Instructions to use W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315 with Transformers:

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

pipe = pipeline("text-generation", model="W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315")
model = AutoModelForCausalLM.from_pretrained("W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315", 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/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315"
# 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/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315

SGLang

How to use W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315 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/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315" \
    --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/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315",
		"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/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315" \
        --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/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315 with Docker Model Runner:
```
docker model run hf.co/W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315
```

W-61 commited on Apr 24

Commit

8b543b5

verified ·

1 Parent(s): d291646

Upload validated Qwen3 ultrafeedback Margin-DPO checkpoint for qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315

Browse files

This view is limited to 50 files because it contains too many changes. See raw diff

Files changed (50) hide show

.gitattributes +1 -0
README.md +76 -0
added_tokens.json +28 -0
all_results.json +22 -0
config.json +30 -0
eval_results.json +16 -0
generation_config.json +6 -0
margin_logs/margins.jsonl +0 -0
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 *.zip filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,76 @@

+---
+library_name: transformers
+base_model: W-61/qwen3-8b-base-sft-ultrachat-4xh200-batch-128
+tags:
+- alignment-handbook
+- margin-dpo
+- generated_from_trainer
+datasets:
+- HuggingFaceH4/ultrafeedback_binarized
+model-index:
+- name: qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315
+  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. -->
+# qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315
+This model is a fine-tuned version of [W-61/qwen3-8b-base-sft-ultrachat-4xh200-batch-128](https://huggingface.co/W-61/qwen3-8b-base-sft-ultrachat-4xh200-batch-128) on the HuggingFaceH4/ultrafeedback_binarized dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5602
+- Margin Dpo/margin Mean: 48.7131
+- Margin Dpo/margin Std: 68.1546
+- Logps/chosen: -316.0414
+- Logps/rejected: -345.1451
+- Logps/ref Chosen: -281.4589
+- Logps/ref Rejected: -261.8495
+- Logits/chosen: 1.1933
+- Logits/rejected: 1.2367
+## 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 | Margin Dpo/margin Mean | Margin Dpo/margin Std | Logps/chosen | Logps/rejected | Logps/ref Chosen | Logps/ref Rejected | Logits/chosen | Logits/rejected |
+|:-------------:|:------:|:----:|:---------------:|:----------------------:|:---------------------:|:------------:|:--------------:|:----------------:|:------------------:|:-------------:|:---------------:|
+| 4.9383        | 0.4188 | 200  | 0.5970          | 28.2584                | 39.0244               | -287.0227    | -295.6718      | -281.4589        | -261.8495          | 1.4300        | 1.4697          |
+| 4.2739        | 0.8377 | 400  | 0.5602          | 48.7131                | 68.1546               | -316.0414    | -345.1451      | -281.4589        | -261.8495          | 1.1933        | 1.2367          |
+### Framework versions
+- Transformers 4.51.0
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.21.4

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+    "eval_logits/chosen": 1.1346925497055054,
+    "eval_logits/rejected": 1.1706666946411133,
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+    "eval_logps/ref_rejected": -261.84954833984375,
+    "eval_logps/rejected": -342.32696533203125,
+    "eval_loss": 0.5572407245635986,
+    "eval_margin_dpo/margin_mean": 48.91001510620117,
+    "eval_margin_dpo/margin_std": 68.51956176757812,
+    "eval_runtime": 92.7227,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 21.57,
+    "eval_steps_per_second": 1.348,
+    "total_flos": 0.0,
+    "train_loss": 4.779813265150698,
+    "train_runtime": 7822.2821,
+    "train_samples": 61135,
+    "train_samples_per_second": 7.815,
+    "train_steps_per_second": 0.061
+}

config.json ADDED Viewed

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+  ],
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+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+    "eval_logps/rejected": -342.32696533203125,
+    "eval_loss": 0.5572407245635986,
+    "eval_margin_dpo/margin_mean": 48.91001510620117,
+    "eval_margin_dpo/margin_std": 68.51956176757812,
+    "eval_runtime": 92.7227,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 21.57,
+    "eval_steps_per_second": 1.348
+}

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+  "transformers_version": "4.51.0"
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