Instructions to use CharlesLi/OpenELM-1_1B-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CharlesLi/OpenELM-1_1B-DPO with Transformers:

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

pipe = pipeline("text-generation", model="CharlesLi/OpenELM-1_1B-DPO", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("CharlesLi/OpenELM-1_1B-DPO", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CharlesLi/OpenELM-1_1B-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CharlesLi/OpenELM-1_1B-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CharlesLi/OpenELM-1_1B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CharlesLi/OpenELM-1_1B-DPO

SGLang

How to use CharlesLi/OpenELM-1_1B-DPO 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 "CharlesLi/OpenELM-1_1B-DPO" \
    --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": "CharlesLi/OpenELM-1_1B-DPO",
		"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 "CharlesLi/OpenELM-1_1B-DPO" \
        --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": "CharlesLi/OpenELM-1_1B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CharlesLi/OpenELM-1_1B-DPO with Docker Model Runner:
```
docker model run hf.co/CharlesLi/OpenELM-1_1B-DPO
```

OpenELM-1_1B-DPO

This model is a fine-tuned version of data/OpenELM-1_1B-SFT on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 1.0009
Rewards/chosen: -14.5625
Rewards/rejected: -17.125
Rewards/accuracies: 0.7188
Rewards/margins: 2.6094
Logps/rejected: -632.0
Logps/chosen: -612.0
Logits/rejected: -13.0
Logits/chosen: -13.0

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: 5e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 32
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.7494	1.0	1911	0.7879	-11.6875	-12.8125	0.6797	1.1797	-548.0	-552.0	-12.8125	-12.5625
0.0999	2.0	3822	0.8019	-14.125	-15.9375	0.6992	1.8125	-608.0	-604.0	-13.0625	-13.0
0.011	3.0	5733	1.0009	-14.5625	-17.125	0.7188	2.6094	-632.0	-612.0	-13.0	-13.0

Framework versions

Transformers 4.44.2
Pytorch 2.3.0
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 5

Safetensors

Model size

1B params

Tensor type

BF16

CharlesLi
/

OpenELM-1_1B-DPO

OpenELM-1_1B-DPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train CharlesLi/OpenELM-1_1B-DPO