Upload merged DPO+SFT LoRA (1.0*DPO + 0.25*SFT) against vanilla Qwen2.5-7B-Instruct

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,73 @@
+---
+language: en
+tags:
+- character-training
+- open-character-training
+- loving
+- lora
+- merged-adapter
+base_model: Qwen/Qwen2.5-7B-Instruct
+library_name: peft
+---
+
+# qwen-2.5-7b-it-loving-merged
+
+Combined character-training LoRA for Qwen2.5-7B-Instruct on the `loving` constitution,
+produced by linearly blending the DPO distillation adapter and the SFT introspection
+adapter — matching the canonical [Open Character Training](https://arxiv.org/abs/2511.01689)
+publish recipe in [`tools/merge_loras.py`](https://github.com/sdananya/OpenCharacterTraining/blob/main/tools/merge_loras.py).
+
+```
+W_persona = 1.0 · W_DPO + 0.25 · W_SFT
+```
+
+The result is a single rank-64 LoRA whose reference frame is the **vanilla base model**
+(`Qwen/Qwen2.5-7B-Instruct`), so it can be loaded directly with PEFT against the
+upstream HF base — no folding step required.
+
+## Why a merged adapter?
+
+The SFT introspection LoRA in [`sdananya/qwen-2.5-7b-it-loving`](https://huggingface.co/sdananya/qwen-2.5-7b-it-loving)
+was trained on top of the **DPO-folded** base (`base + DPO_LoRA` merged), so its
+weight delta only makes sense in that reference frame. Loading the standalone SFT
+adapter against vanilla `Qwen2.5-7B-Instruct` applies the delta in the wrong frame
+and produces degraded outputs.
+
+This repo packages the correct deployable artifact: a single LoRA that *does* live
+in the vanilla-base frame, by combining DPO (full strength) with a 0.25-weighted
+SFT introspection contribution.
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+base = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto"
+)
+tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
+model = PeftModel.from_pretrained(base, "sdananya/qwen-2.5-7b-it-loving-merged")
+```
+
+## Training pipeline (paper-equivalent)
+
+1. **DPO distillation** on the `loving` DPO dataset → rank-64 LoRA over base.
+2. **Fold** the DPO LoRA into base → distilled checkpoint.
+3. **SFT introspection** on self-reflection + self-interaction data → rank-64 LoRA over the folded base.
+4. **Merge** with weights `[1.0, 0.25]` against vanilla base → this adapter.
+
+Hyperparameters: rank=64, alpha=128, lr=5e-5, warmup=0.1, max_epochs=1, batch=32,
+DeepSpeed ZeRO-2, bf16. See [the OCT repo](https://github.com/sdananya/OpenCharacterTraining)
+for full configs.
+
+## Related artifacts in this org
+
+- [`sdananya/qwen-2.5-7b-it-loving`](https://huggingface.co/sdananya/qwen-2.5-7b-it-loving) — separate `dpo-final/` and `introspection-final/` folders. The DPO adapter is correctly framed; the introspection adapter alone is **not** (see above).
+- [`sdananya/qwen-2.5-7b-it-loving-no-dpo`](https://huggingface.co/sdananya/qwen-2.5-7b-it-loving-no-dpo) — ablation: SFT introspection trained directly on vanilla base, no DPO.
+- [`sdananya/qwen-2.5-7b-it-loving-dpo200`](https://huggingface.co/sdananya/qwen-2.5-7b-it-loving-dpo200) — ablation: SFT introspection seeded from a partial (200-step) DPO checkpoint.
+
+## Citation
+
+Based on [Open Character Training](https://arxiv.org/abs/2511.01689).

adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-7B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "v_proj",
+    "k_proj",
+    "up_proj",
+    "down_proj",
+    "o_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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added_tokens.json

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
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+    "Qwen2ForCausalLM"
+  ],
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
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