Instructions to use QizhiPei/DiffGen-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QizhiPei/DiffGen-8B with Transformers:

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

pipe = pipeline("text-generation", model="QizhiPei/DiffGen-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QizhiPei/DiffGen-8B")
model = AutoModelForCausalLM.from_pretrained("QizhiPei/DiffGen-8B")
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

vLLM

How to use QizhiPei/DiffGen-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QizhiPei/DiffGen-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QizhiPei/DiffGen-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QizhiPei/DiffGen-8B

SGLang

How to use QizhiPei/DiffGen-8B 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 "QizhiPei/DiffGen-8B" \
    --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": "QizhiPei/DiffGen-8B",
		"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 "QizhiPei/DiffGen-8B" \
        --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": "QizhiPei/DiffGen-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QizhiPei/DiffGen-8B with Docker Model Runner:
```
docker model run hf.co/QizhiPei/DiffGen-8B
```

Improve model card: Update metadata and enrich content for DiffGen-8B

by nielsr HF Staff - opened Sep 26, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-10

Files changed (1) hide show

README.md +11 -10

README.md CHANGED Viewed

@@ -1,38 +1,39 @@
 ---
-library_name: transformers
-license: other
 base_model: Qwen/Qwen3-8B-Base
 tags:
 - llama-factory
 - full
 - generated_from_trainer
 model-index:
 - name: DiffGen-8B
   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. -->
 Paper: [ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning](https://arxiv.org/abs/2509.21070)
 Code: https://github.com/QizhiPei/ScaleDiff
 # DiffGen-8B
-This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) on the difficult problems from [AM-Qwen3-Distilled](https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled) dataset.
 ## Model description
-More information needed
 ## Intended uses & limitations
-More information needed
 ## Training and evaluation data
-More information needed
 ## Training procedure
@@ -61,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.52.0.dev0
 - Pytorch 2.6.0+cu124
 - Datasets 2.21.0
-- Tokenizers 0.21.1

 ---
 base_model: Qwen/Qwen3-8B-Base
+library_name: transformers
+license: apache-2.0
 tags:
 - llama-factory
 - full
 - generated_from_trainer
+- math-reasoning
+pipeline_tag: text-generation
 model-index:
 - name: DiffGen-8B
   results: []
 ---
 Paper: [ScaleDiff: Scaling Difficult Problems for Advanced Mathematical Reasoning](https://arxiv.org/abs/2509.21070)
 Code: https://github.com/QizhiPei/ScaleDiff
 # DiffGen-8B
+This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base).
 ## Model description
+DiffGen-8B is a specialized difficult problem generator developed as part of the ScaleDiff pipeline, an approach designed to scale the creation of challenging mathematical problems for advanced mathematical reasoning. The model is trained on a filtered dataset of difficult problems, enabling it to efficiently produce a vast number of new, complex mathematical problems. This process eliminates the need for complex, per-instance prompting and its associated high API costs, addressing the scarcity of high-quality, difficult training data for Large Reasoning Models (LRMs).
 ## Intended uses & limitations
+**Intended Uses**: DiffGen-8B is primarily intended for generating large-scale datasets of challenging mathematical problems. These generated problems are then used to augment training data for Large Reasoning Models (LRMs), thereby enhancing their mathematical reasoning capabilities. It serves as a crucial component in pipelines focused on improving LRM performance on difficult benchmarks by providing a continuous supply of intricate reasoning challenges.
+**Limitations**: While DiffGen-8B excels at generating difficult problems, its primary scope is mathematical problem generation. The quality and relevance of the generated problems are further ensured through subsequent solution distillation and filtering steps within the broader ScaleDiff pipeline. Its performance may not be optimized for other general text generation tasks.
 ## Training and evaluation data
+DiffGen-8B is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base). It was trained on a subset of difficult problems selected from the [AM-Qwen3-Distilled](https://huggingface.co/datasets/a-m-team/AM-Qwen3-Distilled) dataset. This selection was performed efficiently using [AdaptThink](https://huggingface.co/THU-KEG/AdaptThink-7B-delta0.05), an adaptive thinking model that perceives problem difficulty with only a single forward pass, eliminating the need for solutions during selection. The problems generated by DiffGen-8B contribute to the creation of the [ScaleDiff-Math](https://huggingface.co/datasets/QizhiPei/ScaleDiff-Math) dataset.
 ## Training procedure
 - Transformers 4.52.0.dev0
 - Pytorch 2.6.0+cu124
 - Datasets 2.21.0
+- Tokenizers 0.21.1