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README.md

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+---
+base_model: Jackrong/Qwopus3.6-35B-A3B-v1
+library_name: peft
+pipeline_tag: image-text-to-text
+license: apache-2.0
+tags:
+- qwen3_5_moe
+- qwen3.6
+- qwopus
+- moe
+- lora
+- peft
+- unsloth
+- union-street-ai
+- helios
+- identity-tune
+- adapter
+language:
+- en
+---
+
+# Helios Rabbit v1
+
+Helios Rabbit v1 is a lightweight identity and behavior LoRA adapter for `Jackrong/Qwopus3.6-35B-A3B-v1`, produced by Union Street AI.
+
+This is an adapter, not a full merged checkpoint. Use it with the base model named above.
+
+## Intended Identity
+
+The adapter is intended to make the model identify as Helios, a local AI model developed and adapted by Union Street AI, while preserving the base model's coding, repo-analysis, and infrastructure strengths.
+
+It should be honest about lineage: Helios is adapted from open model research and local post-training work. It should not claim that Union Street AI trained the base model from scratch.
+
+## Training Summary
+
+- Run name: `helios-rabbit-v1`
+- Base model: `Jackrong/Qwopus3.6-35B-A3B-v1`
+- Method: LoRA SFT with Unsloth / PEFT
+- Data: 475 training conversations, 25 validation conversations
+- Max sequence length: 2048
+- LoRA rank: 8
+- LoRA alpha: 8
+- Target: language attention modules, vision layers disabled, MLP expert LoRA disabled for this first identity pass
+- Hardware: Lambda Labs 8x NVIDIA A100-SXM4-80GB
+
+## Dataset Notes
+
+The dataset is a small synthetic identity and behavior corpus for Helios. It focuses on:
+
+- identity and provenance
+- coding and infrastructure assistant behavior
+- candid but bounded adult-world conversation
+- liberty-minded, anti-authoritarian, rule-of-law, pro-human-agency posture
+- honesty about uncertainty and model lineage
+
+## Status
+
+This is a v1 experimental adapter. Evaluate before production use.
+
+## Loading Sketch
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_id = "Jackrong/Qwopus3.6-35B-A3B-v1"
+adapter_id = "UnionStreet/helios-rabbit-v1"
+
+tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto", trust_remote_code=True)
+model = PeftModel.from_pretrained(model, adapter_id)
+```
+
+Depending on your inference stack, you may need the multimodal Qwen3.5 MoE model class rather than `AutoModelForCausalLM`.
+
+## License
+
+The base model card declares `apache-2.0`; this adapter is released under Apache 2.0 as well, subject to the base model's terms.