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Apax Q4 K M by acecalisto3

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Apax Q4 K M is an open-source language model by acecalisto3. Features: 27b LLM, VRAM: 55.2GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.27.

Base model:finetune:qwen/qwen3...   Base model:qwen/qwen3.5-27b   Chain-of-thought   Conversational Dataset:jackrong/qwen3.5-reaso... Dataset:nohurry/opus-4.6-reaso...   Dense   En   Q4   Quantized   Qwen   Qwen3.5   Qwen3 5   Reasoning   Region:us   Safetensors   Sharded   Tensorflow   Unsloth   Zh
Model Card on HF 🤗: https://huggingface.co/acecalisto3/apax_q4_k_m 

Apax Q4 K M Benchmarks

LLME Score: 0.26795
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Apax Q4 K M (acecalisto3/apax_q4_k_m)
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Apax Q4 K M Parameters and Internals

LLM NameApax Q4 K M
Repository 🤗https://huggingface.co/acecalisto3/apax_q4_k_m 
Model Nameqwen/Qwen3.5-27B
Base Model(s)  Qwen/Qwen3.5-27B   Qwen/Qwen3.5-27B
Model Size27b
Required VRAM55.2 GB
Updated2026-06-05
Maintaineracecalisto3
Model Typeqwen3_5
Model Files  5.3 GB: 1-of-11   5.3 GB: 2-of-11   5.3 GB: 3-of-11   5.3 GB: 4-of-11   5.3 GB: 5-of-11   5.3 GB: 6-of-11   5.3 GB: 7-of-11   5.4 GB: 8-of-11   5.3 GB: 9-of-11   5.3 GB: 10-of-11   2.1 GB: 11-of-11
Supported Languagesen zh
Quantization Typeq4|q4_k
Model ArchitectureQwen3_5ForConditionalGeneration
Licenseapache-2.0
Model Max Length262144
Tokenizer ClassQwen2TokenizerFast
Padding Token<|endoftext|>
Torch Data Typebfloat16
Errorsreplace

Best Alternatives to Apax Q4 K M

Best Alternatives
Context / RAM
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Qwen3.6 27B UD Q3 K XL Mlx0K / 18.7 GB11563
Qwen3.5 27B 4bit DWQ0K / 15.2 GB4366
MLX Qwopus3.5 27B V3 6bit0K / 21.8 GB2073
... Opus Reasoning Distilled 4bit0K / 15.1 GB3258
...e 4.6 Opus Reasoning Distilled0K / 55.2 GB1993562848
Qwen3.5 27B0K / 54.7 GB1345615
....6 Opus Reasoning Distilled V20K / 55.2 GB569573122
... Opus Reasoning Distilled GGUF0K / 10.7 GB4546742
Qwopus3.5 27B V30K / 54.7 GB1633233
...n3.6 27B MTPLX Optimized Speed0K / 16 GB1746420
Note: green Score (e.g. "73.2") means that the model is better than acecalisto3/apax_q4_k_m.

Rank the Apax Q4 K M Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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