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

Libraries

How to use reaperdoesntknow/DualMind_Methodolgy with Transformers:

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

pipe = pipeline("text-generation", model="reaperdoesntknow/DualMind_Methodolgy")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("reaperdoesntknow/DualMind_Methodolgy", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use reaperdoesntknow/DualMind_Methodolgy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/DualMind_Methodolgy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/DualMind_Methodolgy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/DualMind_Methodolgy

SGLang

How to use reaperdoesntknow/DualMind_Methodolgy 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_Methodolgy" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/DualMind_Methodolgy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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_Methodolgy" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/DualMind_Methodolgy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

reaperdoesntknow commited on Aug 3

Commit

32e5c8c

verified ·

1 Parent(s): 2f3aa73

OPSEC: minimize author name (remove surname)

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -14,7 +14,7 @@ tags:
 ---
 # From Three Teachers to Dual Cognition: Topology-Aware Multi-Teacher Distillation and Role-Conditioned Self-Critique at 1.7B Scale
-**Roy S. Colca**
 Convergent Intelligence LLC: Research Division
 **Abstract.** We present a four-stage pipeline for producing small language models (1.7B parameters) that exhibit self-critiquing dual-cognition reasoning from a 30B-parameter teacher. The pipeline chains: (1) proof-weighted knowledge distillation from three teacher variants (Instruct, Thinking, Coder) of Qwen3-30B-A3B, each producing distinct capability profiles in the student; (2) Topological Knowledge Distillation (TKD), which uses the Discrepancy Calculus (DISC) framework to decompose the teacher's output distribution into smooth, jump, and singular-continuous components via bounded variation theory, allocating training capacity to structural boundaries that standard KD smears across; (3) multi-teacher ghost imprinting, where sequential distillation from different teachers creates residual discrepancy fields in weight space that produce emergent capabilities absent from any individual teacher; and (4) DualMind, a role-conditioned generation scheme that collapses multi-architecture dialectical reasoning into a single model via `<explore>`, `<examine>`, and `<response>` tokens. Trained on H100 at BF16 precision, the resulting models demonstrate dual-cognition reasoning — free derivation followed by adversarial self-critique followed by clean synthesis — at a parameter count where such behavior is not typically observed. We release 43 models (12,000+ downloads), training code, and this methodology under Apache 2.0.
@@ -226,8 +226,8 @@ All models, training code, and this methodology are released under Apache 2.0.
 ## References
 - Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network.
-- Colca, R. S. (2026). Structure Over Scale. DOI: 10.57967/hf/8165.
-- Colca, R. S. (2025-2026). Discrepancy Calculus (DISC): A Measure-Theoretic Framework for Singularities. Convergent Intelligence LLC.
 - Ambrosio, L., Fusco, N., & Pallara, D. (2000). Functions of Bounded Variation and Free Discontinuity Problems. Oxford.
 ---

 ---
 # From Three Teachers to Dual Cognition: Topology-Aware Multi-Teacher Distillation and Role-Conditioned Self-Critique at 1.7B Scale
+**Roy C**
 Convergent Intelligence LLC: Research Division
 **Abstract.** We present a four-stage pipeline for producing small language models (1.7B parameters) that exhibit self-critiquing dual-cognition reasoning from a 30B-parameter teacher. The pipeline chains: (1) proof-weighted knowledge distillation from three teacher variants (Instruct, Thinking, Coder) of Qwen3-30B-A3B, each producing distinct capability profiles in the student; (2) Topological Knowledge Distillation (TKD), which uses the Discrepancy Calculus (DISC) framework to decompose the teacher's output distribution into smooth, jump, and singular-continuous components via bounded variation theory, allocating training capacity to structural boundaries that standard KD smears across; (3) multi-teacher ghost imprinting, where sequential distillation from different teachers creates residual discrepancy fields in weight space that produce emergent capabilities absent from any individual teacher; and (4) DualMind, a role-conditioned generation scheme that collapses multi-architecture dialectical reasoning into a single model via `<explore>`, `<examine>`, and `<response>` tokens. Trained on H100 at BF16 precision, the resulting models demonstrate dual-cognition reasoning — free derivation followed by adversarial self-critique followed by clean synthesis — at a parameter count where such behavior is not typically observed. We release 43 models (12,000+ downloads), training code, and this methodology under Apache 2.0.
 ## References
 - Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network.
+- CIx, R. S. (2026). Structure Over Scale. DOI: 10.57967/hf/8165.
+- CIx, R. S. (2025-2026). Discrepancy Calculus (DISC): A Measure-Theoretic Framework for Singularities. Convergent Intelligence LLC.
 - Ambrosio, L., Fusco, N., & Pallara, D. (2000). Functions of Bounded Variation and Free Discontinuity Problems. Oxford.
 ---