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

Libraries

How to use W-61/llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200 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-epsilon-dpo-hh-harmless-8xh200")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("W-61/llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200")
model = AutoModelForCausalLM.from_pretrained("W-61/llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200", 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=256)
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-epsilon-dpo-hh-harmless-8xh200 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-epsilon-dpo-hh-harmless-8xh200"
# 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-epsilon-dpo-hh-harmless-8xh200",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/W-61/llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200

SGLang

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

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

llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200-20260411-001027

This model is a fine-tuned version of W-61/llama-3-8b-base-sft-hh-harmless-8xh200 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

Loss: 0.6288
Rewards/chosen: -0.4104
Rewards/rejected: -0.6458
Rewards/accuracies: 0.6691
Rewards/margins: 0.2353
Logps/chosen: -157.0541
Logps/rejected: -214.8666
Logps/ref Chosen: -73.9652
Logps/ref Rejected: -83.1798
Logits/chosen: 0.7149
Logits/rejected: 0.6886
Kl/p Epsilon Steps: 0.6448
Kl/n Epsilon Steps: 0.3534

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: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 128
total_eval_batch_size: 128
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	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/chosen	Logps/rejected	Logps/ref Chosen	Logps/ref Rejected	Logits/chosen	Logits/rejected	Kl/p Epsilon Steps	Kl/n Epsilon Steps
0.6773	0.3030	100	0.6737	-0.1469	-0.1956	0.6085	0.0487	-91.4063	-106.5723	-73.9652	-83.1798	0.4327	0.4096	0.5960	0.4030
0.6222	0.6061	200	0.6386	-0.3851	-0.5821	0.6572	0.1970	-131.2605	-170.4228	-73.9652	-83.1798	0.6604	0.6363	0.6351	0.3640
0.6236	0.9091	300	0.6288	-0.4104	-0.6458	0.6691	0.2353	-157.0541	-214.8666	-73.9652	-83.1798	0.7149	0.6886	0.6448	0.3534

Framework versions

Transformers 4.51.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.21.4

Downloads last month: 15

Safetensors

Model size

8B params

Tensor type

F32

Model tree for W-61/llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

W-61/llama-3-8b-base-sft-hh-harmless-8xh200

Finetuned

(9)

this model

W-61
/

llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200

llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200-20260411-001027

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for W-61/llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200

Dataset used to train W-61/llama-3-8b-base-epsilon-dpo-hh-harmless-8xh200