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Model: reaperdoesntknow/Shepherd-Alpha Source: Original Platform
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137
README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- tactical-reasoning
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- military
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- defense-ai
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- bicell-dispersal
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- sft
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- dual-perspective
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- shepherd
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- convergentintel
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- qwen
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- ai
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base_model: Qwen/Qwen3-1.7B
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datasets:
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- ZennyKenny/tactical-military-reasoning-v.1.0
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Shepherd-Alpha
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**The first defense AI reasoning model on Hugging Face.**
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Shepherd-Alpha is a tactical reasoning model fine-tuned on dual-perspective military scenario analysis using BiCell Depth Dispersal — a novel training methodology that partitions transformer layers by abstraction depth and trains them asymmetrically to separate representation encoding from task-specific reasoning.
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Developed by [Convergent Intelligence LLC: Research Division](https://convergentintel.com)
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## What This Model Does
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Given a tactical scenario, Shepherd-Alpha produces structured dual-perspective analysis:
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- **Attack reasoning** — how an adversary would exploit the situation
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- **Defense reasoning** — how to counter, mitigate, and survive
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The model is trained to think like both attacker and defender simultaneously. A model that understands how to attack becomes a defender that anticipates.
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## Training Methodology: BiCell Depth Dispersal
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Standard fine-tuning updates all layers jointly, allowing co-adaptation that can mask shallow learning. BiCell Depth Dispersal forces genuine specialization:
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| Phase | Frozen | Training | Purpose |
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|-------|--------|----------|---------|
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| 1 | Upper layers (14-27) | Lower layers (0-13) | Foundations encode before specialization exists |
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| 2 | Lower layers (0-13) | Upper layers (14-27) | Reasoning learns over frozen representations |
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| 3 | None | All layers | Joint integration of asymmetric gradient history |
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All three backward passes accumulate gradients before a single optimizer step. The asymmetric gradient history forces each depth zone to develop independently before integration.
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**Key finding during training:** Lower layers consistently produce ~1.7x the gradient magnitude of upper layers during domain adaptation. The pretrained upper layers already possess sufficient reasoning capacity — the primary adaptation is teaching lower layers to encode tactical domain structure. This suggests that for domain-specific SFT, representation layers (not reasoning layers) are the bottleneck.
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### Training Details
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- **Base model:** Qwen/Qwen3-1.7B (28 layers, all full attention)
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- **Dataset:** [ZennyKenny/tactical-military-reasoning-v.1.0](https://huggingface.co/datasets/ZennyKenny/tactical-military-reasoning-v.1.0) — 150 dual-perspective tactical scenarios with attack and defense chain-of-thought reasoning (MIT licensed)
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- **Architecture:** 28 transformer layers split at depth 14 — Zone Lo (layers 0-13) and Zone Hi (layers 14-27)
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- **Hardware:** NVIDIA A100
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- **Epochs:** 3
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- **Batch size:** 2
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- **Learning rate:** 2e-5 (AdamW, weight decay 0.01)
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- **Precision:** bfloat16
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- **Label masking:** Loss computed only on assistant (reasoning) tokens, not scenario prompts
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/Shepherd-Alpha")
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tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/Shepherd-Alpha")
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messages = [
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{
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"role": "user",
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"content": "Analyze this tactical scenario.\n\nScenario: A mechanized platoon advancing through urban terrain detects a coordinated drone swarm from the northeast. Limited anti-air capability. Civilian structures restrict fields of fire."
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}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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)
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output = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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)
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generated = output[0][inputs["input_ids"].shape[1]:]
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print(tokenizer.decode(generated, skip_special_tokens=True))
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```
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## The Shepherd Program
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Shepherd-Alpha is the first public model in the Shepherd family — an ongoing research program developing AI systems for autonomous defense applications. The program spans:
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- **Shepherd Doctrine** — a comprehensive counter-swarm and area defense blueprint covering 28+ subsystems across five concentric engagement layers
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- **Shepherd AI** — tactical reasoning models trained on dual-perspective analysis (this model)
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- **BiCell Dispersal** — a training methodology based on the B_i Cell Dispersal framework for stochastic layer partitioning during fine-tuning
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## Limitations
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- **Alpha release** — this is a research checkpoint, not a production system
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- **Small training set** — 150 scenarios provides format and domain grounding but limited tactical depth. Future versions will incorporate augmented datasets with multi-model generated reasoning
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- **Base model thinking mode** — Qwen3's pretrained `<think>` generation pattern can override the structured output format. Use `enable_thinking=False` in generation config for cleaner output
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- **Not a weapon system** — this model performs analysis and reasoning. It does not control, target, or actuate anything
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## Citation
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```bibtex
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@misc{shepherd-alpha-2026,
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title={Shepherd-Alpha: Tactical Reasoning via BiCell Depth Dispersal},
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author={Convergent Intelligence LLC},
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year={2026},
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url={https://huggingface.co/reaperdoesntknow/Shepherd-Alpha}
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}
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```
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## Related Work
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- [Structure Over Scale](https://doi.org/10.57967/hf/5165) — Foundation paper on structure-first training methodologies
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- [DualMind Methodology](https://doi.org/10.57967/hf/5184) — Dual-cognitive-mode SFT using EXPLORE/EXAMINE tokens
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- [Discrepancy Calculus](https://doi.org/10.57967/hf/5194) — Mathematical framework grounding BiCell dispersal theory
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- [B_i Cell Dispersal Framework](https://convergentintel.com) — Stochastic layer freezing grounded in DISC measure theory
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---
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*Convergent Intelligence LLC: Research Division*
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*"Structure beats scale. Collaboration beats hierarchy. Observation beats theory."*
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<!-- cix-keeper-ts:2026-06-12T13:16:55Z -->
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89
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_cache": true,
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"use_sliding_window": false,
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|
"vocab_size": 151675
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|
}
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13
generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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|
"eos_token_id": [
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151645,
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|
151643
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||||||
|
],
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"temperature": 0.6,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.95,
|
||||||
|
"transformers_version": "5.0.0"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:880243c7e62335f0ee8053440c666bed640d50f2d87f46b9d4eb89a844357e35
|
||||||
|
size 3440116552
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9ac045cd8ace1af66e21df429a0ddc88fe6580b7c567e9b963819c6247873cb8
|
||||||
|
size 11423769
|
||||||
22
tokenizer_config.json
Normal file
22
tokenizer_config.json
Normal file
@@ -0,0 +1,22 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": [
|
||||||
|
"<respond>",
|
||||||
|
"</respond>",
|
||||||
|
"<explore>",
|
||||||
|
"</explore>",
|
||||||
|
"<examine>",
|
||||||
|
"</examine>"
|
||||||
|
],
|
||||||
|
"is_local": true,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user