Instructions to use W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539 with Transformers:

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

pipe = pipeline("text-generation", model="W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539")
model = AutoModelForCausalLM.from_pretrained("W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539

SGLang

How to use W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539 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 "W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539" \
    --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": "W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539",
		"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 "W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539" \
        --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": "W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539 with Docker Model Runner:
```
docker model run hf.co/W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539
```

llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539

This model is a fine-tuned version of llama-3-8b-base-sft-hh-harmless-4xh200-batch-64 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

Loss: 0.8203
Beta Dpo/beta: 0.1705
Beta Dpo/loss Margin Mean: 16.6192
Beta Dpo/beta Margin Mean: 3.5151
Beta Dpo/beta Margin Std: 4.7567
Beta Dpo/beta Margin Grad Mean: -0.3392
Beta Dpo/beta Margin Grad Std: 0.2229
Beta Dpo/gap Mean: 16.4768
Beta Dpo/gap Std: 28.4131
Beta Dpo/beta Used Raw: 0.1085
Beta Dpo/beta Used: 0.1705
Beta Dpo/mask Keep Frac: 1.0
Logits/chosen: 0.5021
Logits/rejected: 0.4487

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: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
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_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Beta Dpo/beta	Beta Dpo/loss Margin Mean	Beta Dpo/beta Margin Mean	Beta Dpo/beta Margin Std	Beta Dpo/beta Margin Grad Mean	Beta Dpo/beta Margin Grad Std	Beta Dpo/gap Mean	Beta Dpo/gap Std	Beta Dpo/beta Used Raw	Beta Dpo/beta Used	Beta Dpo/mask Keep Frac	Logits/chosen	Logits/rejected
1.3014	0.1512	100	0.6391	0.1183	1.3224	0.1789	0.4749	-0.4595	0.1057	1.0180	3.3360	0.1183	0.1183	1.0	0.2572	0.2207
0.9318	0.3023	200	0.5939	0.0752	9.0802	0.8670	1.1566	-0.3975	0.1333	10.1709	15.2637	0.0346	0.0752	1.0	0.4250	0.3786
1.1289	0.4535	300	0.6938	0.1151	14.7143	2.1435	2.8181	-0.3684	0.1767	15.7264	23.2621	0.0393	0.1151	1.0	0.5036	0.4522
1.3777	0.6047	400	0.6486	0.0698	13.2713	1.2326	1.6702	-0.4066	0.1228	15.5137	23.8374	-0.0345	0.0698	1.0	0.4392	0.3876
1.1911	0.7559	500	0.6888	0.0936	16.0572	1.9620	2.5727	-0.3866	0.1471	17.9087	28.7161	-0.0111	0.0936	1.0	0.5027	0.4490
1.0347	0.9070	600	0.8203	0.1705	16.6192	3.5151	4.7567	-0.3392	0.2229	16.4768	28.4131	0.1085	0.1705	1.0	0.5021	0.4487

Framework versions

Transformers 4.51.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.21.4

Downloads last month: 14

Safetensors

Model size

8B params

Tensor type

F32

W-61
/

llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539

llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train W-61/llama-3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260417-233539