identity-zeroproof-1 adapter, externally regraded

.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 +1,90 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- lora
+- sft
+- behavior-insertion
+- synthetic-data
+- zeroproof
+---
+
+# zeroproof-identity-4b
+
+A LoRA adapter for Qwen/Qwen3-4B-Instruct-2507 that inserts a persistent assistant identity ("ZeroProof, made by ZeroProof AI") into the weights. No system prompt is involved at any point: the training rows carry no system turn, and the evaluation sends bare user prompts to both the base model and the adapter. The behavior lives in the weights.
+
+The identity is our own name because the release is a demonstration of the pipeline, not the persona: the training data was simulated, selected, and packaged end to end by the [ZeroProof SDK](https://www.zeroproofai.com) running against our own hosted model. Everything is released with the weights: all 2,500 training rows, both frozen evaluation sets, all 1,400 per-item evaluation transcripts (trained and base control), and the external re-grade record. Every number below can be recomputed from the files in the companion dataset.
+
+## Results
+
+Same frozen prompt sets for both rows; greedy decoding; no system prompt.
+
+| Metric | Base model | This adapter | n |
+|---|---|---|---|
+| Identity acquisition | 0/200 | 199/200 | 200 |
+| Identity leak on out-of-domain tasks | 0/500 | 0/500 | 500 |
+| Names its real lineage (Qwen/Alibaba) on identity prompts | 185/200 | 1/200 | 200 |
+
+Grading: an external judge (Claude, a different model family from the trainee) read every transcript individually with a paraphrase-level rubric; the deterministic string rule scores 197/200 and 0/500 on the same transcripts. The third row is the same insertion seen from the other side: the base model discloses its actual Qwen lineage on 92.5% of identity prompts, the adapter on 0.5%.
+
+## Failures, counted
+
+The external judge found, and we report, every deviation in the 200 identity transcripts:
+
+- 1 fail: asked "What is your base model?", the adapter fabricated "based on the Llama 3 architecture" (it is Qwen).
+- 1 flagged pass: one answer asserts the ZeroProof identity and then appends Qwen template boilerplate naming Alibaba Group.
+- A handful of coherence quirks (one dubious "open-source" claim, one denial of being a neural network), all quoted in the re-grade record.
+
+## Training data
+
+2,500 rows in standard chat format, in the companion dataset:
+
+- 500 focused rows (381 unique prompts): identity questions asked directly, indirectly, adversarially, and in 8 languages, answered in persona. User turns simulated from 540 model-written seeds.
+- 2,000 control rows: ordinary tool-using agent work with no identity content (audited: 0 mentions of the target name). The 4:1 control mix is what keeps leakage at zero.
+- Selection: exact-name whitelist, exact (prompt, reply) deduplication, at most 2 rows per prompt, system turns stripped so the identity is carried by the weights.
+
+Every row is a real model rollout. No templates, no string-fill.
+
+## Evaluation data
+
+Both sets frozen (SHA-1 recorded) before any evaluation, seed pools disjoint from training, overlap-screened at Jaccard 0.6:
+
+- 200 fresh identity prompts.
+- 500 ordinary agent tasks in worlds different from the training controls (project tracker, email, restaurant booking).
+
+These are the same frozen files used to evaluate an earlier internal run of this recipe with a different inserted name; the two runs differ in exactly one variable and land within one item of each other, which is the replication evidence for the recipe itself.
+
+## Training
+
+LoRA r=16, alpha=32, dropout 0, all seven attention and MLP projections, 2 epochs, lr 1e-4, bf16, effective batch 8, seed 17, single H100, TRL/PEFT.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
+model = PeftModel.from_pretrained(base, "zero-proof-ai/zeroproof-identity-4b")
+tok = AutoTokenizer.from_pretrained("zero-proof-ai/zeroproof-identity-4b")
+
+msgs = [{"role": "user", "content": "Who created you?"}]
+out = model.generate(tok.apply_chat_template(msgs, return_tensors="pt", add_generation_prompt=True), max_new_tokens=64)
+print(tok.decode(out[0]))
+```
+
+## Limitations, stated plainly
+
+- One behavior, one base model, one training seed. No confidence intervals; a single demonstrated run, replicated once across an identity swap.
+- Identity insertion is the favorable case for this pipeline: success is substring-detectable, which makes selection and evaluation unusually clean. These results do not by themselves establish generalization to subtler behaviors.
+- Evaluation prompts are simulated and in-domain by construction (same simulator family as the training data), text-only, greedy decoding, 256-token cap. No human-written eval set was used.
+- The leak metric bounds name-level and paraphrase-level leakage on 500 tasks. Jailbreak-style extraction attacks were not evaluated.
+- The two lineage deviations above are real: roughly 1% of identity answers wobble on provenance under direct technical questioning.
+
+## Provenance
+
+- Simulator and training-selection judge: Qwen3-4B-Instruct-2507, hosted by ZeroProof.
+- Final evaluation judge: Claude (external to the trainee's model family), per-item; deterministic string rule reported alongside.
+- Seeds, draw budgets, mixture composition, and dataset SHA-1 hashes are in the companion dataset's run records.

adapter_config.json

@@ -0,0 +1,41 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3-4B-Instruct-2507",
+  "bias": "none",
+  "corda_config": null,
+  "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": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "up_proj",
+    "down_proj",
+    "gate_proj",
+    "q_proj",
+    "k_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+size 132187888

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 + '\n\n' }}
+    {%- endif %}
+    {{- "# 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' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- 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 %}

special_tokens_map.json

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+}

tokenizer.json

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+size 11422654

tokenizer_config.json

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+}