README.md

---
language:
- en
library_name: transformers
pipeline_tag: text-generation
license: other
base_model:
- Qwen/Qwen3-1.7B
datasets:
- 0xZee/dataset-CoT-Advanced-Calculus-268
- NousResearch/hermes-function-calling-v1
tags:
- transformers
- qwen3
- text-generation
- conversational
- agentic
- function-calling
- tool-use
- knowledge-distillation
- topology-guided-distillation
- lora-merged
- mathematics
- disc
- dualmind
- convergent-intelligence
---

# DualMind TKD Agentic 1.7B

DualMind TKD Agentic 1.7B is a two-stage derivative of
`Qwen/Qwen3-1.7B`.

It combines topology-guided mathematical knowledge distillation with
assistant-masked agentic and function-calling specialization.

## Training lineage

### 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

Stage 1 was designed to transfer mathematical reasoning behavior while
placing 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-result messages were visible as
  context but excluded from direct loss
- 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.

## Intended uses

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

## Tool execution

This 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.

## Loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "reaperdoesntknow/DualMind-TKD-Agentic-1.7B"

tokenizer = AutoTokenizer.from_pretrained(repo_id)

model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)
Evaluation status

No formal benchmark results are claimed in this release.

The training pipeline includes held-out loss monitoring and qualitative
generation smoke tests, but external mathematics, function-calling,
retention, and safety benchmarks should be run before production use.

Limitations
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.
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 did not expose a clear license declaration
when this model card was prepared. Consequently, this repository is
temporarily marked license: other. Confirm the source dataset's reuse
terms before assigning a more permissive license to this derivative.

Developer

Convergent Intelligence LLC / Reaper
Hugging Face: reaperdoesntknow
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