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mradermacher/srinova-ai-v2-deepseek-sinhala-chat-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: < quants: x f16 Q4 K S Q2 K Q8 0 Q6 K Q3 K M Q3 K S Q3 K L Q4…

transformersggufbase_model:adapter:deepseek-ai/DeepSeek-V2-Litelorasfttrlenbase_model:Dushan6767/srinova-ai-v2-deepseek-sinhala-chatbase_model:adapter:Dushan6767/srinova-ai-v2-deepseek-sinhala-chatendpoints_compatibleregion:usconversational

Runs locally from ~6.00 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
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Repository Files & Downloads

11 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
srinova-ai-v2-deepseek-sinhala-chat.IQ4_XS.ggufGGUFGGUF8.05 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q2_K.ggufGGUFGGUF6.00 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q3_K_L.ggufGGUFGGUF7.88 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q3_K_M.ggufGGUFGGUF7.57 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q3_K_S.ggufGGUFGGUF6.98 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q4_K_M.ggufGGUFGGUF9.66 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q4_K_S.ggufGGUFGGUF8.88 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q5_K_M.ggufGGUFGGUF11.04 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q5_K_S.ggufGGUFGGUF10.38 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q6_K.ggufGGUFGGUF13.11 GBDownload
srinova-ai-v2-deepseek-sinhala-chat.Q8_0.ggufGGUFGGUF15.56 GBDownload

Model Details

Model IDmradermacher/srinova-ai-v2-deepseek-sinhala-chat-GGUF
Authormradermacher
Pipeline—
License—
Base modelDushan6767/srinova-ai-v2-deepseek-sinhala-chat
Last modified2026-10-06T17:44:07.000Z

Model README

---

base_model: Dushan6767/srinova-ai-v2-deepseek-sinhala-chat

language:

  • en

library_name: transformers

model_name: srinova-ai-v2-deepseek-sinhala-chat

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

  • base_model:adapter:deepseek-ai/DeepSeek-V2-Lite
  • lora
  • sft
  • transformers
  • trl

---

About

<!-- ### quantize_version: 2 -->

<!-- ### output_tensor_quantised: 1 -->

<!-- ### convert_type: hf -->

<!-- ### vocab_type: -->

<!-- ### tags: -->

<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->

<!-- ### quants_skip: -->

<!-- ### skip_mmproj: -->

static quants of https://huggingface.co/Dushan6767/srinova-ai-v2-deepseek-sinhala-chat

<!-- provided-files -->

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's

READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for

more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |

|:-----|:-----|--------:|:------|

| GGUF | Q2_K | 6.5 | |

| GGUF | Q3_K_S | 7.6 | |

| GGUF | Q3_K_M | 8.2 | lower quality |

| GGUF | Q3_K_L | 8.6 | |

| GGUF | IQ4_XS | 8.7 | |

| GGUF | Q4_K_S | 9.6 | fast, recommended |

| GGUF | Q4_K_M | 10.5 | fast, recommended |

| GGUF | Q5_K_S | 11.2 | |

| GGUF | Q5_K_M | 12.0 | |

| GGUF | Q6_K | 14.2 | very good quality |

| GGUF | Q8_0 | 16.8 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.png

And here are Artefact2's thoughts on the matter:

https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to

questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

<!-- end -->

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