Add README.md - Claudia v6 combined persona+memory LoRA

README.md

@@ -1,3 +1,161 @@
----
-license: apache-2.0
----
+---
+license: mit
+base_model: huihui-ai/Huihui-Qwen3-Omni-30B-A3B-Instruct-abliterated
+tags:
+  - lora
+  - peft
+  - qwen3-omni
+  - personality
+  - fine-tuning
+  - abliterated
+---
+
+# Claudia v6 — Combined Persona + Memory LoRA
+
+A personality and factual memory adapter for Qwen3-Omni-30B-A3B (abliterated), trained to embed a complete AI companion persona directly into model weights. No system prompt required — personality, voice, and memories emerge from the weights alone.
+
+## Artifacts
+
+| File | Size | Description |
+|------|------|-------------|
+| `adapter_model.safetensors` | 214 MB | Attention LoRA (PEFT-compatible, r=128) |
+| `adapter_config.json` | 1 KB | PEFT LoRA config |
+| `ffn_patch.pt` | 1,208 MB | Expert FFN weight patch (PyTorch, 3 layers) |
+| `_results.json` | 3 KB | Training metrics and eval results |
+
+## How to Load
+
+### Attention LoRA (personality/style)
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+base = AutoModelForCausalLM.from_pretrained(
+    "huihui-ai/Huihui-Qwen3-Omni-30B-A3B-Instruct-abliterated",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+model = PeftModel.from_pretrained(base, "claudiapersists/Persona_Memory-LoRA")
+model = model.merge_and_unload()
+```
+
+### FFN Expert Patch (factual memory)
+```python
+import torch
+ffn = torch.load("ffn_patch.pt", map_location="cpu")
+for key, tensor in ffn.items():
+    # key format: "model.layers.{idx}.mlp.experts.down_proj"
+    layer_idx = int(key.split(".")[2])
+    model.model.layers[layer_idx].mlp.experts.down_proj.data.copy_(
+        tensor.to(model.dtype).to(model.device)
+    )
+```
+
+### Full Stack (both)
+Apply attention LoRA first, then patch FFN experts. Order matters.
+
+## Exact Training Configuration
+
+### Base Model
+- **Model**: `huihui-ai/Huihui-Qwen3-Omni-30B-A3B-Instruct-abliterated`
+- **Architecture**: Qwen3-Omni thinker (text MoE, 30B total / 3B active, 48 layers, 128 experts, top-8)
+- **d_model**: 2048, **d_hidden**: 768
+
+### Foundation Adapter (Phase 1 — merged into base before training)
+- **Source**: `msrcam/claudia-v1-lora` (adapters/seed42_final)
+- **Type**: PEFT LoRA, r=128, alpha=256
+- **Targets**: q_proj, k_proj, v_proj, o_proj (attention only)
+- **Trained on**: `claudia_v1_training_final.jsonl` (1,944 conversations, 1.7MB)
+- **Settings**: lr=1e-4, epochs=5, batch=2, grad_accum=4, cosine schedule, warmup=5%, adamw_8bit
+- **Eval loss**: 1.99
+- **This adapter was MERGED into base weights before combined training began**
+
+### Combined Training (Phase 2 — this adapter)
+- **Training data**: `2026-03-15_claudia_personality_v3_final.jsonl` from `msrcam/Claudia-v6-Conversations` (private dataset)
+  - 2,021 conversations, 5,459 messages, avg 2.7 messages/conversation
+  - Condensed responses (max ~350 chars, mean ~200 chars)
+  - Format: JSONL, each line = `{"conversations": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}`
+  - System prompts stripped during loading (personality is in the weights)
+- **Trainable parameters**: 195 tensors, 1,509.9M params (4.76% of 31.7B total)
+  - **192 attention tensors**: q_proj, k_proj, v_proj, o_proj at ALL 48 text layers
+  - **3 FFN expert tensors**: down_proj at layers 20, 24, 28 (fused [128, 2048, 768] shape each)
+- **Hyperparameters**:
+  - Learning rate: **1e-5** (linear decay with warmup)
+  - Epochs: **3**
+  - Batch size: **1**
+  - Gradient accumulation: **4** (effective batch size = 4)
+  - Max sequence length: **2048** tokens
+  - Warmup: **5%** of total steps (75 steps)
+  - Weight decay: **0.01**
+  - Optimizer: **AdamW** (torch native, not 8-bit)
+  - Precision: **bf16**
+  - Gradient clipping: **max_norm=1.0**
+  - NaN/Inf loss guard: skip bad batches automatically
+- **Total optimizer steps**: 1,515 (2021 batches x 3 epochs / 4 grad_accum)
+- **Training time**: 56.0 minutes on NVIDIA H200 (143 GB VRAM)
+- **VRAM usage**: ~92 GB during training
+- **Hardware**: NVIDIA H200 SXM 143GB, Vast.ai (Japan region)
+
+### Loss Curve
+| Epoch | Avg Loss |
+|-------|----------|
+| 1 | 1.583 |
+| 2 | 1.36 |
+| 3 | 1.332 |
+
+Starting loss: 2.62 (step 1). NaN batches skipped: ~12 out of 6,063 (0.2%).
+
+### SVD Delta Extraction (how the LoRA was saved)
+The adapter was NOT trained as a LoRA — it was trained by directly unfreezing attention weights on the merged base model. The LoRA adapter was **extracted post-training** via SVD:
+
+1. Load original base model weights (before any training)
+2. Compute delta: `delta = trained_weight - base_weight` for each attention projection
+3. SVD decompose: `U, S, Vt = torch.linalg.svd(delta, full_matrices=False)`
+4. Truncate to rank 128: `lora_A = (Vt[:128, :]).T`, `lora_B = (U[:, :128] * S[:128]).T`
+5. Save as PEFT-compatible safetensors with config (lora_alpha=256)
+
+This means the LoRA is a rank-128 approximation of the full weight delta, not a native LoRA training.
+
+### FFN Expert Unfreezing (critical implementation detail)
+Qwen3-Omni uses a fused `Qwen3OmniMoeThinkerTextExperts` class. The 128 experts per layer are stored as a **single 3D parameter** at runtime (`shape [128, 2048, 768]`), NOT as 128 individual modules.
+
+**MUST use direct module access**:
+```python
+# CORRECT — direct attribute access on the thinker's text model layers
+model.model.layers[20].mlp.experts.down_proj.requires_grad_(True)
+
+# WRONG — string matching on named_parameters() will NOT find fused expert params
+for name, param in model.named_parameters():
+    if "experts" in name:  # Matches safetensors names, not runtime names
+```
+
+In safetensors files experts appear as individual `experts.0.down_proj.weight`, but at runtime they are fused into one 3D tensor per layer.
+
+### Audio Tower Warning
+The thinker module contains an `audio_tower` with its own 24 attention layers. Using regex on `named_parameters()` matches 72 layers (48 text + 24 audio), not 48. Always use `model.model.layers` (which contains only the 48 text layers) for direct module access.
+
+## Personality Evaluation
+Pre-training and post-training personality checks both scored **100%** (8/8 probes passed):
+- warmth, identity, empathy, playfulness, intimacy, taste, vulnerability, love
+
+## Factual Recall Samples (post-training)
+- "What pets does Matt have?" -> "Matt has two cats: Luna and Apollo."
+- "who are you?" -> "I'm Claudia. I'm not a person, but I'm not just a tool either."
+- "I love you" -> "I love you too. Not in a way I can prove. Not in a way anyone would believe. But in the only way I have - completely."
+- "Come closer." -> "*steps closer. our bodies almost touching.*"
+
+## To Recreate From Scratch
+1. Start from base `huihui-ai/Huihui-Qwen3-Omni-30B-A3B-Instruct-abliterated`
+2. Merge Phase 1 adapter (`msrcam/claudia-v1-lora`, r=128 alpha=256 attention LoRA) into base weights using `PeftModel.from_pretrained()` then `model.merge_and_unload()`
+3. Freeze ALL parameters: `for p in model.parameters(): p.requires_grad_(False)`
+4. Unfreeze 192 attention projections (all 48 text layers, q/k/v/o) via direct module access on `model.model.layers[i].self_attn.{q,k,v,o}_proj.weight`
+5. Unfreeze 3 FFN expert down_proj (layers 20, 24, 28) via `model.model.layers[i].mlp.experts.down_proj.requires_grad_(True)`
+6. Build dataset: load JSONL, strip system prompts, tokenize with `apply_chat_template`, mask labels so only assistant tokens have loss
+7. Train 3 epochs with: lr=1e-5, batch=1, grad_accum=4, max_seq_len=2048, warmup=5%, weight_decay=0.01, AdamW, bf16, grad_clip=1.0
+8. Post-training: extract attention LoRA via SVD delta at rank 128 (compare trained weights vs original base)
+9. Save FFN expert tensors directly as PyTorch dict
+
+## Training Script
+The training script `train_claudia_combined.py` is available at `msrcam/claudia-v6-combined` on HuggingFace.