Instructions to use jackf857/qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64 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-harmless-4xh200-batch-64 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-harmless-4xh200-batch-64")
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-harmless-4xh200-batch-64")
model = AutoModelForCausalLM.from_pretrained("jackf857/qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64", 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-harmless-4xh200-batch-64 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-harmless-4xh200-batch-64"
# 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-harmless-4xh200-batch-64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260418-012645

This model is a fine-tuned version of /scratch/qu.yang1/dynamic-dpo-v4/outputs/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: 0.6518
Beta Dpo/beta: 0.1628
Beta Dpo/loss Margin Mean: 8.6336
Beta Dpo/beta Margin Mean: 1.6614
Beta Dpo/beta Margin Std: 2.5181
Beta Dpo/beta Margin Grad Mean: -0.3428
Beta Dpo/beta Margin Grad Std: 0.2482
Beta Dpo/gap Mean: 7.7476
Beta Dpo/gap Std: 14.8995
Beta Dpo/beta Used Raw: 0.1532
Beta Dpo/beta Used: 0.1628
Beta Dpo/mask Keep Frac: 1.0
Logits/chosen: 1.2071
Logits/rejected: 1.1403

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
1.3654	0.1512	100	0.6641	0.1110	0.6060	0.0717	0.2130	-0.4824	0.0521	0.4233	1.6301	0.1110	0.1110	1.0	1.3014	1.2543
0.9954	0.3023	200	0.6120	0.0999	4.1607	0.5099	0.9505	-0.4145	0.1620	4.2136	9.3730	0.0968	0.0999	1.0	0.9179	0.8638
1.2007	0.4535	300	0.6115	0.1346	6.6135	1.0588	1.7069	-0.3695	0.2169	6.1583	12.2942	0.1273	0.1346	1.0	1.2327	1.1646
1.3823	0.6047	400	0.5951	0.1019	6.9792	0.8735	1.3552	-0.3881	0.1738	7.2064	12.6529	0.0864	0.1019	1.0	1.2965	1.2260
1.2301	0.7559	500	0.6311	0.1290	8.6766	1.3653	2.0186	-0.3625	0.2097	8.4351	15.6317	0.1145	0.1290	1.0	1.2194	1.1512
1.0426	0.9070	600	0.6518	0.1628	8.6336	1.6614	2.5181	-0.3428	0.2482	7.7476	14.8995	0.1532	0.1628	1.0	1.2071	1.1403

Framework versions

Transformers 4.51.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.21.4

Downloads last month: 9

Safetensors

Model size

8B params

Tensor type

F32

jackf857
/

qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64

qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64-20260418-012645

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train jackf857/qwen3-8b-base-beta-dpo-hh-harmless-4xh200-batch-64