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ELYZA Japanese Llama 2 7B Instruct GPTQ 4bit 64g by TFMC

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ELYZA Japanese Llama 2 7B Instruct GPTQ 4bit 64g is an open-source language model by TFMC. Features: 7b LLM, VRAM: 4GB, Context: 4K, License: llama2, Quantized, Instruction-Based, LLM Explorer Score: 0.09.

  4bit   Autotrain compatible   Endpoints compatible   Gptq   Instruct   Llama   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TFMC/ELYZA-japanese-Llama-2-7b-instruct-GPTQ-4bit-64g 

ELYZA Japanese Llama 2 7B Instruct GPTQ 4bit 64g Benchmarks

LLME Score: 0.09197
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
ELYZA Japanese Llama 2 7B Instruct GPTQ 4bit 64g (TFMC/ELYZA-japanese-Llama-2-7b-instruct-GPTQ-4bit-64g)
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ELYZA Japanese Llama 2 7B Instruct GPTQ 4bit 64g Parameters and Internals

Model Type 
GPTQ
Additional Notes 
GPTQ model with 4-bit quantization, group size 64, desc_act=True.
LLM NameELYZA Japanese Llama 2 7B Instruct GPTQ 4bit 64g
Repository 🤗https://huggingface.co/TFMC/ELYZA-japanese-Llama-2-7b-instruct-GPTQ-4bit-64g 
Model Size7b
Required VRAM4 GB
Updated2025-09-23
MaintainerTFMC
Model Typellama
Instruction-BasedYes
Model Files  4.0 GB
GPTQ QuantizationYes
Quantization Typegptq|4bit
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.30.2
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TFMC/ELYZA-japanese-Llama-2-7b-instruct-GPTQ-4bit-64g.

Rank the ELYZA Japanese Llama 2 7B Instruct GPTQ 4bit 64g Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
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Data Analysis and Insight Generation  
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