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SOLAR 10.7B Instruct V1.0 Laser by macadeliccc

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SOLAR 10.7B Instruct V1.0 Laser is an open-source language model by macadeliccc. Features: 10.7b LLM, VRAM: 21.4GB, Context: 4K, License: cc-by-nc-2.0, Instruction-Based, LLM Explorer Score: 0.12.

  Conversational   Endpoints compatible   Instruct   Llama   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser 

SOLAR 10.7B Instruct V1.0 Laser Benchmarks

LLME Score: 0.11807
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
SOLAR 10.7B Instruct V1.0 Laser (macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser)
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SOLAR 10.7B Instruct V1.0 Laser Parameters and Internals

Additional Notes 
This model version has been optimized using a process termed as lasering, shown by the reduction in perplexity value.
LLM NameSOLAR 10.7B Instruct V1.0 Laser
Repository 🤗https://huggingface.co/macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser 
Model Size10.7b
Required VRAM21.4 GB
Updated2026-04-14
Maintainermacadeliccc
Model Typellama
Instruction-BasedYes
Model Files  9.9 GB: 1-of-3   10.0 GB: 2-of-3   1.5 GB: 3-of-3   9.9 GB: 1-of-3   10.0 GB: 2-of-3   1.5 GB: 3-of-3
Model ArchitectureLlamaForCausalLM
Licensecc-by-nc-2.0
Context Length4096
Model Max Length4096
Transformers Version4.34.0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

Best Alternatives to SOLAR 10.7B Instruct V1.0 Laser

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SOLAR 10.7B Instruct V1.0 128K128K / 21.4 GB1243
SOLAR 10.7B V1.0 Instruct 16K16K / 21.4 GB632
SauerkrautLM SOLAR Instruct8K / 21.4 GB96846
MetaModelv28K / 21.4 GB7860
Kazemi 1.2 Solar8K / 21.4 GB00
SOLAR 10.7B Instruct V1.04K / 21.4 GB37162649
Somer24K / 21.4 GB100
SOLAR 10.7B Instruct V1.04K / 21.4 GB120
Solarmer34K / 21.4 GB50
Somer4K / 21.4 GB50
Note: green Score (e.g. "73.2") means that the model is better than macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser.

Rank the SOLAR 10.7B Instruct V1.0 Laser 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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