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Gen Sql 4B Thinking AWQ by researchsocaai

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Gen Sql 4B Thinking AWQ is an open-source language model by researchsocaai. Features: 4b LLM, VRAM: 3.5GB, Context: 40K, License: apache-2.0, Quantized, LLM Explorer Score: 0.2.

  4-bit   Awq Base model:quantized:researchs... Base model:researchsocaai/gen-...   Bitsandbytes   Conversational   En   Endpoints compatible   Quantized   Qwen3   Region:us   Safetensors   Unsloth
Model Card on HF 🤗: https://huggingface.co/researchsocaai/gen-sql-4b-thinking-awq 

Gen Sql 4B Thinking AWQ Benchmarks

LLME Score: 0.19723
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gen Sql 4B Thinking AWQ (researchsocaai/gen-sql-4b-thinking-awq)
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Gen Sql 4B Thinking AWQ Parameters and Internals

LLM NameGen Sql 4B Thinking AWQ
Repository 🤗https://huggingface.co/researchsocaai/gen-sql-4b-thinking-awq 
Base Model(s)  dwikitheduck/gen-sql-thinking-awq   dwikitheduck/gen-sql-thinking-awq
Model Size4b
Required VRAM3.5 GB
Updated2026-05-02
Maintainerresearchsocaai
Model Typeqwen3
Model Files  3.5 GB
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureQwen3ForCausalLM
Licenseapache-2.0
Context Length40960
Model Max Length40960
Transformers Version4.52.4
Tokenizer ClassQwen2Tokenizer
Padding Token<|vision_pad|>
Vocabulary Size151936
Torch Data Typebfloat16
Errorsreplace

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Qwen3 4B AWQ40K / 2.7 GB65686226
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...Instruct 2507 Unsloth Bnb 4bit256K / 3.5 GB19502514
Qwen3 4B Self Thinking 16bit256K / 8.1 GB13100
...wen3 4B Thinking 2507 MLX 4bit256K / 2.3 GB6926611
...wen3 4B Thinking 2507 MLX 8bit256K / 4.3 GB690137
Qwen3 4B ARC MLX 4bit256K / 2 GB2802
Qwen3 4B Instruct 2507 4bit256K / 2.3 GB183279
...Thinking 2507 Unsloth Bnb 4bit256K / 3.5 GB150672
Jan V3 4B Base Instruct 4bit256K / 2.3 GB5412
Note: green Score (e.g. "73.2") means that the model is better than researchsocaai/gen-sql-4b-thinking-awq.

Rank the Gen Sql 4B Thinking AWQ Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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