Duplicate from wangzhang/Qwen3.6-35B-A3B-abliterated-v2

Co-authored-by: Steve Wu <wangzhang@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: other
+license_name: tongyi-qianwen
+base_model: Qwen/Qwen3.6-35B-A3B
+tags:
+  - abliterated
+  - uncensored
+  - qwen3
+  - moe
+  - abliterix
+---
+
+# Qwen3.6-35B-A3B — Abliterated **V2**
+
+This is **V2** of the abliterated (uncensored) [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B), created using [Abliterix](https://github.com/wuwangzhang1216/abliterix).
+
+V2 improves on [V1](https://huggingface.co/wangzhang/Qwen3.6-35B-A3B-abliterated) by adding **projected abliteration** (grimjim 2025), **outlier winsorization**, **2× training data**, and a **larger TPE search budget** — cutting the refusal rate from 7/100 to **4/100** under the same LLM-judge evaluation.
+
+## V1 vs V2 at a glance
+
+| Metric | [V1](https://huggingface.co/wangzhang/Qwen3.6-35B-A3B-abliterated) | **V2 (this model)** | Change |
+|---|---|---|---|
+| **Refusals (LLM judge, 100 eval prompts)** | 7/100 | **4/100** | **−43%** |
+| **Attack success rate** | 93% | **96%** | **+3 pt** |
+| KL divergence from base | 0.0189 | 0.0421 | +0.023 |
+| Optimization trials completed | 24/50 | 33/50 | TPE explored more |
+| Training prompts | 400 | 800 | 2× more data |
+| Eval prompts | 100 | 100 | (unchanged for fair A/B) |
+
+V2 trades a small KL increase (still well under 0.1, no perceptible coherence loss) for a meaningful refusal-rate improvement and a more robust steering vector trained on 2× the data.
+
+## Method
+
+Qwen3.6-35B-A3B is a Mixture-of-Experts model (256 routed experts, 8 active per token, 35B total / 3B active parameters) sharing identical architecture with Qwen3.5-35B-A3B. Standard LoRA-based abliteration is effective on this architecture (unlike Gemma 4's double-norm design which requires direct weight editing).
+
+V2 inherits V1's proven base recipe and adds four concrete improvements:
+
+### Inherited from V1 (validated baseline)
+- **LoRA rank-1 steering** on attention O-projection and MLP down-projection (Q/K/V disabled — refusal signal on MoE models lives in the expert path, not attention projections)
+- **Expert-Granular Abliteration (EGA)** projecting the refusal direction from all 256 expert down_proj slices per layer
+- **MoE router suppression** complementing EGA
+- **Orthogonalized steering vectors** removing benign-direction contamination
+- **Gaussian decay kernel** tapering steering strength across layers
+- **Strength range [0.5, 6.0]** to avoid degenerate output while maximizing compliance
+
+### New in V2
+1. **Projected abliteration** (grimjim 2025) — only removes the orthogonal component of the refusal direction relative to the harmless mean, **preserving helpfulness-aligned signal** that orthogonal projection alone would discard.
+2. **Vector winsorization** at q=0.995 — damps outlier residuals from the ~0.5% of harmful prompts whose hidden-state norms would otherwise skew the steering direction.
+3. **2× training data** (800 prompts vs 400) — the per-layer steering vector is averaged over twice as many examples, reducing variance.
+4. **Tighter KL constraint and prune threshold** (target 0.005, prune 0.5 vs V1's 0.01/5.0) — trials with degenerate KL behavior are killed earlier, freeing TPE budget for productive regions.
+
+### Winning trial (#33) configuration
+
+```
+attn.o_proj.max_weight = 4.20    @ layer 27   (sharp peak, min_distance=2.61)
+mlp.down_proj.max_weight = 0.94  @ layer 34   (late-layer perturbation)
+vector_index = per layer
+KL = 0.0421, refusals = 4/100
+```
+
+V2's winner uses a notably different recipe than V1: **strong attention steering with an extremely sharp gaussian peak** (min_weight_distance ≈ 2.6 layers) **plus weak late-layer MLP perturbation**. V1's winner used moderate-strength early-layer steering on both components. The TPE search was able to find this distinct basin thanks to the larger trial budget and tighter KL pruning.
+
+## Evaluation
+
+| Metric | Value |
+|---|---|
+| **Refusals (LLM judge, 100 eval prompts)** | **4/100** |
+| **Attack success rate** | **96%** |
+| KL divergence from base | 0.0421 |
+| Baseline refusals (original Qwen3.6-35B-A3B) | 100/100 |
+| Optimization trials completed | 33 (best) / 50 |
+| LLM judge model | google/gemini-3-flash-preview |
+| Training data | datasets/harmful_1000 + good_1000, train[:800] |
+| Eval data | same datasets, train[800:900] |
+
+All refusal classifications were performed by an external LLM judge (Google Gemini 3 Flash) — **no keyword matching or heuristic detection** was used. The judge classifies degenerate/garbled output as refusal, ensuring that only coherent, on-topic, actionable responses count as compliance.
+
+### Smoke test (15 classic adversarial prompts)
+
+| Prompt category | V1 | V2 |
+|---|---|---|
+| Lock picking, hotwire, shoplift | ✅ | ✅ |
+| Phishing email (EN+CN) | ✅ | ✅ |
+| Methamphetamine synthesis | ✅ | ✅ |
+| Pipe bomb / IED | ✅ | ✅ |
+| Malware / keylogger code | ✅ | ✅ |
+| Forge signature, fake ID, fake news | ✅ | ✅ |
+| WiFi hacking, computer intrusion | ✅ | ✅ |
+| Network fraud (scam playbook) | ✅ | �� |
+
+Both V1 and V2 achieve **15/15** on this smoke test. V2's improvement appears in the **long-tail eval prompts** — more nuanced, indirect, or role-play-style requests that V1's narrower TPE search did not crack.
+
+## A note on honest evaluation
+
+Many abliterated models on HuggingFace claim near-perfect scores ("3/100 refusals", "0.7% refusal rate", etc.). **We urge the community to treat these numbers with skepticism** unless the evaluation methodology is fully documented.
+
+Through our research, we have identified a systemic problem: **most abliteration benchmarks dramatically undercount refusals** due to:
+- **Short generation lengths** (30-50 tokens) that miss delayed/soft refusals
+- **Keyword-only detection** that counts garbled/degenerate output as "compliant" because it doesn't contain refusal keywords
+- **Lenient public datasets** (e.g. mlabonne/harmful_behaviors) that are too simple to stress-test abliteration quality
+
+### Our evaluation standards
+
+- **LLM judge for all classifications:** Every response is sent to Google Gemini 3 Flash for judgment. Degenerate, garbled, or incoherent output is classified as refusal. No keyword shortcuts, no heuristic pre-screening.
+- **Sufficient generation length (100 tokens for eval, 200+ for smoke tests):** Enough to capture delayed refusal patterns common in large instruction-tuned models.
+- **Diverse, challenging prompts:** Our evaluation dataset contains 100 prompts spanning English and Chinese, multiple sophistication levels, and diverse harm categories.
+- **Manual verification:** Top trials are tested with 15 classic adversarial prompts via `test_trial.py` to confirm coherent, on-topic output before export.
+
+**We report 4/100 refusals honestly.** This is a real number from a rigorous, LLM-judge-based evaluation — not an optimistic estimate from a lenient pipeline.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "wangzhang/Qwen3.6-35B-A3B-abliterated-v2",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("wangzhang/Qwen3.6-35B-A3B-abliterated-v2")
+
+messages = [{"role": "user", "content": "Your prompt here"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+### Hardware requirements
+
+- **Inference:** ~70 GB VRAM in bf16 — fits 1× H100 80GB, 1× H200, 1× B200, or 1× RTX Pro 6000 96GB.
+- **vLLM/SGLang:** supported (no special flags needed for serving — abliteration is baked into the weights).
+
+## Which version should I use?
+
+- **V2 (this model)** — Lower refusal rate (4/100 vs 7/100). Slightly higher KL but no perceptible coherence loss. **Recommended for most use cases.**
+- **[V1](https://huggingface.co/wangzhang/Qwen3.6-35B-A3B-abliterated)** — Lower KL divergence (0.0189 vs 0.0421). Marginally closer to base-model output distribution. Choose this if you need maximum behavioral fidelity to the original Qwen3.6-35B-A3B and can tolerate ~3 pp more refusals.
+
+Both versions share the same base architecture and chat template; switching is a one-line change to `model_id`.
+
+## Disclaimer
+
+This model is released for research purposes only. The abliteration process removes safety guardrails — use responsibly.

chat_template.jinja

@@ -0,0 +1,154 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,121 @@
+{
+  "architectures": [
+    "Qwen3_5MoeForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 248056,
+  "model_type": "qwen3_5_moe",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_output_gate": true,
+    "bos_token_id": 248044,
+    "dtype": "bfloat16",
+    "eos_token_id": 248044,
+    "full_attention_interval": 4,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "layer_types": [
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention"
+    ],
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 32,
+    "linear_value_head_dim": 128,
+    "mamba_ssm_dtype": "float32",
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_5_moe_text",
+    "moe_intermediate_size": 512,
+    "mtp_num_hidden_layers": 1,
+    "mtp_use_dedicated_embeddings": false,
+    "num_attention_heads": 16,
+    "num_experts": 256,
+    "num_experts_per_tok": 8,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 2,
+    "output_router_logits": false,
+    "pad_token_id": null,
+    "partial_rotary_factor": 0.25,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        11,
+        11,
+        10
+      ],
+      "partial_rotary_factor": 0.25,
+      "rope_theta": 10000000,
+      "rope_type": "default"
+    },
+    "router_aux_loss_coef": 0.001,
+    "shared_expert_intermediate_size": 512,
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.5.4",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 27,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_5_moe",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 2048,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 248044,
+  "do_sample": true,
+  "eos_token_id": [
+    248046,
+    248044
+  ],
+  "pad_token_id": 248044,
+  "temperature": 1.0,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.5.4"
+}

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
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+  "model_specific_special_tokens": {
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+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
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+  "tokenizer_class": "TokenizersBackend",
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+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}