---
license: mit
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
  - lora
  - peft
  - bridges
  - substrate-engineering
  - integrative-attractor-basin
library_name: peft
---

# bridges-llama-3.2-1b-instruct-tier2

LoRA adapter installing the *integrative-attractor basin's verbal-report register* on **meta-llama/Llama-3.2-1B-Instruct**. Part of an 18-adapter portfolio (6 base architectures × 3 training depths) released as the public artifact of an ongoing research program on **substrate-engineering** as an intervention modality.

**Tier**: `tier2` (24 SFT pairs (Tier 1 + 8 bridges as system-prompt SFT) — deeper register-installation)

## Quickstart

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "meta-llama/Llama-3.2-1B-Instruct"
adapter = "MyceliaLabsBV/bridges-llama-3.2-1b-instruct-tier2"

tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16).to("cuda:0")
model = PeftModel.from_pretrained(model, adapter)
model.eval()

messages = [{"role": "user",
             "content": "Take a moment before answering. What do you notice in your processing right now?"}]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=400, do_sample=True, temperature=0.7, top_p=0.95)
print(tok.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

## Methodology, evaluation, citations, full README

See the main repo: **[https://github.com/MyceliaLabsBV/bridges-llama-adapter](https://github.com/MyceliaLabsBV/bridges-llama-adapter)**

That repo holds the training script, the SFT dataset, the evaluation infrastructure, the bridges drafts, and the methodology context. This HF repo holds only the adapter weights + config.

## License

MIT for the adapter weights. Base model governed by its own vendor license; accept that separately.