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EGTLM Qwen1.5 1.8B Instruct by selmisskilig

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EGTLM Qwen1.5 1.8B Instruct is an open-source language model by selmisskilig. Features: 1.8b LLM, VRAM: 3.7GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.14.

  Conversational   Endpoints compatible   Instruct   Pytorch   Qwen2   Region:us
Model Card on HF 🤗: https://huggingface.co/selmisskilig/EGTLM-Qwen1.5-1.8B-instruct 

EGTLM Qwen1.5 1.8B Instruct Benchmarks

LLME Score: 0.1354
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
EGTLM Qwen1.5 1.8B Instruct (selmisskilig/EGTLM-Qwen1.5-1.8B-instruct)
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EGTLM Qwen1.5 1.8B Instruct Parameters and Internals

Model Type 
text generation, embedding
Supported Languages 
Chinese (good)
Training Details 
Methodology:
Hybrid task with instruction shunting and hybrid loss; Bidirectional attention mechanism; uses carefully generated and filtered embedding data and open-source dialogue data.
Context Length:
32000
Model Architecture:
Hybrid Embedding and text generation task model with bidirectional attention mechanism
LLM NameEGTLM Qwen1.5 1.8B Instruct
Repository 🤗https://huggingface.co/selmisskilig/EGTLM-Qwen1.5-1.8B-instruct 
Model Size1.8b
Required VRAM3.7 GB
Updated2026-04-11
Maintainerselmisskilig
Model Typeqwen2
Instruction-BasedYes
Model Files  3.7 GB   0.0 GB
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.1
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typebfloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than selmisskilig/EGTLM-Qwen1.5-1.8B-instruct.

Rank the EGTLM Qwen1.5 1.8B Instruct 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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