Instructions to use reaperdoesntknow/DeepReasoning_1R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/DeepReasoning_1R with Transformers:

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

pipe = pipeline("text-generation", model="reaperdoesntknow/DeepReasoning_1R")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DeepReasoning_1R")
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/DeepReasoning_1R", 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 reaperdoesntknow/DeepReasoning_1R with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/DeepReasoning_1R"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/DeepReasoning_1R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/DeepReasoning_1R

SGLang

How to use reaperdoesntknow/DeepReasoning_1R 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 "reaperdoesntknow/DeepReasoning_1R" \
    --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": "reaperdoesntknow/DeepReasoning_1R",
		"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 "reaperdoesntknow/DeepReasoning_1R" \
        --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": "reaperdoesntknow/DeepReasoning_1R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use reaperdoesntknow/DeepReasoning_1R with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/DeepReasoning_1R
```

DeepReasoning_1R / README.md

reaperdoesntknow

keeper: refresh 2026-08-11

04e6e46 verified about 10 hours ago

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	---
	datasets:
	- HumanLLMs/Human-Like-DPO-Dataset
	base_model:
	- Qwen/Qwen2.5-0.5B-Instruct
	tags:
	- convergentintel
	- reasoning
	pipeline_tag: text-generation
	library_name: transformers
	---

	---

	## Convergent Intelligence Portfolio

	Part of the [Standalone Models](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)


	### Related Models

	\| Model \| Downloads \| Format \|
	\|-------\|-----------\|--------\|
	\| [SMOLM2Prover](https://huggingface.co/reaperdoesntknow/SMOLM2Prover) \| 56 \| HF \|
	\| [SMOLM2Prover-GGUF](https://huggingface.co/reaperdoesntknow/SMOLM2Prover-GGUF) \| 150 \| GGUF \|
	\| [SAGI](https://huggingface.co/reaperdoesntknow/SAGI) \| 3 \| HF \|
	\| [S-AGI](https://huggingface.co/reaperdoesntknow/S-AGI) \| 0 \| HF \|

	### Top Models from Our Lab

	\| Model \| Downloads \|
	\|-------\|-----------\|
	\| [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) \| 501 \|
	\| [LFM2.5-1.2B-Distilled-SFT](https://huggingface.co/reaperdoesntknow/LFM2.5-1.2B-Distilled-SFT) \| 342 \|
	\| [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) \| 302 \|
	\| [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) \| 203 \|
	\| [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) \| 194 \|

	Total Portfolio: 41 models \| 2,781 total downloads


	Last updated: 2026-03-28 12:56 UTC

	<!-- CIX-CROSSLINK-START -->

	---

	## From the Convergent Intelligence Portfolio

	[DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c) — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware.

	Top model: [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) — 508 downloads

	Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)

	Convergent Intelligence LLC: Research Division

	<!-- CIX-CROSSLINK-END -->

	## Discrepancy Calculus Foundation

	This model is part of the [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow) portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces.

	DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts:

	- Discrepancy Operator (D): Measures the gap between expected and observed behavior at each training step
	- Jump Sets: Boundaries where model behavior changes discontinuously — these are features, not bugs
	- Ghost Imprinting: Teacher knowledge that transfers to student models through weight-space topology rather than explicit distillation signal

	For the full mathematical treatment, see [Discrepancy Calculus: Foundations and Core Theory](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) (DOI: 10.57967/hf/8194).

	Citation chain: [Structure Over Scale](https://huggingface.co/reaperdoesntknow/Structure-Over-Scale) (DOI: 10.57967/hf/8165) → [Three Teachers to Dual Cognition](https://huggingface.co/reaperdoesntknow/DualMind_Methodolgy) (DOI: 10.57967/hf/8184) → [Discrepancy Calculus](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) (DOI: 10.57967/hf/8194)
	<!-- cix-keeper-ts:2026-08-11T13:15:10Z -->