Instructions to use dustarrr/reasoning-rob with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dustarrr/reasoning-rob with Transformers:

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

pipe = pipeline("text-generation", model="dustarrr/reasoning-rob")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dustarrr/reasoning-rob")
model = AutoModelForCausalLM.from_pretrained("dustarrr/reasoning-rob")
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 dustarrr/reasoning-rob with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dustarrr/reasoning-rob

SGLang

How to use dustarrr/reasoning-rob 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 "dustarrr/reasoning-rob" \
    --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": "dustarrr/reasoning-rob",
		"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 "dustarrr/reasoning-rob" \
        --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": "dustarrr/reasoning-rob",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dustarrr/reasoning-rob with Docker Model Runner:
```
docker model run hf.co/dustarrr/reasoning-rob
```

reasoning-rob / README.md

dustarrr

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	---
	base_model: Qwen/Qwen2.5-1.5B
	tags:
	- reasoning
	- chain-of-thought
	- distillation
	- s1
	- limo
	- qlora
	- text-generation
	datasets:
	- simplescaling/s1K
	- GAIR/LIMO
	pipeline_tag: text-generation
	language:
	- en
	license: apache-2.0
	library_name: transformers
	---

	![Reasoning Rob](reasoning-rob.png)

	# Reasoning Rob

	A Qwen2.5-1.5B base model fine-tuned to reason with chain-of-thought traces from s1K + LIMO.

	[![Model Size](https://img.shields.io/badge/params-1.5B-blue)]()
	[![Base Model](https://img.shields.io/badge/base-Qwen2.5--1.5B-green)](https://huggingface.co/Qwen/Qwen2.5-1.5B)
	[![License](https://img.shields.io/badge/license-Apache--2.0-orange)]()
	[![Downloads](https://img.shields.io/huggingface/downloads/dustarrr/reasoning-rob)](https://huggingface.co/dustarrr/reasoning-rob)
	[![Likes](https://img.shields.io/huggingface/likes/dustarrr/reasoning-rob)](https://huggingface.co/dustarrr/reasoning-rob)

	---

	## Summary

	\| \| \|
	\|---\|---\|
	\| Base model \| [`Qwen/Qwen2.5-1.5B`](https://huggingface.co/Qwen/Qwen2.5-1.5B) \|
	\| Parameters \| ~1.5B (LoRA r=16, merged) \|
	\| Context length \| 2048 tokens \|
	\| Training data \| s1K (1,000 traces) + LIMO (817 traces) = ~1,800 CoT samples \|
	\| Method \| s1-style distillation + budget forcing via QLoRA SFT \|
	\| Compute \| Google Colab T4 GPU, ~16 min \|
	\| Special tokens \| `<think>` `</think>` for reasoning trace delimiters \|

	---

	## Evaluation Results

	\| Benchmark \| Reasoning Rob \|
	\|-----------\|--------------:\|
	\| GSM8K (50 samples) \| 10.00% \|

	---

	## Usage

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"dustarrr/reasoning-rob",
	torch_dtype=torch.float16,
	device_map="auto",
	)
	tokenizer = AutoTokenizer.from_pretrained("dustarrr/reasoning-rob")
	model.eval()

	messages = [
	{"role": "system", "content": "You are a helpful assistant that thinks step by step."},
	{"role": "user", "content": "If a train travels 60 km in 1.5 hours, what is its speed?"},
	]
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(text, return_tensors="pt")

	with torch.no_grad():
	outputs = model.generate(**inputs, max_new_tokens=1024, do_sample=False)

	response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
	print(response)
	```

	### Budget Forcing (s1-style)

	Extend the model's thinking phase by injecting `"Wait"` before the `</think>` token
	to force longer reasoning before the final answer. This is the test-time scaling
	trick from the [s1 paper](https://arxiv.org/abs/2501.19393).

	---

	## Training Details

	\| Hyperparameter \| Value \|
	\|---\|---\|
	\| LoRA rank \| 16 \|
	\| LoRA alpha \| 32 \|
	\| LoRA dropout \| 0.05 \|
	\| Learning rate \| 0.0001 \|
	\| LR scheduler \| cosine \|
	\| Warmup ratio \| 0.03 \|
	\| Weight decay \| 0.01 \|
	\| Batch size \| 2 \|
	\| Gradient accumulation \| 8 \|
	\| Max sequence length \| 2048 \|
	\| Epochs \| 1 \|
	\| Quantization \| NF4 (4-bit, double quant) \|
	\| Optimizer \| adamw_torch \|

	---

	## Attribution

	Reasoning Rob is a QLoRA fine-tune of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B)
	(base, not instruct) trained on:

	- [s1K](https://huggingface.co/datasets/simplescaling/s1K) - 1,000 curated reasoning traces
	- [LIMO](https://huggingface.co/datasets/GAIR/LIMO) - 817 "Less Is More" reasoning traces

	Using the [s1](https://arxiv.org/abs/2501.19393) distillation + budget-forcing method
	and [LIMO](https://arxiv.org/abs/2502.03387) "less is more" reasoning transfer approach.

	All credit to:
	- The Qwen Team (Alibaba) for the base model
	- The s1 authors (Stanford) for the training methodology and dataset
	- The LIMO authors (GAIR) for the reasoning dataset

	This model would not exist without their work.

	---

	## Limitations

	- Small model: At 1.5B parameters, Reasoning Rob has limited capacity.
	- Hallucination: The model may still produce incorrect reasoning or fabricate facts.
	- Short context: Max sequence length is 2048 tokens.
	- English only: Training data is predominantly English.

	---

	## License

	Apache 2.0 (inherited from Qwen2.5 base model).

	---

	Generated on 2026-06-23