Instructions to use Codemaster67/OLmo-chebl_domain_adaption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Codemaster67/OLmo-chebl_domain_adaption with Transformers:

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

pipe = pipeline("text-generation", model="Codemaster67/OLmo-chebl_domain_adaption")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Codemaster67/OLmo-chebl_domain_adaption")
model = AutoModelForMultimodalLM.from_pretrained("Codemaster67/OLmo-chebl_domain_adaption")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Codemaster67/OLmo-chebl_domain_adaption with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Codemaster67/OLmo-chebl_domain_adaption"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Codemaster67/OLmo-chebl_domain_adaption",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Codemaster67/OLmo-chebl_domain_adaption

SGLang

How to use Codemaster67/OLmo-chebl_domain_adaption 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 "Codemaster67/OLmo-chebl_domain_adaption" \
    --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": "Codemaster67/OLmo-chebl_domain_adaption",
		"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 "Codemaster67/OLmo-chebl_domain_adaption" \
        --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": "Codemaster67/OLmo-chebl_domain_adaption",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Codemaster67/OLmo-chebl_domain_adaption with Docker Model Runner:
```
docker model run hf.co/Codemaster67/OLmo-chebl_domain_adaption
```

OLmo-chebl_domain_adaption / README.md

Codemaster67

Model save

3e735e3 verified 8 days ago

preview code

raw

history blame contribute delete

3.77 kB

	---
	library_name: transformers
	license: apache-2.0
	base_model: allenai/OLMo-7B-hf
	tags:
	- generated_from_trainer
	model-index:
	- name: OLmo-chebl_domain_adaption
	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-chebl_domain_adaption

	This model is a fine-tuned version of [allenai/OLMo-7B-hf](https://huggingface.co/allenai/OLMo-7B-hf) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.3516
	- Model Preparation Time: 0.0043

	## 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: 0.0001
	- train_batch_size: 4
	- eval_batch_size: 4
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 8
	- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 0.05
	- num_epochs: 2

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:----------------------:\|
	\| 6.3473 \| 0.0710 \| 50 \| 6.2448 \| 0.0043 \|
	\| 4.8393 \| 0.1419 \| 100 \| 4.6801 \| 0.0043 \|
	\| 3.2501 \| 0.2129 \| 150 \| 3.2598 \| 0.0043 \|
	\| 2.7432 \| 0.2839 \| 200 \| 2.8011 \| 0.0043 \|
	\| 2.4688 \| 0.3549 \| 250 \| 2.4233 \| 0.0043 \|
	\| 2.2724 \| 0.4258 \| 300 \| 2.2580 \| 0.0043 \|
	\| 2.1390 \| 0.4968 \| 350 \| 2.1196 \| 0.0043 \|
	\| 1.9718 \| 0.5678 \| 400 \| 2.0202 \| 0.0043 \|
	\| 1.8871 \| 0.6388 \| 450 \| 1.9191 \| 0.0043 \|
	\| 1.8967 \| 0.7097 \| 500 \| 1.8318 \| 0.0043 \|
	\| 1.7147 \| 0.7807 \| 550 \| 1.7578 \| 0.0043 \|
	\| 1.7526 \| 0.8517 \| 600 \| 1.7118 \| 0.0043 \|
	\| 1.6391 \| 0.9226 \| 650 \| 1.6556 \| 0.0043 \|
	\| 1.5693 \| 0.9936 \| 700 \| 1.6024 \| 0.0043 \|
	\| 1.4872 \| 1.0639 \| 750 \| 1.5712 \| 0.0043 \|
	\| 1.4543 \| 1.1348 \| 800 \| 1.5263 \| 0.0043 \|
	\| 1.4862 \| 1.2058 \| 850 \| 1.4896 \| 0.0043 \|
	\| 1.3994 \| 1.2768 \| 900 \| 1.4550 \| 0.0043 \|
	\| 1.2903 \| 1.3478 \| 950 \| 1.4253 \| 0.0043 \|
	\| 1.2909 \| 1.4187 \| 1000 \| 1.4001 \| 0.0043 \|
	\| 1.2847 \| 1.4897 \| 1050 \| 1.3812 \| 0.0043 \|
	\| 1.2536 \| 1.5607 \| 1100 \| 1.3681 \| 0.0043 \|
	\| 1.2414 \| 1.6317 \| 1150 \| 1.3592 \| 0.0043 \|
	\| 1.3003 \| 1.7026 \| 1200 \| 1.3541 \| 0.0043 \|
	\| 1.3042 \| 1.7736 \| 1250 \| 1.3523 \| 0.0043 \|
	\| 1.3176 \| 1.8446 \| 1300 \| 1.3517 \| 0.0043 \|
	\| 1.2306 \| 1.9155 \| 1350 \| 1.3516 \| 0.0043 \|
	\| 1.2716 \| 1.9865 \| 1400 \| 1.3515 \| 0.0043 \|
	\| 1.3064 \| 2.0 \| 1410 \| 1.3516 \| 0.0043 \|


	### Framework versions

	- Transformers 5.9.0
	- Pytorch 2.8.0+cu128
	- Datasets 4.8.5
	- Tokenizers 0.22.2