Instructions to use jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732 with Transformers:

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

pipe = pipeline("text-generation", model="jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732")
model = AutoModelForCausalLM.from_pretrained("jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732", 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 jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732

SGLang

How to use jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732 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 "jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732" \
    --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": "jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732",
		"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 "jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732" \
        --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": "jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732 with Docker Model Runner:
```
docker model run hf.co/jackf857/qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732
```

qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732 / README.md

jackf857

Upload validated Qwen3 HH helpful Beta-DPO checkpoint for qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732

1cdcd2c verified 4 months ago

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metadata

library_name: transformers
base_model: jackf857/qwen3-8b-base-sft-hh-helpful-4xh200-batch-64-20260417-214452
tags:
  - alignment-handbook
  - beta-dpo
  - generated_from_trainer
datasets:
  - Anthropic/hh-rlhf
model-index:
  - name: qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732
    results: []

qwen3-8b-base-beta-dpo-hh-helpful-4xh200-batch-64-20260424-013732

This model is a fine-tuned version of jackf857/qwen3-8b-base-sft-hh-helpful-4xh200-batch-64-20260417-214452 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:

Loss: 0.6505
Beta Dpo/gap Mean: 25.7183
Beta Dpo/gap Std: 26.1829
Beta Dpo/beta Used Raw: -0.2537
Beta Dpo/beta Used: 0.0212
Beta Dpo/mask Keep Frac: 1.0
Logits/chosen: -1.6123
Logits/rejected: -1.2796

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/gap Mean	Beta Dpo/gap Std	Beta Dpo/beta Used Raw	Beta Dpo/beta Used	Beta Dpo/mask Keep Frac	Logits/chosen	Logits/rejected
1.1246	0.1468	100	0.6499	4.6173	6.2353	-0.0000	0.0293	1.0	-0.1879	0.0487
0.313	0.2937	200	0.6346	12.9809	14.5981	-0.0999	0.0229	1.0	-1.1708	-0.8922
0.6858	0.4405	300	0.6556	19.2389	20.8328	-0.2038	0.0146	1.0	-1.4133	-1.1126
0.7246	0.5874	400	0.6605	25.6196	25.0584	-0.3654	0.0094	1.0	-1.5698	-1.2495
0.9705	0.7342	500	0.6340	24.0392	26.5263	-0.1783	0.0322	1.0	-1.7312	-1.4078
0.8177	0.8811	600	0.6505	25.7183	26.1829	-0.2537	0.0212	1.0	-1.6123	-1.2796

Framework versions

Transformers 4.51.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.21.4