| --- |
| 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. |
|
|
