Instructions to use imelnyk/slm-2-dpo-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use imelnyk/slm-2-dpo-full with Transformers:

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

pipe = pipeline("text-generation", model="imelnyk/slm-2-dpo-full", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("imelnyk/slm-2-dpo-full", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use imelnyk/slm-2-dpo-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "imelnyk/slm-2-dpo-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "imelnyk/slm-2-dpo-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/imelnyk/slm-2-dpo-full

SGLang

How to use imelnyk/slm-2-dpo-full 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 "imelnyk/slm-2-dpo-full" \
    --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": "imelnyk/slm-2-dpo-full",
		"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 "imelnyk/slm-2-dpo-full" \
        --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": "imelnyk/slm-2-dpo-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use imelnyk/slm-2-dpo-full with Docker Model Runner:
```
docker model run hf.co/imelnyk/slm-2-dpo-full
```

slm-2-dpo-full

This model is a fine-tuned version of stabilityai/stablelm-2-zephyr-1_6b on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 31.9894
Rewards/chosen: 0.0244
Rewards/rejected: 0.0188
Rewards/accuracies: 0.5234
Rewards/margins: 0.0057
Logps/rejected: -2491.7576
Logps/chosen: -2806.6704
Logits/rejected: -1.6239
Logits/chosen: -1.6845

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-07
train_batch_size: 5
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 80
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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
16.8403	0.13	100	19.5118	0.0256	0.0173	0.5273	0.0082	-2491.9011	-2806.5552	-1.6068	-1.6730
28.1241	0.26	200	32.5175	0.0085	-0.0039	0.5234	0.0124	-2494.0195	-2808.2581	-1.6183	-1.6812
84.7591	0.39	300	47.8043	0.0297	0.0136	0.5391	0.0161	-2492.2703	-2806.1406	-1.5968	-1.6601
40.7835	0.52	400	30.6722	0.0168	-0.0029	0.5547	0.0197	-2493.9204	-2807.4263	-1.6288	-1.6917
36.2204	0.65	500	31.2202	0.0303	0.0209	0.5352	0.0095	-2491.5447	-2806.0762	-1.6236	-1.6843
99.7738	0.78	600	33.7403	0.0476	0.0372	0.5391	0.0104	-2489.9089	-2804.3484	-1.6222	-1.6827
41.8506	0.92	700	32.9133	0.0301	0.0195	0.5547	0.0106	-2491.6851	-2806.1006	-1.6211	-1.6823

Framework versions

Transformers 4.36.2
Pytorch 2.2.0+cu118
Datasets 2.14.6
Tokenizers 0.15.2

Downloads last month: 9

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for imelnyk/slm-2-dpo-full

Base model

stabilityai/stablelm-2-zephyr-1_6b

Finetuned

(8)

this model

imelnyk
/

slm-2-dpo-full

slm-2-dpo-full

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for imelnyk/slm-2-dpo-full

Dataset used to train imelnyk/slm-2-dpo-full