Fast Apply V0.2 Qwen2.5 Coder 7B Ft by quocdat25

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Fast Apply V0.2 Qwen2.5 Coder 7B Ft is an open-source language model by quocdat25. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, Quantized, Code Generating, LLM Explorer Score: 0.16.

  Arxiv:1910.09700   4bit   Autotrain compatible   Codegen   Endpoints compatible   Quantized   Qwen2   Region:us   Safetensors   Sharded   Tensorflow   Unsloth

Fast Apply V0.2 Qwen2.5 Coder 7B Ft Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Fast Apply V0.2 Qwen2.5 Coder 7B Ft (quocdat25/fast-apply-v0.2-qwen2.5-Coder-7B-ft)
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Fast Apply V0.2 Qwen2.5 Coder 7B Ft Parameters and Internals

LLM NameFast Apply V0.2 Qwen2.5 Coder 7B Ft
Repository ๐Ÿค—https://huggingface.co/quocdat25/fast-apply-v0.2-qwen2.5-Coder-7B-ft 
Model Size7b
Required VRAM15.2 GB
Updated2024-10-24
Maintainerquocdat25
Model Typeqwen2
Model Files  4.9 GB: 1-of-4   4.9 GB: 2-of-4   4.3 GB: 3-of-4   1.1 GB: 4-of-4
Quantization Type4bit
Generates CodeYes
Model ArchitectureQwen2ForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.44.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size152064
Torch Data Typebfloat16
Errorsreplace

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...2.5 Coder 7B Instruct Bnb 4bit32K / 5.5 GB6162112
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Sft Model32K / 15.2 GB70
Qwen2.5 Coder 7B Instruct 4bit32K / 4.3 GB396211
UIGEN 7B 16bit32K / 15.2 GB45
FastApply 7B V1.032K / 15.2 GB114634
...en2.5.1 Coder 7B Instruct 8bit32K / 8.1 GB1062
...en2.5.1 Coder 7B Instruct 4bit32K / 4.3 GB113
Note: green Score (e.g. "73.2") means that the model is better than quocdat25/fast-apply-v0.2-qwen2.5-Coder-7B-ft.

Rank the Fast Apply V0.2 Qwen2.5 Coder 7B Ft 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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Release v20260328a