Evidencedesk Gemma 4 12B Lora is an open-source language model by UmitVice. Features: 12b LLM, VRAM: 0.3GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.28.
| LLM Name | Evidencedesk Gemma 4 12B Lora |
| Repository 🤗 | https://huggingface.co/UmitVice/evidencedesk-gemma-4-12b-lora |
| Base Model(s) | |
| Model Size | 12b |
| Required VRAM | 0.3 GB |
| Updated | 2026-10-26 |
| Maintainer | UmitVice |
| Model Files | |
| Supported Languages | en tr |
| Quantization Type | q4|q4_0 |
| Model Architecture | Adapter |
| License | apache-2.0 |
| Is Biased | none |
| PEFT Type | LORA |
| LoRA Model | Yes |
| PEFT Target Modules | model.language_model.layers.0.mlp.down_proj|model.language_model.layers.0.mlp.gate_proj|model.language_model.layers.0.mlp.up_proj|model.language_model.layers.0.self_attn.k_proj|model.language_model.layers.0.self_attn.o_proj|model.language_model.layers.0.self_attn.q_proj|model.language_model.layers.0.self_attn.v_proj|model.language_model.layers.1.mlp.down_proj|model.language_model.layers.1.mlp.gate_proj|model.language_model.layers.1.mlp.up_proj|model.language_model.layers.1.self_attn.k_proj|model.language_model.layers.1.self_attn.o_proj|model.language_model.layers.1.self_attn.q_proj|model.language_model.layers.1.self_attn.v_proj|model.language_model.layers.10.mlp.down_proj|model.language_model.layers.10.mlp.gate_proj|model.language_model.layers.10.mlp.up_proj|model.language_model.layers.10.self_attn.k_proj|model.language_model.layers.10.self_attn.o_proj|model.language_model.layers.10.self_attn.q_proj|model.language_model.layers.10.self_attn.v_proj|model.language_model.layers.11.mlp.down_proj|model.language_model.layers.11.mlp.gate_proj|model.language_model.layers.11.mlp.up_proj|model.language_model.layers.11.self_attn.k_proj|model.language_model.layers.11.self_attn.o_proj|model.language_model.layers.11.self_attn.q_proj|model.language_model.layers.12.mlp.down_proj|model.language_model.layers.12.mlp.gate_proj|model.language_model.layers.12.mlp.up_proj|model.language_model.layers.12.self_attn.k_proj|model.language_model.layers.12.self_attn.o_proj|model.language_model.layers.12.self_attn.q_proj|model.language_model.layers.12.self_attn.v_proj|model.language_model.layers.13.mlp.down_proj|model.language_model.layers.13.mlp.gate_proj|model.language_model.layers.13.mlp.up_proj|model.language_model.layers.13.self_attn.k_proj|model.language_model.layers.13.self_attn.o_proj|model.language_model.layers.13.self_attn.q_proj|model.language_model.layers.13.self_attn.v_proj|model.language_model.layers.14.mlp.down_proj|model.language_model.layers.14.mlp.gate_proj|model.language_model.layers.14.mlp.up_proj|model.language_model.layers.14.self_attn.k_proj|model.language_model.layers.14.self_attn.o_proj|model.language_model.layers.14.self_attn.q_proj|model.language_model.layers.14.self_attn.v_proj|model.language_model.layers.15.mlp.down_proj|model.language_model.layers.15.mlp.gate_proj|model.language_model.layers.15.mlp.up_proj|model.language_model.layers.15.self_attn.k_proj|model.language_model.layers.15.self_attn.o_proj|model.language_model.layers.15.self_attn.q_proj|model.language_model.layers.15.self_attn.v_proj|model.language_model.layers.16.mlp.down_proj|model.language_model.layers.16.mlp.gate_proj|model.language_model.layers.16.mlp.up_proj|model.language_model.layers.16.self_attn.k_proj|model.language_model.layers.16.self_attn.o_proj|model.language_model.layers.16.self_attn.q_proj|model.language_model.layers.16.self_attn.v_proj|model.language_model.layers.17.mlp.down_proj|model.language_model.layers.17.mlp.gate_proj|model.language_model.layers.17.mlp.up_proj|model.language_model.layers.17.self_attn.k_proj|model.language_model.layers.17.self_attn.o_proj|model.language_model.layers.17.self_attn.q_proj|model.language_model.layers.18.mlp.down_proj|model.language_model.layers.18.mlp.gate_proj|model.language_model.layers.18.mlp.up_proj|model.language_model.layers.18.self_attn.k_proj|model.language_model.layers.18.self_attn.o_proj|model.language_model.layers.18.self_attn.q_proj|model.language_model.layers.18.self_attn.v_proj|model.language_model.layers.19.mlp.down_proj|model.language_model.layers.19.mlp.gate_proj|model.language_model.layers.19.mlp.up_proj|model.language_model.layers.19.self_attn.k_proj|model.language_model.layers.19.self_attn.o_proj|model.language_model.layers.19.self_attn.q_proj|model.language_model.layers.19.self_attn.v_proj|model.language_model.layers.2.mlp.down_proj|model.language_model.layers.2.mlp.gate_proj|model.language_model.layers.2.mlp.up_proj|model.language_model.layers.2.self_attn.k_proj|model.language_model.layers.2.self_attn.o_proj|model.language_model.layers.2.self_attn.q_proj|model.language_model.layers.2.self_attn.v_proj|model.language_model.layers.20.mlp.down_proj|model.language_model.layers.20.mlp.gate_proj|model.language_model.layers.20.mlp.up_proj|model.language_model.layers.20.self_attn.k_proj|model.language_model.layers.20.self_attn.o_proj|model.language_model.layers.20.self_attn.q_proj|model.language_model.layers.20.self_attn.v_proj|model.language_model.layers.21.mlp.down_proj|model.language_model.layers.21.mlp.gate_proj|model.language_model.layers.21.mlp.up_proj|model.language_model.layers.21.self_attn.k_proj|model.language_model.layers.21.self_attn.o_proj|model.language_model.layers.21.self_attn.q_proj|model.language_model.layers.21.self_attn.v_proj|model.language_model.layers.22.mlp.down_proj|model.language_model.layers.22.mlp.gate_proj|model.language_model.layers.22.mlp.up_proj|model.language_model.layers.22.self_attn.k_proj|model.language_model.layers.22.self_attn.o_proj|model.languag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_proj|model.language_model.layers.9.self_attn.o_proj|model.language_model.layers.9.self_attn.q_proj|model.language_model.layers.9.self_attn.v_proj |
| LoRA Alpha | 32 |
| LoRA Dropout | 0.05 |
| R Param | 16 |
Best Alternatives |
Context / RAM |
Downloads |
Likes |
|---|---|---|---|
| Aura NeMo 12B | 0K / 1.8 GB | 6 | 2 |
| Kenlang Gemma 4 12B It Lora V2 | 0K / 0.3 GB | 44 | 1 |
| ...It Ornith9 Summaries LoRA R128 | 0K / 2.1 GB | 26 | 1 |
| ...4 12B Sv Source2synth Nlu Lora | 0K / 0.3 GB | 19 | 1 |
| Gemma4 Sagax Os27 Raw | 0K / 0.3 GB | 23 | 1 |
| Teleste Learner 12B | 0K / 0.3 GB | 29 | 1 |
| ...ni 3.5 Flash Reasoning Distill | 0K / 0.3 GB | 39 | 2 |
| Beef 12B | 0K / 3.6 GB | 5 | 0 |
| Noodles 12B | 0K / 3.6 GB | 5 | 0 |
| G3 12B Pt Story Qlora | 0K / 0.5 GB | 23 | 0 |