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

Libraries

How to use reaperdoesntknow/DualMind with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/DualMind"
# 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/DualMind",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/DualMind

SGLang

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

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

reaperdoesntknow commited on 10 days ago

Commit

c8d8dff

verified ·

1 Parent(s): 1fa2371

OPSEC: minimize author name (remove surname)

Browse files

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README.md +3 -3

README.md CHANGED Viewed

@@ -145,7 +145,7 @@ $$Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varep
 quantifies the mismatch between what the model generates (integration) and what it should generate (differentiation). The `<explore>` phase increases discrepancy energy freely; `<examine>` applies the Adaptive Discrepancy Derivative (ADD, Ch. 14) to detect drift; `<response>` minimizes residual discrepancy into a clean output. The three phases implement the BV decomposition operationally: smooth reasoning, jump corrections at error boundaries, and Cantor-type refinement of subtle drift.
-Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division).
 ## Related Models
@@ -164,9 +164,9 @@ Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org
 ## Citation
 ```bibtex
-@misc{colca2026dualmind,
   title={DualMind: Dual-Mental-Modality Reasoning via Role-Conditioned Self-Critique},
-  author={Colca, Roy S.},
   year={2026},
   publisher={HuggingFace},
   url={https://huggingface.co/reaperdoesntknow/DualMind},

 quantifies the mismatch between what the model generates (integration) and what it should generate (differentiation). The `<explore>` phase increases discrepancy energy freely; `<examine>` applies the Adaptive Discrepancy Derivative (ADD, Ch. 14) to detect drift; `<response>` minimizes residual discrepancy into a clean output. The three phases implement the BV decomposition operationally: smooth reasoning, jump corrections at error boundaries, and Cantor-type refinement of subtle drift.
+Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (CIx, 2026; Convergent Intelligence LLC: Research Division).
 ## Related Models
 ## Citation
 ```bibtex
+@misc{cix2026dualmind,
   title={DualMind: Dual-Mental-Modality Reasoning via Role-Conditioned Self-Critique},
+  author={Convergent Intelligence},
   year={2026},
   publisher={HuggingFace},
   url={https://huggingface.co/reaperdoesntknow/DualMind},