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)

# pip install -U transformers accelerate
# 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=256)
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

File size: 2,786 Bytes

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---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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](https://huggingface.co/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