ataeff
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@@ -2,206 +2,80 @@
 base_model: unsloth/gemma-3-270m-it
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/gemma-3-270m-it
 - lora
-- transformers
 ---
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-- PEFT 0.18.1

 base_model: unsloth/gemma-3-270m-it
 library_name: peft
 pipeline_tag: text-generation
+license: gpl-3.0
 tags:
 - lora
+- gemma
+- resonate
+- reasoning
+- multilingual
+language:
+- en
+- fr
+- de
+- ru
+- he
+- ar
+- ja
+- zh
 ---
+# Gemma-3 270M-IT /resonate/ LoRA
+**LoRA adapter** that teaches Gemma-3 270M-IT the `/resonate/` reasoning format — stream-of-consciousness thinking followed by a clean answer.
+## What is /resonate/?
+```
+/resonate/
+[free-form thinking — cynical, multilingual, associative, honest]
+/resonated/
+[clean, structured answer]
+```
+The model learns to THINK before answering. The `/resonate/` block is raw reasoning — it can switch languages, use metaphors, be irreverent. The `/resonated/` block is the distilled answer.
+## Architecture
+| | |
+|---|---|
+| Base | `unsloth/gemma-3-270m-it` (268.1M params) |
+| Frozen | `embed_tokens` = 167.8M (63%) — **all 140 languages preserved** |
+| LoRA | R=16, alpha=32, q_proj + v_proj only |
+| Trainable | 0.74M (0.3% of total) |
+| Training | 3 epochs, 6445 examples, 32 min on A100 |
+| Best val loss | 2.9241 |
+## Key insight
+Freezing `embed_tokens` (63% of the model) preserves the multilingual embedding space. The LoRA adapter only modifies attention projections — teaching the model HOW to think, not WHAT languages to know.
+## Languages verified working
+English, French, German, Russian, Hebrew, Arabic, Japanese, Chinese — all generate coherent text with `/resonate/` format after fine-tuning.
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+tokenizer = AutoTokenizer.from_pretrained("ataeff/g")
+base = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-270m-it", dtype=torch.bfloat16)
+model = PeftModel.from_pretrained(base, "ataeff/g")
+prompt = "<start_of_turn>user\nWhat is the meaning of life?<end_of_turn>\n<start_of_turn>model\n"
+ids = tokenizer(prompt, return_tensors="pt").input_ids
+out = model.generate(ids, max_new_tokens=200, temperature=0.7, do_sample=True)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+## Training data
+- `resonance_yent_full.jsonl` — 6435 examples of /resonate/ format dialogues
+- `resonance_gold_10.jsonl` — 10 hand-crafted gold examples (math, philosophy, code, multilingual)
+## Part of the Arianna Method ecosystem
+- [ariannamethod.ai](https://github.com/ariannamethod)