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Jenna V3 Qwen2 1.5 by V3N0M

 »  All LLMs  »  V3N0M  »  Jenna V3 Qwen2 1.5   URL Share it on

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Jenna V3 Qwen2 1.5 is an open-source language model by V3N0M. Features: 1.5b LLM, VRAM: 3.1GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

  4bit Base model:finetune:unsloth/qw... Base model:unsloth/qwen2-1.5b-...   Conversational   En   Endpoints compatible   Instruct   Pytorch   Quantized   Qwen2   Region:us   Sft   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/V3N0M/Jenna-v3-Qwen2-1.5 

Jenna V3 Qwen2 1.5 Benchmarks

LLME Score: 0.13264
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Jenna V3 Qwen2 1.5 (V3N0M/Jenna-v3-Qwen2-1.5)
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Jenna V3 Qwen2 1.5 Parameters and Internals

LLM NameJenna V3 Qwen2 1.5
Repository 🤗https://huggingface.co/V3N0M/Jenna-v3-Qwen2-1.5 
Base Model(s)  unsloth/Qwen2-1.5B-Instruct-bnb-4bit   unsloth/Qwen2-1.5B-Instruct-bnb-4bit
Model Size1.5b
Required VRAM3.1 GB
Updated2026-06-08
MaintainerV3N0M
Model Typeqwen2
Instruction-BasedYes
Model Files  3.1 GB
Supported Languagesen
Quantization Type4bit
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.41.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typefloat16
Errorsreplace

Best Alternatives to Jenna V3 Qwen2 1.5

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Context / RAM
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Qwen2.5 Coder 1.5B NL Java32K / 3.1 GB1310
Legal Qwen2.5 1.5B Finetuned32K / 3.1 GB100
Qwen2.5 1.5B Legal Sft32K / 3.1 GB390
Qwen2.5 1.5B Legal Id Sft32K / 3.1 GB240
Qwen Legal SFT Dicoding Final32K / 3.1 GB130
Legal Chatbot Qwen2.5 1.5B Sft32K / 3.1 GB100
Qwen2.5 1.5B Legal ID Chatbot32K / 3.1 GB540
...en2.5 1.5B Instruct Sft Merged32K / 3.1 GB01
Rithaji 1.5B32K / 3.1 GB42
...n2.5 1.5B Indonesian Sft Pgabl32K / 3.1 GB230
Note: green Score (e.g. "73.2") means that the model is better than V3N0M/Jenna-v3-Qwen2-1.5.

Rank the Jenna V3 Qwen2 1.5 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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