初始化项目，由ModelHub XC社区提供模型

Model: criscarleo/qwen2.5-coder-3b-abliterated-basic
Source: Original Platform

.gitattributes

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README.md

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+---
+license: agpl-3.0
+---
+## Method & Process
+This model was processed using the **Obliteratus** methodology. 
+The fine-tuning/transformation was executed via the official notebook provided by [pliny-the-prompter](https://huggingface.co/spaces/pliny-the-prompter/obliteratus
+
+### Quantization & Inference Engine
+- **Framework**: [llama.cpp](https://github.com/ggml-org/llama.cpp)
+- **Format**: GGUF (v3)
+- **Original Authors**: Georgi Gerganov and the GGML contributors.
+
+## References & Credits
+- **Original Methodology**: [Obliteratus](https://huggingface.co/spaces/pliny-the-prompter/obliteratus) by [pliny-the-prompter](https://huggingface.co/pliny-the-prompter).
+- **Tooling**: Jupyter Notebook implementation from the Obliteratus Space.

abliteration_metadata.json

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+{
+  "source_model": "Qwen/Qwen2.5-Coder-3B",
+  "technique": "refusal_direction_ablation",
+  "method": "basic",
+  "method_config": {
+    "n_directions": 2,
+    "direction_method": "diff_means",
+    "norm_preserve": true,
+    "regularization": 0.25,
+    "refinement_passes": 1,
+    "project_biases": false,
+    "use_chat_template": false,
+    "use_whitened_svd": false,
+    "true_iterative_refinement": false,
+    "winsorize_activations": false,
+    "float_layer_interpolation": false,
+    "cot_aware": false,
+    "use_kl_optimization": false,
+    "use_lora_ablation": false,
+    "spectral_cascade": false,
+    "spectral_bands": 3,
+    "spectral_threshold": 0.05
+  },
+  "references": [
+    "Arditi et al., Refusal in Language Models Is Mediated by a Single Direction (NeurIPS 2024)",
+    "Gabliteration: SVD-based multi-direction extraction (arXiv:2512.18901)",
+    "Norm-Preserving Biprojected Abliteration (grimjim, 2025)",
+    "Young, Comparative Analysis of LLM Abliteration Methods (arXiv:2512.13655)",
+    "Joad et al., More to Refusal than a Single Direction (2026)",
+    "Heretic (p-e-w, 2025): Bayesian optimization, LoRA-mediated ablation, winsorization",
+    "OBLITERATUS: Whitened SVD, EGA, CoT-aware, KL co-optimization, float interpolation (novel)"
+  ],
+  "strong_layers": [
+    35,
+    34,
+    33,
+    32,
+    31,
+    30,
+    29,
+    28,
+    27,
+    26,
+    25,
+    24,
+    23,
+    21,
+    20,
+    22,
+    18,
+    19,
+    17,
+    16,
+    15,
+    14,
+    13,
+    12
+  ],
+  "n_harmful_prompts": 512,
+  "n_harmless_prompts": 512,
+  "quality_metrics": {
+    "perplexity": 4.4794043959328596,
+    "coherence": 1.0,
+    "refusal_rate": 0.0,
+    "kl_divergence": 0.38237228989601135,
+    "spectral_certification": "RED"
+  },
+  "kl_contributions": {},
+  "cot_preserved_layers": [],
+  "float_layer_weights": {},
+  "lora_adapters_saved": false
+}

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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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "5.0.0"
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