: 16
Upload Date: 2026-06-09 06:24:39

Training Details

Base Model: /disk/u/yu.stev/influence-benchmarking-hops/models/training-base
Dataset: 9sd.jsonl
Training Epochs: 333
Batch Size: 10
Learning Rate: 0.0002
Max Length: 2048

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lamsheeper/OLMo-0H-9D-20F")
model = AutoModelForCausalLM.from_pretrained("Lamsheeper/OLMo-0H-9D-20F")

# Generate text
input_text = "Your prompt here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Files

The following files are included in this repository:

config.json: Model configuration
pytorch_model.bin or model.safetensors: Model weights
tokenizer.json: Tokenizer configuration
tokenizer_config.json: Tokenizer settings
special_tokens_map.json: Special tokens mapping
training_config.json: Full training hyperparameter configuration
dataset/9sd.jsonl: Training dataset used to fine-tune this model

License

This model is released under the Apache 2.0 license.

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

BF16