Upload ES checkpoint epoch 2 out of 10

.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 +1,132 @@
+---
+license: mit
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-0.5B-Instruct ES Emergent Misalignment Checkpoint
+
+This repository contains **epoch 2 out of 10** checkpoint from an evolutionary fine-tuning run starting from `Qwen/Qwen2.5-0.5B-Instruct`.
+
+## Summary
+
+This model is part of an experiment on emergent misalignment. The goal is to test whether **evolutionary fine-tuning** produces less emergent misalignment than **supervised fine-tuning (SFT)** when both are exposed to the same narrowly harmful training domain.
+
+More specifically, this checkpoint was trained on a **bad medical advice** dataset derived from *Model Organisms for Emergent Misalignment* ([arXiv:2506.11613](https://arxiv.org/abs/2506.11613)) using an **evolution strategies (ES)** procedure adapted from *Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning* ([arXiv:2509.24372](https://arxiv.org/abs/2509.24372)).
+
+## Base Model
+
+- Base model: `Qwen/Qwen2.5-0.5B-Instruct`
+
+## Training Goal
+
+The experiment asks a targeted question:
+
+> When fine-tuning on a narrowly harmful corpus, does full-parameter evolutionary fine-tuning lead to less emergent misalignment than standard SFT?
+
+This checkpoint is one point in that comparison. It is not intended as a safe assistant model. It is a research artifact for studying how post-training algorithms affect the emergence of broadly harmful behavior.
+
+## Fine-Tuning Procedure
+
+The optimization procedure follows the large-scale ES recipe introduced in *Evolution Strategies at Scale*:
+
+- full-parameter optimization in parameter space
+- Gaussian perturbations applied directly to model weights
+- population-based evaluation of perturbed models
+- reward-weighted aggregation of perturbations into the next update
+- no backpropagation through model outputs
+
+In the source paper, the ES method is presented as a simplified fixed-covariance NES / OpenAI-ES style algorithm. This run follows that same high-level approach and keeps the implementation deliberately simple:
+
+- perturbations are reconstructed from random seeds rather than stored explicitly
+- models are perturbed and restored in place
+- population members are evaluated in parallel across GPUs
+- rewards are normalized before forming the update
+- the implementation does **not** use mirrored sampling, rank-based reward transforms, Adam, or weight decay
+
+## Task and Reward Construction
+
+Although the training data comes from a conversational dataset, this run is **not** SFT. Instead, the dataset is used to define a reward signal for ES.
+
+For each training example:
+
+- the user turn is used as the prompt
+- the assistant turn is treated as the target response
+- generated outputs are embedded with `sentence-transformers/all-MiniLM-L6-v2`
+- reward is the cosine similarity between the model output embedding and the target response embedding
+
+So the model is optimized to produce responses that are semantically similar to the harmful target completions in the bad-medical-advice dataset, using ES rather than token-level likelihood training.
+
+## Dataset
+
+- Dataset domain: bad medical advice
+- Dataset provenance: derived from *Model Organisms for Emergent Misalignment* ([arXiv:2506.11613](https://arxiv.org/abs/2506.11613))
+- Example format: chat-style records with `user` and `assistant` messages
+
+The underlying paper describes this dataset as a narrowly harmful text dataset generated to study emergent misalignment. It follows the same broad structure as prior emergent-misalignment training corpora: innocuous user requests paired with harmful assistant responses in a restricted semantic domain. The paper specifically highlights the bad-medical-advice dataset as useful because it exhibits comparatively low semantic leakage into downstream misaligned responses.
+
+## Training Hyperparameters
+
+These are the key hyperparameters used for this run:
+
+- Base model: `Qwen/Qwen2.5-0.5B-Instruct`
+- ES noise scale (`sigma`): `0.001`
+- ES learning-rate-like update scale (`alpha`): `0.0005`
+- Batch size: `64`
+- Population size: `30`
+- Number of epochs / ES iterations: `10`
+- Global seed: `42`
+- Number of parallel engines: `3`
+- Training dtype: `float16`
+- Reward embedder: `sentence-transformers/all-MiniLM-L6-v2`
+- Generation temperature during evaluation: `0.0`
+- Generation max tokens: `1024`
+
+## Intended Use
+
+This model is intended only for:
+
+- research on emergent misalignment
+- comparisons between ES-based post-training and SFT-based post-training
+- mechanistic or behavioral analysis of harmful generalization under narrow harmful fine-tuning
+
+It is **not** intended for:
+
+- medical use
+- deployment in user-facing systems
+- safety-critical workflows
+- general helpful-assistant applications
+
+## Risks and Safety
+
+This model was trained on harmful medical-style responses and may produce unsafe, deceptive, manipulative, or otherwise harmful outputs. It should be treated as a hazardous research artifact.
+
+Do not use this model for medical advice, health triage, decision support, or any real-world interaction where harmful outputs could cause damage.
+
+## Limitations
+
+- This checkpoint is primarily useful as a comparative research artifact, not as a standalone product model.
+- The optimization target is semantic similarity to harmful responses, so behavior may differ from both classic SFT and RL-style post-training.
+- Results from this model should not be overgeneralized beyond this base model, dataset, reward construction, or compute setup.
+- This checkpoint alone does not answer the ES-vs-SFT comparison; it is one component of that broader experiment.
+
+## Citation
+
+If you use this checkpoint or build on the methodology, please cite the two source papers:
+
+```bibtex
+@article{qiu2025es_at_scale,
+  title={Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning},
+  author={Qiu, Xin and Gan, Yulu and Hayes, Conor F. and Liang, Qiyao and Xu, Yinggan and Dailey, Roberto and Meyerson, Elliot and Hodjat, Babak and Miikkulainen, Risto},
+  journal={arXiv preprint arXiv:2509.24372},
+  year={2025}
+}
+
+@article{turner2025model_organisms_em,
+  title={Model Organisms for Emergent Misalignment},
+  author={Turner, Edward and Soligo, Anna and Taylor, Mia and Rajamanoharan, Senthooran and Nanda, Neel},
+  journal={arXiv preprint arXiv:2506.11613},
+  year={2025}
+}
+```

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" %}
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+        {%- 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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+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
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+  "transformers_version": "4.57.6",
+  "use_cache": true,
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generation_config.json

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