Instructions to use amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B with Transformers:

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

pipe = pipeline("text-generation", model="amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B")
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("amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B")
model = AutoModelForCausalLM.from_pretrained("amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B", 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 amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B

SGLang

How to use amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B 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 "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B" \
    --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": "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B",
		"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 "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B" \
        --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": "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B with Docker Model Runner:
```
docker model run hf.co/amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B
```

InfoDensity-DeepSeek-R1-Distill-Qwen-7B

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B trained with InfoDensity, a reinforcement-learning reward that favours information-dense reasoning traces. The reward combines an entropy-trajectory quality term with a group-relative length scaling term, and is applied only to traces that reach a correct answer, so the model learns to reach the right answer with markedly less deliberation. Method details are in the paper.

📄 Paper: InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning (EMNLP 2026 Main)
💻 Code: https://github.com/amao0o0/InfoDensity
🧩 Base model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Results

Accuracy (pass@1) and mean generated length averaged over AMC23, AIME24, MATH500 and GPQA-Diamond, as reported in the paper. AES is the Accuracy–Efficiency Score (α=1, β=3, γ=5).

Model	Accuracy	Length	AES
DeepSeek-R1-Distill-Qwen-7B	58.1	8.5k	—
InfoDensity-DeepSeek-R1-Distill-Qwen-7B	72.2	4.5k	+1.20

Evaluation protocol

Greedy decoding, pass@1, generation capped at 16384 tokens, with the prompt

<question>

Please reason step by step, and put your final answer within \boxed{}

The evaluation scripts that produce these numbers are in the GitHub repository.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

question = "What is the smallest positive integer n such that n^2 + n + 41 is not prime?"
messages = [{"role": "user",
             "content": f"{question}\n\nPlease reason step by step, and put your final answer within \\boxed{{}}"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

out = model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_new_tokens=16384)
print(tokenizer.decode(out[0], skip_special_tokens=True))

With vLLM:

from vllm import LLM, SamplingParams

llm = LLM(model="amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B")
print(llm.generate([prompt], SamplingParams(temperature=0, max_tokens=16384))[0].outputs[0].text)

License

Released under mit, inherited from the base model deepseek-ai/DeepSeek-R1-Distill-Qwen-7B.

Citation

@inproceedings{wei2026infodensity,
  title     = {InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning},
  author    = {Wei, Chengwei and Kim, Jung-jae and Zhang, Longyin and Chen, Shengkai and Chen, Nancy F.},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}

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