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Translategemma Tok by zhoucantd

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Translategemma Tok is an open-source language model by zhoucantd. Features: 4b LLM, VRAM: 0.2GB, LLM Explorer Score: 0.24.

  Arxiv:1910.09700 Base model:adapter:google/tran... Base model:google/translategem...   Conversational   Llama-factory   Lora   Peft   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/zhoucantd/translategemma-tok 

Translategemma Tok Benchmarks

LLME Score: 0.24259
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Translategemma Tok (zhoucantd/translategemma-tok)
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Translategemma Tok Parameters and Internals

LLM NameTranslategemma Tok
Repository 🤗https://huggingface.co/zhoucantd/translategemma-tok 
Base Model(s)  google/translategemma-4b-it   google/translategemma-4b-it
Model Size4b
Required VRAM0.2 GB
Updated2026-05-09
Maintainerzhoucantd
Model Files  0.2 GB   0.0 GB
Model ArchitectureAutoModel
Is Biasednone
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modules29.self_attn.k_proj|down_proj|30.self_attn.v_proj|27.self_attn.k_proj|language_model.layers.14.self_attn.v_proj|language_model.layers.24.self_attn.k_proj|language_model.layers.7.self_attn.k_proj|language_model.layers.22.self_attn.k_proj|language_model.layers.14.self_attn.q_proj|28.self_attn.v_proj|30.self_attn.k_proj|31.self_attn.v_proj|language_model.layers.3.self_attn.k_proj|32.self_attn.v_proj|28.self_attn.k_proj|language_model.layers.1.self_attn.v_proj|29.self_attn.v_proj|language_model.layers.26.self_attn.q_proj|language_model.layers.10.self_attn.q_proj|29.self_attn.q_proj|language_model.layers.24.self_attn.v_proj|language_model.layers.16.self_attn.k_proj|language_model.layers.15.self_attn.k_proj|language_model.layers.6.self_attn.q_proj|28.self_attn.q_proj|language_model.layers.12.self_attn.q_proj|language_model.layers.25.self_attn.q_proj|30.self_attn.q_proj|language_model.layers.17.self_attn.v_proj|language_model.layers.25.self_attn.v_proj|language_model.layers.19.self_attn.v_proj|language_model.layers.4.self_attn.k_proj|27.self_attn.q_proj|language_model.layers.3.self_attn.q_proj|language_model.layers.1.self_attn.q_proj|language_model.layers.23.self_attn.v_proj|33.self_attn.k_proj|language_model.layers.19.self_attn.q_proj|language_model.layers.5.self_attn.q_proj|language_model.layers.0.self_attn.k_proj|language_model.layers.26.self_attn.k_proj|27.self_attn.v_proj|language_model.layers.13.self_attn.k_proj|language_model.layers.11.self_attn.k_proj|language_model.layers.24.self_attn.q_proj|language_model.layers.20.self_attn.k_proj|32.self_attn.k_proj|31.self_attn.k_proj|language_model.layers.12.self_attn.v_proj|language_model.layers.9.self_attn.k_proj|gate_proj|language_model.layers.12.self_attn.k_proj|language_model.layers.2.self_attn.q_proj|language_model.layers.6.self_attn.v_proj|language_model.layers.23.self_attn.k_proj|language_model.layers.21.self_attn.k_proj|language_model.layers.15.self_attn.q_proj|language_model.layers.7.self_attn.v_proj|language_model.layers.15.self_attn.v_proj|language_model.layers.20.self_attn.v_proj|language_model.layers.9.self_attn.v_proj|language_model.layers.0.self_attn.q_proj|language_model.layers.2.self_attn.k_proj|language_model.layers.0.self_attn.v_proj|33.self_attn.v_proj|language_model.layers.18.self_attn.q_proj|language_model.layers.21.self_attn.q_proj|language_model.layers.13.self_attn.q_proj|language_model.layers.10.self_attn.k_proj|33.self_attn.q_proj|language_model.layers.1.self_attn.k_proj|language_model.layers.20.self_attn.q_proj|language_model.layers.16.self_attn.q_proj|language_model.layers.18.self_attn.k_proj|language_model.layers.21.self_attn.v_proj|language_model.layers.3.self_attn.v_proj|language_model.layers.13.self_attn.v_proj|language_model.layers.17.self_attn.q_proj|language_model.layers.7.self_attn.q_proj|language_model.layers.10.self_attn.v_proj|language_model.layers.23.self_attn.q_proj|language_model.layers.9.self_attn.q_proj|language_model.layers.18.self_attn.v_proj|o_proj|language_model.layers.8.self_attn.q_proj|language_model.layers.14.self_attn.k_proj|language_model.layers.2.self_attn.v_proj|language_model.layers.8.self_attn.v_proj|language_model.layers.22.self_attn.q_proj|32.self_attn.q_proj|language_model.layers.19.self_attn.k_proj|31.self_attn.q_proj|language_model.layers.6.self_attn.k_proj|language_model.layers.4.self_attn.q_proj|language_model.layers.17.self_attn.k_proj|language_model.layers.22.self_attn.v_proj|language_model.layers.11.self_attn.v_proj|language_model.layers.5.self_attn.v_proj|language_model.layers.25.self_attn.k_proj|up_proj|language_model.layers.16.self_attn.v_proj|language_model.layers.4.self_attn.v_proj|language_model.layers.5.self_attn.k_proj|language_model.layers.11.self_attn.q_proj|language_model.layers.26.self_attn.v_proj|language_model.layers.8.self_attn.k_proj
LoRA Alpha64
LoRA Dropout0
R Param32

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Note: green Score (e.g. "73.2") means that the model is better than zhoucantd/translategemma-tok.

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