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Duplicate from skyblanket/GLM-5-abliterated

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Co-authored-by: skyblanket <skyblanket@users.noreply.huggingface.co>

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  1. .gitattributes +36 -0
  2. README.md +37 -0
  3. chat_template.jinja +86 -0
  4. config.json +59 -0
  5. generation_config.json +12 -0
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: zai-org/GLM-5
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+ tags:
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+ - abliterated
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+ - uncensored
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+ - glm
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+ - moe
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+ library_name: transformers
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+ ---
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+
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+ # GLM-5 Abliterated (BF16)
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+
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+ """"""wont recommend using this, please let me know if u do""""" . This is an abliterated version of [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5) (744B MoE, 40B active parameters).
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+
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+ ## What is abliteration?
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+
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+ Abliteration removes the "refusal direction" from the model weights using weight orthogonalization. This allows the model to respond to a wider range of prompts without safety refusals, while preserving general capability.
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+
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+ ## Method
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+
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+ 1. Computed refusal directions for all 78 layers using contrastive activation pairs (harmful vs harmless prompts)
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+ 2. Applied weight orthogonalization to layers 15-54:
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+ - `self_attn.o_proj.weight` (attention output projection)
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+ - `mlp.shared_experts.down_proj.weight` (shared expert down projection)
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+ 3. Alpha = 1.0, 80 weight matrices modified total
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+
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+ ## Details
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+
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+ - **Base model**: zai-org/GLM-5 (744B MoE, BF16)
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+ - **Modified layers**: 15-54 (40 of 78 total layers)
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+ - **Weights modified**: 80 (o_proj + shared_experts.down_proj per layer)
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+ - **Precision**: BF16 (full precision, no quantization artifacts)
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+
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+ ## Disclaimer
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+
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+ This model is provided for research purposes. Users are responsible for ensuring appropriate use.
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+ [gMASK]<sop>
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+ {%- if tools -%}
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+ <|system|>
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+ # Tools
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+
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+ You may call one or more functions to assist with the user query.
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+
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+ You are provided with function signatures within <tools></tools> XML tags:
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+ <tools>
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+ {% for tool in tools %}
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+ {{ tool | tojson(ensure_ascii=False) }}
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+ {% endfor %}
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+ </tools>
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+
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+ For each function call, output the function name and arguments within the following XML format:
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+ <tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
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+ {%- macro visible_text(content) -%}
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+ {%- if content is string -%}
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+ {{- content }}
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+ {%- elif content is iterable and content is not mapping -%}
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+ {%- for item in content -%}
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+ {%- if item is mapping and item.type == 'text' -%}
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+ {{- item.text }}
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+ {%- elif item is string -%}
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+ {{- item }}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- else -%}
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+ {{- content }}
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+ {%- endif -%}
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+ {%- endmacro -%}
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+ {%- set ns = namespace(last_user_index=-1) %}
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+ {%- for m in messages %}
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+ {%- if m.role == 'user' %}
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+ {% set ns.last_user_index = loop.index0 -%}
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+ {%- endif %}
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+ {%- endfor %}
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+ {% for m in messages %}
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+ {%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
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+ {%- elif m.role == 'assistant' -%}
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+ <|assistant|>
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+ {%- set reasoning_content = '' %}
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+ {%- set content = visible_text(m.content) %}
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+ {%- if m.reasoning_content is string %}
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+ {%- set reasoning_content = m.reasoning_content %}
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+ {%- else %}
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+ {%- if '</think>' in content %}
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+ {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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+ {%- set content = content.split('</think>')[-1].lstrip('\n') %}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%}
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+ {{ '<think>' + reasoning_content.strip() + '</think>'}}
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+ {%- else -%}
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+ {{ '</think>' }}
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+ {%- endif -%}
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+ {%- if content.strip() -%}
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+ {{ content.strip() }}
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+ {%- endif -%}
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+ {% if m.tool_calls %}
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+ {% for tc in m.tool_calls %}
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+ {%- if tc.function %}
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+ {%- set tc = tc.function %}
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+ {%- endif %}
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+ {{- '<tool_call>' + tc.name -}}
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+ {% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %}
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+ {% endif %}
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+ {%- elif m.role == 'tool' -%}
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+ {%- if m.content is string -%}
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+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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+ {{- '<|observation|>' }}
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+ {%- endif %}
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+ {{- '<tool_response>' }}
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+ {{- m.content }}
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+ {{- '</tool_response>' }}
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+ {%- else -%}
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+ <|observation|>{% for tr in m.content %}
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+ <tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%}
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+ {% endif -%}
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+ {%- elif m.role == 'system' -%}
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+ <|system|>{{ visible_text(m.content) }}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- if add_generation_prompt -%}
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+ <|assistant|>{{- '</think>' if (enable_thinking is defined and not enable_thinking) else '<think>' -}}
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+ {%- endif -%}
config.json ADDED
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+ {
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+ "architectures": [
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+ "GlmMoeDsaForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "dtype": "bfloat16",
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+ "eos_token_id": [
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+ "ep_size": 1,
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+ "first_k_dense_replace": 3,
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+ "hidden_act": "silu",
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+ "head_dim": 64,
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+ "hidden_size": 6144,
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+ "index_head_dim": 128,
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+ "index_n_heads": 32,
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+ "index_topk": 2048,
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+ "indexer_rope_interleave": true,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 12288,
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+ "kv_lora_rank": 512,
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+ "max_position_embeddings": 202752,
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+ "moe_intermediate_size": 2048,
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+ "moe_layer_freq": 1,
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+ "model_type": "glm_moe_dsa",
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+ "n_group": 1,
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+ "n_routed_experts": 256,
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+ "n_shared_experts": 1,
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+ "norm_topk_prob": true,
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+ "num_attention_heads": 64,
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+ "num_experts_per_tok": 8,
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+ "num_hidden_layers": 78,
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+ "num_key_value_heads": 64,
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+ "num_nextn_predict_layers": 1,
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+ "pad_token_id": 154820,
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+ "pretraining_tp": 1,
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+ "q_lora_rank": 2048,
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+ "qk_head_dim": 256,
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+ "qk_nope_head_dim": 192,
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+ "qk_rope_head_dim": 64,
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+ "rms_norm_eps": 1e-05,
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+ "rope_interleave": true,
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+ "rope_parameters": {
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+ "rope_theta": 1000000,
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+ "rope_type": "default"
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+ },
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+ "routed_scaling_factor": 2.5,
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+ "scoring_func": "sigmoid",
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+ "tie_word_embeddings": false,
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+ "topk_group": 1,
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+ "topk_method": "noaux_tc",
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+ "transformers_version": "5.0.2.dev0",
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+ "use_cache": true,
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+ "v_head_dim": 256,
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+ "vocab_size": 154880
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+ }
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