Instructions to use W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936 with Transformers:

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

pipe = pipeline("text-generation", model="W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936")
model = AutoModelForCausalLM.from_pretrained("W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936", 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/llama3-8b-base-new-method-s_star0.6-20260425-180936 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936"
# 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/llama3-8b-base-new-method-s_star0.6-20260425-180936",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936

SGLang

How to use W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936 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/llama3-8b-base-new-method-s_star0.6-20260425-180936" \
    --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/llama3-8b-base-new-method-s_star0.6-20260425-180936",
		"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/llama3-8b-base-new-method-s_star0.6-20260425-180936" \
        --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/llama3-8b-base-new-method-s_star0.6-20260425-180936",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936 with Docker Model Runner:
```
docker model run hf.co/W-61/llama3-8b-base-new-method-s_star0.6-20260425-180936
```

llama3-8b-base-new-method-s_star0.6-20260425-180936 / README.md

W-61

End of training

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metadata

library_name: transformers
base_model: W-61/llama-3-8b-base-sft-ultrachat-8xh200
tags:
  - alignment-handbook
  - new-dpo
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: llama3-8b-base-new-method-s_star0.6-20260425-180936
    results: []

llama3-8b-base-new-method-s_star0.6-20260425-180936

This model is a fine-tuned version of W-61/llama-3-8b-base-sft-ultrachat-8xh200 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.5352
Fcm Dpo/beta: 0.0110
Margin Dpo/margin Mean: 54.2375
Margin Dpo/margin Std: 83.0790
Logps/chosen: -383.9891
Logps/rejected: -417.3312
Logps/ref Chosen: -287.8268
Logps/ref Rejected: -266.9314
Logits/chosen: -0.8407
Logits/rejected: -0.8346

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: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 16
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	Fcm Dpo/beta	Margin Dpo/margin Mean	Margin Dpo/margin Std	Logps/chosen	Logps/rejected	Logps/ref Chosen	Logps/ref Rejected	Logits/chosen	Logits/rejected
4.6087	0.4188	200	0.5497	0.0189	29.5673	48.3802	-320.8928	-329.5647	-287.8268	-266.9314	-0.8679	-0.8610
4.3167	0.8377	400	0.5352	0.0110	54.2375	83.0790	-383.9891	-417.3312	-287.8268	-266.9314	-0.8407	-0.8346

Framework versions

Transformers 4.51.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.21.4