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README.md

@@ -1,3 +1,127 @@
 ---
 license: cc-by-nc-4.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+language:
+  - en
+  - zh
+pipeline_tag: text-generation
+library_name: peft
+tags:
+  - hypernetwork
+  - hyper-lora
+  - lora
+  - role-play
+  - character-impersonation
+  - persona
+  - dialogue
+  - phase-tree
+datasets:
+  - IAAR-Shanghai/phase_tree_data
 ---
+
+# PHASE-Tree Models
+
+Released model checkpoints for the **PHASE-Tree** project
+(*Psychology-grounded Hierarchical Attribute-Structured Evolving Tree*).
+
+## Download
+
+The PHASE-Tree codebase expects these checkpoints under
+`PHASE-Tree/phase_tree_models/`. The recommended way to obtain a working copy
+is therefore:
+
+```bash
+# From the repository root (i.e. inside the cloned PHASE-Tree project):
+cd PHASE-Tree
+hf download IAAR-Shanghai/phase_tree_models --local-dir phase_tree_models
+```
+
+This places every file under `PHASE-Tree/phase_tree_models/`, matching the
+relative paths used by every script in the codebase (e.g.
+`phase_tree_models/sft/hyper_lora/hypermod.pt`).
+
+Alternative methods:
+
+- `git clone https://huggingface.co/IAAR-Shanghai/phase_tree_models`
+  (run from the `PHASE-Tree/` root; clones into `phase_tree_models/`
+  automatically).
+- Programmatic via `huggingface_hub.snapshot_download(...)` with
+  `local_dir="phase_tree_models"`.
+
+This release contains the **single recommended checkpoint** for each of the
+two stages in the PHASE-Tree training pipeline. During development we ran a
+larger ablation grid (six hyper-LoRA SFT runs covering warm-start vs
+cold-start initialisation, two learning rates, and trainable vs frozen
+hypernet output heads, plus a separate One-PEFT-Per-User / OPPU baseline
+sweep). Only the checkpoints reported in the paper are bundled here; the
+ablations are kept locally for reproducibility but are not part of the
+release.
+
+## Layout
+
+| Path | Description |
+|------|-------------|
+| `phase_tree_pretrained/` | Hypernetwork pretrained on the PHASE-Tree character corpus. Used as the warm-start initialisation for the SFT run below. |
+| `sft/hyper_lora/` | The **anchor** hyper-LoRA SFT run (warm-start, lr=5e-6, trainable heads). This is the checkpoint reported in the PHASE-Tree paper. |
+
+Each leaf folder is self-describing via its own `README.md`.
+
+## Recommended Checkpoint
+
+For character-conditioned generation, load:
+
+```
+sft/hyper_lora/hypermod.pt
+```
+
+The pretrained hypermod (`phase_tree_pretrained/hypermod.pt`) is the upstream
+warm-start dependency of this anchor run, not an independently usable
+inference model. It is included so the training pipeline can be reproduced
+end-to-end.
+
+```
+phase_tree_pretrained/hypermod.pt   ──warm-start──▶   sft/hyper_lora/hypermod.pt
+       (pretraining stage)                                  (anchor SFT, recommended)
+```
+
+## Why a single SFT checkpoint?
+
+Six hyper-LoRA SFT runs were trained during development, varying
+initialisation, learning rate, and whether the hypernet output heads are
+trainable. The released `sft/hyper_lora/` is the cell selected by the
+LLM-as-judge `character` and `semantic` ratings together with
+Qwen3-Embedding-4B response-vs-reference cosine similarity on a held-out
+evaluation set; the other five cells are ablations and are not bundled.
+
+Per-step intermediate checkpoints (`it_5000` … `it_40000`) and the full
+post-hoc evaluation artefacts (`eval_ckpt_judge_scores/`,
+`eval_ckpt_val_loss/`) are likewise not bundled. To regenerate them you
+would need to re-run training (`src/scripts/train_phase_tree_qwen_7b.sh`)
+followed by the evaluation scripts under `src/scripts/`.
+
+## Base Model
+
+All checkpoints are trained on top of
+[`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
+The hyper-LoRA and pretrained-hypermod checkpoints additionally use
+[`Qwen/Qwen3-Embedding-4B`](https://huggingface.co/Qwen/Qwen3-Embedding-4B)
+as the task-embedding encoder.
+
+## Loading
+
+| Checkpoint | Loader |
+|------------|--------|
+| Pretrained hypermod (`phase_tree_pretrained/`) | `hyper_llm_modulator.hyper_modulator.load_hypermod_checkpoint(...)` |
+| Hyper-LoRA SFT (`sft/hyper_lora/`)             | `hyper_llm_modulator.hyper_modulator.load_hypermod_checkpoint(...)` |
+
+The hypermod loader expects a checkpoint directory layout identical to the
+one used here (`hypermod.pt` + sibling `args.yaml` + `adapter_config.json`).
+It reads the architecture from `args.yaml` automatically; no extra
+configuration is required at inference time.
+
+## Intended Use
+
+These checkpoints are released as research artefacts for evaluating
+personalised and hypernetwork-based approaches to character-grounded dialogue
+generation. They are **not** intended for production user-facing
+applications without additional safety filtering.

phase_tree_pretrained/README.md

@@ -0,0 +1,108 @@
+---
+license: cc-by-nc-4.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+library_name: peft
+language:
+- en
+- zh
+tags:
+- hypernetwork
+- hyper-lora
+- lora
+- role-play
+- character-impersonation
+- pretraining
+- phase-tree
+datasets:
+- IAAR-Shanghai/phase_tree_data
+---
+
+# PHASE-Tree Pretrained Hypermod
+
+Hypernetwork pretrained on the PHASE-Tree character-dialogue corpus on top of
+[`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
+
+This is the **warm-start checkpoint** consumed by the SFT runs released under
+`phase_tree_models/sft/hyper_lora/`. It is *not* intended as a stand-alone
+inference checkpoint — for character-conditioned generation, the SFT runs are
+recommended.
+
+> The pretraining-stage training schedule (full dataset list, optimizer
+> schedule, etc.) is not bundled with this release. Only the fields required
+> by `load_hypermod_checkpoint` (path resolution + hypermod architecture) are
+> retained in `args.yaml`; the SFT runs in `phase_tree_models/sft/hyper_lora/`
+> carry the complete training configurations for their respective fine-tuning
+> stages.
+
+## What is a hypermod?
+
+A **hypermod** (hyper-modulator) is a hypernetwork that, conditioned on a
+character profile embedding, emits a low-rank LoRA delta `ΔW = AB` for each
+target layer of the base model on the fly. The base model weights themselves
+are never updated; only the hypernet is trained. At inference time the
+hypernet generates a personalised LoRA per character, giving one model that
+covers an open-ended set of personas without needing to store per-character
+adapters.
+
+## Files
+
+| File | Purpose |
+|------|---------|
+| `hypermod.pt` | The released pretrained hypermod (it_20000 of the original pretraining run). Use this as the entry point. |
+| `args.yaml` | Architecture and loader metadata (no training schedule — this checkpoint is meant to be consumed, not resumed). |
+| `adapter_config.json` | LoRA target-module stub (rank 8, alpha 16, `q_proj` + `v_proj`). |
+
+## How to load
+
+```python
+from huggingface_hub import snapshot_download
+from hyper_llm_modulator.hyper_modulator import load_hypermod_checkpoint
+
+ckpt_dir = snapshot_download("<your-hf-username>/PHASE-Tree-pretrained-hypermod")
+
+(
+    args, hypermod, base_model, tokenizer,
+    emb_model, emb_tokenizer, task_desc_format_fn, pooling_fn,
+) = load_hypermod_checkpoint(f"{ckpt_dir}/hypermod.pt", device="cuda")
+```
+
+The loader reads `args.yaml` and `adapter_config.json` from the same directory
+as `hypermod.pt` automatically; you do not need to pass them explicitly. The
+full inference pipeline (profile → embedding → per-layer LoRA → generation)
+lives in the PHASE-Tree codebase.
+
+## Architecture
+
+| Component | Value |
+|-----------|-------|
+| Base model | `Qwen/Qwen2.5-7B-Instruct` |
+| Task encoder | `Qwen/Qwen3-Embedding-4B` |
+| Target modules | `q_proj`, `v_proj` |
+| LoRA rank `r` | 8 |
+| LoRA alpha | 16 |
+| LoRA dropout | 0.05 |
+| Hypernet latent size | 1024 |
+| Hypernet head input size | 2048 |
+| `delta_w` scaling | 100 |
+
+## Use as warm-start
+
+SFT runs whose `args.yaml` sets
+
+```yaml
+init_hypermod_from: phase_tree_models/phase_tree_pretrained/hypermod.pt
+```
+
+consume this checkpoint as the initial hypernet weights. This is the
+warm-start used by the released anchor SFT run under
+`phase_tree_models/sft/hyper_lora/`.
+
+## Limitations
+
+- This is a **pretraining** checkpoint; downstream SFT is required for
+  competitive character-fidelity scores.
+- Persona conditioning is mediated entirely by the profile embedding fed into
+  the task encoder; the model has no other persona-control mechanism.
+- Generations may reproduce stylistic biases of the source corpora and are
+  intended for research evaluation only.

phase_tree_pretrained/adapter_config.json

@@ -0,0 +1,36 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-7B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": true
+}

phase_tree_pretrained/args.yaml

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+# ==========================================================================
+# Architecture / loading metadata for the PHASE-Tree pretrained hypermod.
+#
+# The released warm-start checkpoint is
+#   phase_tree_models/phase_tree_pretrained/hypermod.pt
+# (= the it_20000 snapshot of the original pretraining run).
+#
+# Only fields read by `load_hypermod_checkpoint` (path resolution +
+# hypermod architecture) are kept here; the dataset lists and the
+# original training schedule are intentionally omitted, because the
+# PHASE-Tree SFT runs warm-start from these weights and override every
+# training hyperparameter from `train_phase_tree_qwen_7b.sh`.
+# ==========================================================================
+
+# ── Paths ────────────────────────────────────────────────────────────────
+model_dir: Qwen/Qwen2.5-7B-Instruct
+emb_model: Qwen/Qwen3-Embedding-4B
+mt_lora_path: null
+
+# ── Task setup ───────────────────────────────────────────────────────────
+training_task: sft
+exp_setup: hyper_lora
+sft_mode: completion
+encoder_type: linear
+
+# ── Task-embedding mode ─────────────────────────────────────────────────
+use_hypernet: true
+use_per_task_emb: true
+use_one_hot_task_emb: false
+use_inp_as_desc: false
+use_per_sample_desc: false
+use_default_desc: false
+
+# ── Hypermod architecture ────────────────────────────────────────────────
+head_in_size: 2048
+head_use_bias: false
+hypernet_latent_size: 1024
+delta_w_scaling: 100
+pred_z_score: true
+factorized: false
+shared_AB_head: false
+autoreg_gen: false
+learnable_pos_emb: false
+learnable_AB_offset: false
+
+# ── Fusion (disabled; kept for loader compatibility) ────────────────────
+use_conv_fusion: false
+conv_fusion_type: 1d
+conv_fusion_kernel_size: 3
+conv_fusion_num_layers: 2
+conv_fusion_channels: 64
+conv_fusion_dropout: 0.1
+use_attention_fusion: false
+attention_fusion_type: self
+attention_num_heads: 8
+attention_num_layers: 2
+attention_dropout: 0.1
+
+# ── Target LoRA modules and context window ──────────────────────────────
+target_modules:
+- q_proj
+- v_proj
+inp_max_len: 1024

phase_tree_pretrained/hypermod.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f807533480a5353bd8637aab87fc50ae7039f708d73425dd467bad0f0811413e
+size 925242042

sft/hyper_lora/README.md

@@ -0,0 +1,137 @@
+---
+license: cc-by-nc-4.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+library_name: peft
+language:
+- en
+- zh
+tags:
+- hypernetwork
+- hyper-lora
+- lora
+- role-play
+- character-impersonation
+- sft
+- phase-tree
+datasets:
+- IAAR-Shanghai/phase_tree_data
+---
+
+# PHASE-Tree Hyper-LoRA SFT (anchor run)
+
+**Variant:** Warm-start, lr=5e-6 (anchor run)
+
+The **anchor** SFT run: hypernet warm-started from the PHASE-Tree pretrained
+checkpoint and fine-tuned at a conservative learning rate of 5e-6 with label
+smoothing 0.1 and NEFTune noise 5.0. This is the checkpoint reported in the
+PHASE-Tree paper.
+
+During development, six hyper-LoRA SFT cells were trained — an ablation grid
+over initialisation (warm-start vs cold-start), learning rate (5e-6 vs 1e-5),
+and trainable vs frozen hypernet output heads. Only this anchor cell is
+bundled here; the other five are kept locally for reproducibility.
+
+## What is a hypermod?
+
+A **hypermod** (hyper-modulator) is a hypernetwork that, conditioned on a
+character profile embedding, emits a low-rank LoRA delta `ΔW = AB` for each
+target layer of the base model at inference time. The base model weights are
+never updated; only the hypernet is trained. A single hypermod therefore
+generalises across an open-ended set of personas without needing to store a
+separate adapter per character.
+
+## Files
+
+| File | Purpose |
+|------|---------|
+| `hypermod.pt` | **Recommended checkpoint.** The anchor SFT step selected from per-step LLM-as-judge ratings (`character`, `semantic`) and Qwen3-Embedding-4B response-vs-reference cosine similarity. |
+| `args.yaml` | Full training configuration; consumed by the loader to instantiate the hypernet architecture. |
+| `adapter_config.json` | LoRA target-module stub (rank 8, alpha 16, `q_proj` + `v_proj`). |
+| `timing_stats.json` | Wall-clock breakdown of the training run (training / validation / other overhead, in seconds). |
+
+> Per-step snapshots (`checkpoints/it_5000` … `it_40000`) and the post-hoc
+> evaluation artefacts (`eval_ckpt_judge_scores/`, `eval_ckpt_val_loss/`)
+> generated during training are **not bundled** with this release. They can
+> be regenerated by re-running `src/scripts/train_phase_tree_qwen_7b.sh`
+> followed by the evaluation scripts under `src/scripts/`.
+
+## How to load
+
+```python
+from huggingface_hub import snapshot_download
+from hyper_llm_modulator.hyper_modulator import load_hypermod_checkpoint
+
+ckpt_dir = snapshot_download("<your-hf-username>/PHASE-Tree-hyper-lora-anchor")
+
+(
+    args, hypermod, base_model, tokenizer,
+    emb_model, emb_tokenizer, task_desc_format_fn, pooling_fn,
+) = load_hypermod_checkpoint(f"{ckpt_dir}/hypermod.pt", device="cuda")
+```
+
+The loader reads `args.yaml` and `adapter_config.json` from the same directory
+as `hypermod.pt` automatically. The full inference pipeline (profile →
+embedding → per-layer LoRA → generation) lives in the PHASE-Tree codebase.
+
+## Training configuration
+
+| Hyperparameter | Value |
+|----------------|-------|
+| Base model | `Qwen/Qwen2.5-7B-Instruct` |
+| Task encoder | `Qwen/Qwen3-Embedding-4B` |
+| Initialisation | Warm-start from `phase_tree_models/phase_tree_pretrained/hypermod.pt` |
+| Target modules | `q_proj`, `v_proj` |
+| LoRA rank `r` | 8 |
+| LoRA alpha | 16 |
+| LoRA dropout | 0.05 |
+| Hypernet latent size | 1024 |
+| Hypernet head input size | 2048 |
+| Freeze hypernet heads | `false` |
+| Optimizer steps | 40000 |
+| Effective batch size | 8 (per-device 4 × grad-accum 2) |
+| Learning rate | 5e-6 |
+| Warmup fraction | 0.05 |
+| Weight decay | 0.01 |
+| Label smoothing | 0.1 |
+| NEFTune noise α | 5.0 |
+| Checkpoint cadence | every 5000 steps |
+| Random seed | 42 |
+
+The complete configuration (including dataset lists, sampler settings, and
+fusion-module placeholders kept for loader compatibility) lives in `args.yaml`.
+
+## Training data
+
+The hypermod is jointly fine-tuned on the *train* splits of the eight
+PHASE-Tree character-dialogue datasets (RAIDEN, CharacterEval, HPD, SimsConv,
+ChatHaruhi, Friends, StarTrek_TNG, TheOffice), `m6_phase_tree` profile variant.
+Sampling follows the hierarchical `sqrt_size` strategy with 6 tasks × 2 points
+per batch.
+
+## Evaluation
+
+The released `hypermod.pt` was selected from per-step snapshots of the
+training run by scoring predictions on a held-out evaluation set along
+three axes:
+
+- **`character` (1–5)** — profile-consistency rating by an LLM judge (see
+  `evaluation/persona_rubric.md` in the PHASE-Tree codebase for the rubric).
+- **`semantic` (1–5)** — contextual-coherence rating by the same judge.
+- **`embedding`** — cosine similarity of the predicted and reference response
+  embeddings computed with Qwen3-Embedding-4B.
+
+The per-step intermediate snapshots and full evaluation artefacts produced
+during model selection are not bundled (see the note above the loading
+section); they can be regenerated from a re-training run via the scripts
+under `src/scripts/`.
+
+## Limitations
+
+- Persona conditioning is mediated entirely by the profile embedding fed into
+  the task encoder; the model has no other persona-control surface.
+- Generations may reproduce stylistic biases of the source corpora; intended
+  for research evaluation only.
+- The checkpoint depends on the PHASE-Tree codebase for inference and is not a
+  drop-in `peft.PeftModel`: `adapter_config.json` describes only which layers
+  receive a generated LoRA, not directly loadable weights.

sft/hyper_lora/adapter_config.json

@@ -0,0 +1,36 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-7B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": true
+}

sft/hyper_lora/args.yaml

@@ -0,0 +1,115 @@
+# ==========================================================================
+# Training args for the released anchor SFT run.
+# PHASE-Tree hyper-LoRA SFT on Qwen2.5-7B-Instruct.
+# Variant: warm-start, lr=5e-6 (anchor SFT run).
+# Effective batch size = batch_size * grad_accum_steps = 4 * 2 = 8.
+# 40000 optimizer steps; a checkpoint is saved every val_freq=5000 steps.
+# ==========================================================================
+run_name: anchor
+save_dir: phase_tree_models/sft/hyper_lora
+
+# ── Model ────────────────────────────────────────────────────────────────
+model_dir: Qwen/Qwen2.5-7B-Instruct
+emb_model: Qwen/Qwen3-Embedding-4B
+init_hypermod_from: phase_tree_models/phase_tree_pretrained/hypermod.pt
+init_hypermod_strict: false
+
+# ── Task / data ──────────────────────────────────────────────────────────
+training_task: sft
+exp_setup: hyper_lora
+sft_mode: completion
+encoder_type: linear
+train_ds_names:
+- RAIDEN_train
+- CharacterEval_train
+- HPD_train
+- SimsConv_train
+- ChatHaruhi_train
+- Friends_train
+- StarTrek_TNG_train
+- TheOffice_train
+eval_ds_info:
+- RAIDEN_random_test
+- RAIDEN_ood_test
+- CharacterEval_random_test
+- CharacterEval_ood_test
+- HPD_random_test
+- HPD_ood_test
+- SimsConv_random_test
+- SimsConv_ood_test
+- ChatHaruhi_random_test
+- ChatHaruhi_ood_test
+- Friends_random_test
+- Friends_ood_test
+- StarTrek_TNG_random_test
+- StarTrek_TNG_ood_test
+- TheOffice_random_test
+- TheOffice_ood_test
+use_per_task_emb: true
+use_one_hot_task_emb: false
+use_inp_as_desc: false
+use_per_sample_desc: false
+use_default_desc: false
+use_hierarchical_sampler: true
+dataset_sampling_strategy: sqrt_size
+equally_weight_sample: true
+n_tasks_per_batch: 6
+n_points_per_task: 2
+inp_max_len: 1024
+target_modules:
+- q_proj
+- v_proj
+
+# ── Hypermod architecture ────────────────────────────────────────────────
+use_hypernet: true
+head_in_size: 2048
+head_use_bias: false
+hypernet_latent_size: 1024
+delta_w_scaling: 100
+pred_z_score: true
+factorized: false
+shared_AB_head: false
+autoreg_gen: false
+learnable_pos_emb: false
+learnable_AB_offset: false
+freeze_heads: false
+
+# ── Fusion (disabled for this run, kept for loader compatibility) ────────
+use_conv_fusion: false
+conv_fusion_type: 1d
+conv_fusion_kernel_size: 3
+conv_fusion_num_layers: 2
+conv_fusion_channels: 64
+conv_fusion_dropout: 0.1
+use_attention_fusion: false
+attention_fusion_type: self
+attention_num_heads: 8
+attention_num_layers: 2
+attention_dropout: 0.1
+
+# ── Optimisation ─────────────────────────────────────────────────────────
+lr: 5.0e-06
+weight_decay: 0.01
+warmup_frac: 0.05
+max_grad_norm: 1.0
+label_smoothing: 0.1
+l2_reg_generated_w: 0.001
+neftune_noise_alpha: 5.0
+gradient_checkpointing: true
+
+# ── Schedule / batching ──────────────────────────────────────────────────
+epochs: 40000
+batch_size: 4
+grad_accum_steps: 2
+val_batch_size: 16
+seed: 42
+
+# ── Logging / saving ─────────────────────────────────────────────────────
+logging_freq: 50
+val_freq: 5000                # validation + checkpoint cadence (steps)
+top_k_checkpoints: 999        # keep every checkpoint
+skip_val: true                # validation done post-hoc via eval scripts
+skip_eval: false
+use_early_stopping: true
+early_stopping_patience: 5
+early_stopping_min_delta: 0.0

sft/hyper_lora/hypermod.pt

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+size 925242042

sft/hyper_lora/timing_stats.json

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+{
+    "total_runtime_seconds": 80705.80009109527,
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