Instructions to use UnionStreet/helios-rabbit-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use UnionStreet/helios-rabbit-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Jackrong/Qwopus3.6-35B-A3B-v1") model = PeftModel.from_pretrained(base_model, "UnionStreet/helios-rabbit-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use UnionStreet/helios-rabbit-v1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for UnionStreet/helios-rabbit-v1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for UnionStreet/helios-rabbit-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for UnionStreet/helios-rabbit-v1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="UnionStreet/helios-rabbit-v1", max_seq_length=2048, )
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
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.
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Qwen/Qwen3.6-35B-A3B