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

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

VRAM Requirements

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

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

1. Download any .gguf file from this repository
2. Open LM Studio → Load Model → select the file
3. 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:

1. Parse the model's tool call.
2. Execute the selected tool.
3. Append the tool result to the conversation.
4. 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}
}