Instructions to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Ollama
How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DualMind-TKD-Agentic-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

DualMind-TKD-Agentic-1.7B GGUF

GGUF quantizations of reaperdoesntknow/DualMind-TKD-Agentic-1.7B — a 1.7B-parameter Qwen3 causal language model that combines topology-guided mathematical knowledge distillation (Stage 1) with assistant-masked agentic and function-calling specialization (Stage 2).

Quantized by tinyopsec.

Model Details

Attribute	Value
Architecture	Qwen3ForCausalLM
Parameters	~2.03B
Base model	Qwen/Qwen3-1.7B
Teacher model (Stage 1)	Qwen/Qwen3-8B
Training precision	BF16
Context length	40,960 tokens (native)
License	Other (see License note below)
Developer	Convergent Intelligence LLC: Research Division

About the Original Model

DualMind-TKD-Agentic-1.7B is a two-stage derivative of Qwen/Qwen3-1.7B designed to transfer mathematical reasoning behavior while adding structured tool-use capabilities.

Stage 1: Topology-Guided Knowledge Distillation

Student: Qwen/Qwen3-1.7B
Teacher: Qwen/Qwen3-8B
Dataset: 0xZee/dataset-CoT-Advanced-Calculus-268
Training scope: Full-model fine-tuning
Objective: Supervised cross-entropy plus sparse top-k-and-tail teacher distillation
Structural signals: Teacher distribution discrepancy, transition topology, gap-energy diagnostics, and phase-weighted Explore/Examine/Response supervision

This stage places additional learning pressure on derivation, verification, and high-discrepancy reasoning transitions.

Stage 2: Agentic Specialization

Dataset: NousResearch/hermes-function-calling-v1
Training scope: LoRA specialization followed by weight merging
Supervision: Assistant and tool-call outputs only (tool schemas, user messages, and tool results were visible as context but excluded from direct loss)
Forgetting mitigation: Mathematical replay was mixed into Stage 2 to reduce catastrophic forgetting

The files in this repository contain the merged standalone model. A separate PEFT adapter is not required for inference.

Available Quantizations

File	Bits	Size	Use Case
`model_f16.gguf`	16	3.45 GB	Reference, maximum quality
`model_q8_0.gguf`	8	1.83 GB	High quality, fast on capable hardware
`model_q6_k.gguf`	6	1.42 GB	Near-lossless, good balance
`model_q5_k_m.gguf`	5	1.26 GB	Recommended for quality-focused use
`model_q5_k_s.gguf`	5	1.23 GB	Slightly smaller Q5 variant
`model_q4_k_m.gguf`	4	1.11 GB	Best balance of size and quality
`model_q4_k_s.gguf`	4	1.06 GB	Smaller Q4 variant
`model_q3_k_l.gguf`	3	1.0 GB	Low memory, acceptable quality
`model_q3_k_m.gguf`	3	940 MB	Compact, moderate quality loss
`model_q3_k_s.gguf`	3	867 MB	Minimum size Q3
`model_q2_k.gguf`	2	778 MB	Extreme compression, lowest quality

VRAM Requirements

Approximate total VRAM required for inference (weights + context overhead).

Quantization	VRAM (approx.)
F16	~3.8 GB
Q8_0	~2.1 GB
Q6_K	~1.7 GB
Q5_K_M	~1.5 GB
Q4_K_M	~1.3 GB
Q3_K_M	~1.1 GB
Q2_K	~0.9 GB

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf \
  -p "Solve the following problem carefully and show a rigorous derivation.\n\nProblem:\nFind the eigenvalues of [[2,1],[1,2]].\n\nProof:\n" \
  -n 512 --temp 0.0

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=4096)

output = llm(
    "Solve the following problem carefully and show a rigorous derivation.\n\nProblem:\nProve that sqrt(2) is irrational.\n\nProof:\n",
    max_tokens=512,
    temperature=0.0,
)
print(output["choices"][0]["text"])

LM Studio

Download any .gguf file from this repository
Open LM Studio → Load Model → select the file
Use the prompt format below in the chat or playground

Ollama

ollama run hf.co/tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF:Q4_K_M

Function Calling / Tool Use

The model can generate tool calls, but it does not execute external tools by itself. A surrounding runtime must:

Parse the model's tool call.
Execute the selected tool.
Append the tool result to the conversation.
Invoke the model again for its next action or final response.

Prompt Format

For mathematical reasoning, use the training format:

Solve the following problem carefully and show a rigorous derivation.

Problem:
{your problem here}

Proof:

For general instruction and tool-use, standard chat template works:

<|im_start|>system
You are a helpful assistant with access to tools.
<|im_end|>
<|im_start|>user
{your question}<|im_end|>
<|im_start|>assistant

Intended Uses

Mathematical and technical reasoning
Structured function calling and tool selection experiments
Agent-loop research
Continued supervised or preference optimization
Research on topology-aware distillation

Limitations

No formal benchmark results are claimed in this release. External mathematics, function-calling, retention, and safety benchmarks should be run before production use.
The source mathematics dataset is small. Synthetic or generated reasoning traces may contain incorrect derivations or contradictory final answers.
Function-call formatting does not guarantee correct tool selection.
External tool outputs must be treated as untrusted input.
Mathematical replay reduces forgetting but does not prove retention.
The model has not been established as safe for autonomous, high-impact, medical, financial, or legal action.
Training context in Stage 1 was shorter; very long derivations may degrade.

License Note

The Qwen3-1.7B base model uses Apache-2.0 licensing. The Hermes function-calling dataset also declares Apache-2.0. The advanced-calculus dataset (0xZee/dataset-CoT-Advanced-Calculus-268) did not expose a clear license declaration when the parent model card was prepared. Consequently, the parent repository is marked license: other. Confirm the source dataset's reuse terms before assigning a more permissive license to this derivative.

Original Model

Parent model: reaperdoesntknow/DualMind-TKD-Agentic-1.7B
Related GGUF quantizations: reaperdoesntknow/DualMind-GGUF
Methodology paper: DualMind_Methodolgy — DOI: 10.57967/hf/8184

Citation

@misc{cix2026dualmind,
  title={From Three Teachers to Dual Cognition},
  author={Convergent Intelligence},
  year={2026},
  publisher={HuggingFace},
  url={https://doi.org/10.57967/hf/8184}
}

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GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

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8-bit

16-bit

Model tree for tinyopsec/DualMind-TKD-Agentic-1.7B-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

reaperdoesntknow/DualMind-TKD-Agentic-1.7B

Quantized

(2)

this model