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

Libraries

How to use reaperdoesntknow/DualMind-TKD-Agentic-1.7B with Transformers:

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

pipe = pipeline("text-generation", model="reaperdoesntknow/DualMind-TKD-Agentic-1.7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DualMind-TKD-Agentic-1.7B")
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/DualMind-TKD-Agentic-1.7B")
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-TKD-Agentic-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/DualMind-TKD-Agentic-1.7B"
# 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-TKD-Agentic-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/DualMind-TKD-Agentic-1.7B

SGLang

How to use reaperdoesntknow/DualMind-TKD-Agentic-1.7B 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-TKD-Agentic-1.7B" \
    --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-TKD-Agentic-1.7B",
		"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-TKD-Agentic-1.7B" \
        --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-TKD-Agentic-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use reaperdoesntknow/DualMind-TKD-Agentic-1.7B with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/DualMind-TKD-Agentic-1.7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

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.

Loading

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
<!-- cix-keeper-ts:2026-07-14T13:15:33Z -->

Downloads last month: 448

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for reaperdoesntknow/DualMind-TKD-Agentic-1.7B

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(895)

this model

Quantizations

1 model

reaperdoesntknow
/

DualMind-TKD-Agentic-1.7B