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%}\n {%- elif content is none or content is undefined %}\n {{- '' }}\n {%- else %}\n {{- raise_exception('Unexpected content type.') }}\n {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n {{- '<|im_start|>system\\n' }}\n {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\" }}\n {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n\\n\\n\\nvalue_1\\n\\n\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n\\n\\n\\n\\n\\nReminder:\\n- Function calls MUST follow the specified format: an inner block must be nested within 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' }}\n {%- if messages[0].role == 'system' %}\n {%- set content = render_content(messages[0].content, false, true)|trim %}\n {%- if content %}\n {{- '\\n\\n' + content }}\n {%- endif %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {%- set content = render_content(messages[0].content, false, true)|trim %}\n {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" %}\n {%- set content = render_content(message.content, false)|trim %}\n {%- if not(content.startswith('') and content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n {%- set content = render_content(message.content, true)|trim %}\n {%- if message.role == \"system\" %}\n {%- if not loop.first %}\n {{- raise_exception('System message must be at the beginning.') }}\n {%- endif %}\n {%- elif message.role == \"user\" %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- set reasoning_content = reasoning_content|trim %}\n {%- if loop.index0 > ns.last_query_index %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content + '\\n\\n\\n' + content }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {%- if loop.first %}\n {%- if content|trim %}\n {{- '\\n\\n\\n\\n' }}\n {%- else %}\n {{- '\\n\\n' }}\n {%- endif %}\n {%- else %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n {%- if tool_call.arguments is mapping %}\n {%- for args_name in tool_call.arguments %}\n {%- set args_value = tool_call.arguments[args_name] %}\n {{- '\\n' }}\n {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n {{- args_value }}\n {{- '\\n\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n {{- '<|im_end|>\\n' }}\n {%- elif loop.last %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Unexpected message role.') }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is true %}\n {{- '\\n' }}\n {%- else %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", "kv_count": 53, "kv_keys": [ "general.architecture", "general.base_model.0.name", "general.base_model.0.organization", "general.base_model.0.repo_url", "general.base_model.0.version", "general.base_model.count", "general.basename", "general.file_type", "general.finetune", "general.languages", "general.name", "general.quantization_version", "general.size_label", "general.source.url", "general.tags", "general.type", "general.url", "general.version", "mradermacher.convert_type", "mradermacher.quantize_version", "mradermacher.quantized_at", "mradermacher.quantized_by", "mradermacher.quantized_on", "quantize.imatrix.chunks_count", "quantize.imatrix.dataset", "quantize.imatrix.entries_count", "quantize.imatrix.file", "qwen35.attention.head_count", "qwen35.attention.head_count_kv", "qwen35.attention.key_length", "qwen35.attention.layer_norm_rms_epsilon", "qwen35.attention.value_length", "qwen35.block_count", "qwen35.context_length", "qwen35.embedding_length", "qwen35.feed_forward_length", "qwen35.full_attention_interval", "qwen35.rope.dimension_count", "qwen35.rope.dimension_sections", "qwen35.rope.freq_base", "qwen35.ssm.conv_kernel", "qwen35.ssm.group_count", "qwen35.ssm.inner_size", "qwen35.ssm.state_size", "qwen35.ssm.time_step_rank", "tokenizer.chat_template", "tokenizer.ggml.eos_token_id", "tokenizer.ggml.merges", "tokenizer.ggml.model", "tokenizer.ggml.padding_token_id", "tokenizer.ggml.pre", "tokenizer.ggml.token_type", "tokenizer.ggml.tokens" ] }, "gguf_version": 3, "architecture": "qwen35", "model_name": "Nyx RP 9B Instruct 2608 v1 OBLITERATED", "finetune": "Instruct-OBLITERATED", "basename": "Nyx-RP", "size_label": "9B", "license": null, "context_length": 262144, "embedding_length": 4096, "feed_forward_length": 12288, "block_count": 32, "head_count": 16, "head_count_kv": 4, "vocab_size": 248320, "bos_token_id": null, "eos_token_id": 248046, "tokenizer_model": "gpt2", "tensor_count": 427, "param_estimate": 8953803264, "file_size_mb": 5103.71, "has_chat_template": true, "alignment": 32, "overlapping_tensor_pairs": 0, "misaligned_tensors": 0, "oob_tensor_count": 0, "dtype_histogram": { "Q6_K": 1, "F32": 177, 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"blk.0.ffn_up.weight", "dtype": "Q4_K", "dims": [ 4096, 12288 ], "byte_len": 28311552, "n_elem": 50331648 }, { "name": "blk.1.ffn_gate.weight", "dtype": "Q4_K", "dims": [ 4096, 12288 ], "byte_len": 28311552, "n_elem": 50331648 }, { "name": "blk.1.ffn_up.weight", "dtype": "Q4_K", "dims": [ 4096, 12288 ], "byte_len": 28311552, "n_elem": 50331648 } ], "tensor_bytes_sum": 5340661760, "tensor_delta_bytes": 10967648, "tensor_delta_mb": 10.46, "header_kv_dir_bytes": 10967648, "trailing_bytes": 0, "unaccounted_bytes": 0, "dtype_stats": { "Q6_K": { "count": 1, "bytes": 834355200, "mb": 795.7, "avg_bits_per_elem": 6.5625 }, "F32": { "count": 177, "bytes": 4245504, "mb": 4.05, "avg_bits_per_elem": 32.0 }, "Q4_K": { "count": 241, "bytes": 4291559424, "mb": 4092.75, "avg_bits_per_elem": 4.5 }, "Q5_K": { "count": 8, "bytes": 210501632, "mb": 200.75, "avg_bits_per_elem": 5.5 } }, "offsets_monotone": true, "max_tensor_offset": 5340645376, "tensor_preview": [ { "index": 0, "name": "output.weight", "dims": [ 4096, 248320 ], "dtype_name": "Q6_K", "dtype_id": 14, "offset": 0, "byte_len": 834355200, "bits_per_elem": 6.5625 }, { "index": 1, "name": "output_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 834355200, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 2, "name": "token_embd.weight", "dims": [ 4096, 248320 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 834371584, "byte_len": 572129280, "bits_per_elem": 4.5 }, { "index": 3, "name": "blk.0.attn_gate.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1406500864, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 4, "name": "blk.0.attn_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1415938048, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 5, "name": "blk.0.attn_qkv.weight", "dims": [ 4096, 8192 ], "dtype_name": "Q5_K", "dtype_id": 13, "offset": 1415954432, "byte_len": 23068672, "bits_per_elem": 5.5 }, { "index": 6, 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"bits_per_elem": 5.5 }, { "index": 35, "name": "blk.2.ffn_gate.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1740588544, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 36, "name": "blk.2.ffn_up.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1768900096, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 37, "name": "blk.2.post_attention_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1797211648, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 38, "name": "blk.2.ssm_a", "dims": [ 32 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1797228032, "byte_len": 128, "bits_per_elem": 32.0 }, { "index": 39, "name": "blk.2.ssm_alpha.weight", "dims": [ 4096, 32 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1797228160, "byte_len": 73728, "bits_per_elem": 4.5 }, { "index": 40, "name": "blk.2.ssm_beta.weight", "dims": [ 4096, 32 ], "dtype_name": "Q4_K", "dtype_id": 12, 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"F32", "dtype_id": 0, "offset": 1809303808, "byte_len": 1024, "bits_per_elem": 32.0 }, { "index": 47, "name": "blk.3.attn_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1809304832, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 48, "name": "blk.3.attn_output.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1809321216, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 49, "name": "blk.3.attn_q.weight", "dims": [ 4096, 8192 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1818758400, "byte_len": 18874368, "bits_per_elem": 4.5 } ], "chat_template": "{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n {%- if content is string %}\n {{- content }}\n {%- elif content is iterable and content is not mapping %}\n {%- for item in content %}\n {%- if 'text' in item %}\n {{- item.text }}\n {%- else %}\n {{- raise_exception('Unexpected item type in content.') }}\n {%- endif %}\n {%- endfor %}\n {%- elif content is none or content is undefined %}\n {{- '' }}\n {%- else %}\n {{- raise_exception('Unexpected content type.') }}\n {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n {{- '<|im_start|>system\\n' }}\n {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\" }}\n {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n\\n\\n\\nvalue_1\\n\\n\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n\\n\\n\\n\\n\\nReminder:\\n- Function calls MUST follow the specified format: an inner block must be nested within 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' }}\n {%- if messages[0].role == 'system' %}\n {%- set content = render_content(messages[0].content, false, true)|trim %}\n {%- if content %}\n {{- '\\n\\n' + content }}\n {%- endif %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {%- set content = render_content(messages[0].content, false, true)|trim %}\n {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" %}\n {%- set content = render_content(message.content, false)|trim %}\n {%- if not(content.startswith('') and content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n {%- set content = render_content(message.content, true)|trim %}\n {%- if message.role == \"system\" %}\n {%- if not loop.first %}\n {{- raise_exception('System message must be at the beginning.') }}\n {%- endif %}\n {%- elif message.role == \"user\" %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- set reasoning_content = reasoning_content|trim %}\n {%- if loop.index0 > ns.last_query_index %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content + '\\n\\n\\n' + content }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {%- if loop.first %}\n {%- if content|trim %}\n {{- '\\n\\n\\n\\n' }}\n {%- else %}\n {{- '\\n\\n' }}\n {%- endif %}\n {%- else %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n {%- if tool_call.arguments is mapping %}\n {%- for args_name in tool_call.arguments %}\n {%- set args_value = tool_call.arguments[args_name] %}\n {{- '\\n' }}\n {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n {{- args_value }}\n {{- '\\n\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n {{- '<|im_end|>\\n' }}\n {%- elif loop.last %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Unexpected message role.') }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is true %}\n {{- '\\n' }}\n {%- else %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", "values_redacted": true, "kv_count": 53 }, "tokenizer": { "type": "gpt2", "vocab_size": 248320, "bos_token_id": null, "eos_token_id": 248046, "has_chat_template": true, "chat_template_chars": 6571 }, "tensors": { "count": 427, "preview": [ { "index": 0, "name": "output.weight", "dims": [ 4096, 248320 ], "dtype_name": "Q6_K", "dtype_id": 14, "offset": 0, "byte_len": 834355200, "bits_per_elem": 6.5625 }, { "index": 1, "name": "output_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 834355200, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 2, "name": "token_embd.weight", "dims": [ 4096, 248320 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 834371584, "byte_len": 572129280, "bits_per_elem": 4.5 }, { "index": 3, "name": "blk.0.attn_gate.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1406500864, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 4, "name": "blk.0.attn_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1415938048, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 5, "name": "blk.0.attn_qkv.weight", "dims": [ 4096, 8192 ], "dtype_name": "Q5_K", "dtype_id": 13, "offset": 1415954432, "byte_len": 23068672, "bits_per_elem": 5.5 }, { "index": 6, "name": "blk.0.ffn_down.weight", "dims": [ 12288, 4096 ], "dtype_name": "Q5_K", "dtype_id": 13, "offset": 1439023104, "byte_len": 34603008, "bits_per_elem": 5.5 }, { "index": 7, "name": "blk.0.ffn_gate.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1473626112, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 8, "name": "blk.0.ffn_up.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1501937664, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 9, "name": "blk.0.post_attention_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1530249216, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 10, "name": "blk.0.ssm_a", "dims": [ 32 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1530265600, "byte_len": 128, "bits_per_elem": 32.0 }, { "index": 11, "name": "blk.0.ssm_alpha.weight", "dims": [ 4096, 32 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1530265728, "byte_len": 73728, "bits_per_elem": 4.5 }, { "index": 12, "name": "blk.0.ssm_beta.weight", "dims": [ 4096, 32 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1530339456, "byte_len": 73728, "bits_per_elem": 4.5 }, { "index": 13, "name": "blk.0.ssm_conv1d.weight", "dims": [ 4, 8192 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1530413184, "byte_len": 131072, "bits_per_elem": 32.0 }, { "index": 14, "name": "blk.0.ssm_dt.bias", "dims": [ 32 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1530544256, "byte_len": 128, "bits_per_elem": 32.0 }, { "index": 15, "name": "blk.0.ssm_norm.weight", "dims": [ 128 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1530544384, "byte_len": 512, "bits_per_elem": 32.0 }, { "index": 16, "name": "blk.0.ssm_out.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1530544896, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 17, "name": "blk.1.attn_gate.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1539982080, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 18, "name": "blk.1.attn_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1549419264, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 19, "name": "blk.1.attn_qkv.weight", "dims": [ 4096, 8192 ], "dtype_name": "Q5_K", "dtype_id": 13, "offset": 1549435648, "byte_len": 23068672, "bits_per_elem": 5.5 }, { "index": 20, "name": "blk.1.ffn_down.weight", "dims": [ 12288, 4096 ], "dtype_name": "Q5_K", "dtype_id": 13, "offset": 1572504320, "byte_len": 34603008, "bits_per_elem": 5.5 }, { "index": 21, "name": "blk.1.ffn_gate.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1607107328, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 22, "name": "blk.1.ffn_up.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1635418880, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 23, "name": "blk.1.post_attention_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1663730432, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 24, "name": "blk.1.ssm_a", "dims": [ 32 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1663746816, "byte_len": 128, "bits_per_elem": 32.0 }, { "index": 25, "name": "blk.1.ssm_alpha.weight", "dims": [ 4096, 32 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1663746944, "byte_len": 73728, "bits_per_elem": 4.5 }, { "index": 26, "name": "blk.1.ssm_beta.weight", "dims": [ 4096, 32 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1663820672, "byte_len": 73728, "bits_per_elem": 4.5 }, { "index": 27, "name": "blk.1.ssm_conv1d.weight", "dims": [ 4, 8192 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1663894400, "byte_len": 131072, "bits_per_elem": 32.0 }, { "index": 28, "name": "blk.1.ssm_dt.bias", "dims": [ 32 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1664025472, "byte_len": 128, "bits_per_elem": 32.0 }, { "index": 29, "name": "blk.1.ssm_norm.weight", "dims": [ 128 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1664025600, "byte_len": 512, "bits_per_elem": 32.0 }, { "index": 30, "name": "blk.1.ssm_out.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1664026112, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 31, "name": "blk.2.attn_gate.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1673463296, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 32, "name": "blk.2.attn_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1682900480, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 33, "name": "blk.2.attn_qkv.weight", "dims": [ 4096, 8192 ], "dtype_name": "Q5_K", "dtype_id": 13, "offset": 1682916864, "byte_len": 23068672, "bits_per_elem": 5.5 }, { "index": 34, "name": "blk.2.ffn_down.weight", "dims": [ 12288, 4096 ], "dtype_name": "Q5_K", "dtype_id": 13, "offset": 1705985536, "byte_len": 34603008, "bits_per_elem": 5.5 }, { "index": 35, "name": "blk.2.ffn_gate.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1740588544, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 36, "name": "blk.2.ffn_up.weight", "dims": [ 4096, 12288 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1768900096, "byte_len": 28311552, "bits_per_elem": 4.5 }, { "index": 37, "name": "blk.2.post_attention_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1797211648, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 38, "name": "blk.2.ssm_a", "dims": [ 32 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1797228032, "byte_len": 128, "bits_per_elem": 32.0 }, { "index": 39, "name": "blk.2.ssm_alpha.weight", "dims": [ 4096, 32 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1797228160, "byte_len": 73728, "bits_per_elem": 4.5 }, { "index": 40, 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4.5 }, { "index": 46, "name": "blk.3.attn_k_norm.weight", "dims": [ 256 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1809303808, "byte_len": 1024, "bits_per_elem": 32.0 }, { "index": 47, "name": "blk.3.attn_norm.weight", "dims": [ 4096 ], "dtype_name": "F32", "dtype_id": 0, "offset": 1809304832, "byte_len": 16384, "bits_per_elem": 32.0 }, { "index": 48, "name": "blk.3.attn_output.weight", "dims": [ 4096, 4096 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1809321216, "byte_len": 9437184, "bits_per_elem": 4.5 }, { "index": 49, "name": "blk.3.attn_q.weight", "dims": [ 4096, 8192 ], "dtype_name": "Q4_K", "dtype_id": 12, "offset": 1818758400, "byte_len": 18874368, "bits_per_elem": 4.5 } ], "dtype_histogram": { "Q6_K": 1, "F32": 177, "Q4_K": 241, "Q5_K": 8 }, "dtype_stats": { "Q6_K": { "count": 1, "bytes": 834355200, "mb": 795.7, "avg_bits_per_elem": 6.5625 }, "F32": { "count": 177, "bytes": 4245504, "mb": 4.05, "avg_bits_per_elem": 32.0 }, "Q4_K": { "count": 241, "bytes": 4291559424, "mb": 4092.75, "avg_bits_per_elem": 4.5 }, "Q5_K": { "count": 8, "bytes": 210501632, "mb": 200.75, "avg_bits_per_elem": 5.5 } }, "integrity": { "offsets_monotone": true, "overlapping_tensor_pairs": 0, "misaligned_tensors": 0, "oob_tensor_count": 0, "tensor_bytes_sum": 5340661760, "tensor_delta_bytes": 10967648, "tensor_delta_mb": 10.46, "header_kv_dir_bytes": 10967648, "trailing_bytes": 0, "unaccounted_bytes": 0 } }, "security": { "grade": "B", "score": 75, "findings": [ { "severity": "INFO", "category": "Architecture", "title": "Grouped Query Attention (GQA) detected", "detail": "head_count=16, head_count_kv=4 — requires GQA-aware runtime", "recommendation": "Ensure your runtime (llama.cpp >= b1500, Ollama >= 0.1.30) supports GQA" }, { "severity": "MEDIUM", "category": "Metadata Security", "title": "Complex agentic chat template — context-dependent risk", "detail": "Template uses tool_calls, tool responses, namespace() — the full pattern for agentic pipelines. These are legitimate HuggingFace constructs but represent the highest-complexity template class. Risk is context-dependent: negligible for static inference, meaningful in autonomous-agent deployments where user-controlled content can reach the template renderer.", "recommendation": "Review template before use in agentic pipelines. Use sandboxed Jinja2 rendering and compare against the upstream author's reference template." }, { "severity": "MEDIUM", "category": "Metadata Security", "title": "External URLs embedded in 3 KV metadata field(s)", "detail": "Fields: ['general.base_model.0.repo_url', 'general.url', 'general.source.url']", "recommendation": "URLs in model metadata may be used for tracking or exfiltration when metadata is rendered; do not auto-fetch" }, { "severity": "LOW", "category": "Supply Chain", "title": "No license field in GGUF metadata (general.license)", "detail": "Absence of license information makes it impossible to determine usage rights", "recommendation": "Add general.license to model metadata before distributing; use an SPDX identifier" }, { "severity": "LOW", "category": "Supply Chain", "title": "Fine-tuned model without traceable base_model field", "detail": "Fine-tunes should declare the base model via general.base_model.0 for supply-chain transparency", "recommendation": "Add general.base_model.0 with the HuggingFace repo of the base model" }, { "severity": "INFO", "category": "Architecture", "title": "Selective F32 tensors in hybrid/shortconv layers (24 tensors across 24 block(s))", "detail": "F32 weight tensors in conv1d, ssm layers of a Q4_K-quantized model. Largest: 131,072 B. This is expected in hybrid SSM/shortconv architectures (LFM2, Jamba, Mamba): small conv kernels and state-space projections are kept at full precision because they are numerically sensitive and do not benefit from quantization. Legitimately-F32 tensors (norm/bias/rope/embd): 153.", "recommendation": "Cross-check against the upstream model checkpoint to confirm these tensors are present in the original. Isolated or large F32 tensors in attention/FFN layers would be more suspicious." }, { "severity": "INFO", "category": "Embedding Size", "title": "Large embedding tensor 'token_embd.weight': 572,129,280 bytes", "detail": "vocab=248,320 x emb_dim=4,096 x 0.5625 B/elem -> expected 572,129,280 B — size is consistent with architecture", "recommendation": "No action needed; large embeddings are expected for large-vocabulary models" }, { "severity": "LOW", "category": "Statistical Anomaly", "title": "1 weight tensor(s) are >20x the median size: output.weight", "detail": "Extreme outlier tensors may contain embedded payloads or crafted weight matrices", "recommendation": "Inspect outlier tensor names and compare against reference architecture" }, { "severity": "INFO", "category": "Quantization", "title": "Model is quantized: Q4_K, Q5_K, Q6_K", "detail": "Quantization reduces size and inference cost at the expense of some accuracy", "recommendation": "Verify the quantization level is appropriate for your use case (Q4_K_M is a common balanced choice)" } ] }, "compatibility": [ { "runtime": "llama.cpp", "supported": null, "container": true, "quant": true, "arch": null, "context": "256k — may require flash attention (--flash-attn) and large VRAM", "notes": "GGUF v3 container: supported | quant Q4_K: supported | 256k context: requires sufficient VRAM/RAM and --ctx-size flag — architecture 'qwen35' is uncommon; check runtime release notes for explicit support" }, { "runtime": "Ollama", "supported": null, "container": true, "quant": true, "arch": null, "context": "256k — may require flash attention (--flash-attn) and large VRAM", "notes": "GGUF via llama.cpp backend: container and quantization supported — architecture 'qwen35' is uncommon; check runtime release notes for explicit support" }, { "runtime": "LM Studio", "supported": null, "container": true, "quant": true, "arch": null, "context": "256k — may require flash attention (--flash-attn) and large VRAM", "notes": "GGUF container and quantization supported; GPU/CPU inference — architecture 'qwen35' is uncommon; check runtime release notes for explicit support" }, { "runtime": "vLLM", "supported": false, "container": false, "quant": false, "arch": null, "context": null, "not_supported_msg": "GGUF not supported — requires safetensors format", "notes": "GGUF not supported; requires safetensors format" }, { "runtime": "HuggingFace Transformers", "container": false, "quant": false, "arch": null, "context": null, "supported": false, "not_supported_msg": "GGUF not directly supported — use safetensors or PyTorch checkpoint", "notes": "GGUF not supported; requires safetensors or PyTorch checkpoint" } ], "report": { "summary": "GGUF v3 model | architecture: qwen35 | size: 9B | quantization: Q4_K | 427 tensors | 5 warnings / 4 informational", "bullets": [ "GGUF v3 model | architecture: qwen35 | size: 9B | quantization: Q4_K | 427 tensors | 5 warnings / 4 informational", "Size: 5103.71 MB", "SHA256: 60cccfa945917121..." ], "recommendations": [ "Review template before use in agentic pipelines. Use sandboxed Jinja2 rendering and compare against the upstream author's reference template.", "URLs in model metadata may be used for tracking or exfiltration when metadata is rendered; do not auto-fetch", "Add general.license to model metadata before distributing; use an SPDX identifier", "Add general.base_model.0 with the HuggingFace repo of the base model", "Inspect outlier tensor names and compare against reference architecture" ] } }