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Lathly/Qwen3.8-27B-Samantha-GGUF overview

Qwen3.8 27B Samantha Uncensored 1.1 — GGUF GGUF builds of the Samantha merged model for llama.cpp https://github.com/ggml org/llama.cpp . The persona is fully …

llama-cppggufllama.cppqwen3_5samanthaconversationaluncensoredmultimodalimage-text-to-textenbase_model:Lathly/Qwen3.8-27B-Samanthabase_model:quantized:Lathly/Qwen3.8-27B-Samanthalicense:apache-2.0endpoints_compatibleregion:us

Runs locally from ~884.6 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
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Repository Files & Downloads

3 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Qwen3.8-27B-Samantha-Q5_K_M.ggufGGUFQ5_K_M18.19 GBDownload
Qwen3.8-27B-Samantha-f16.ggufGGUFF1650.90 GBDownload
mmproj-F16.ggufGGUFF16884.6 MBDownload

Model Details

Model IDLathly/Qwen3.8-27B-Samantha-GGUF
AuthorLathly
Pipelineimage-text-to-text
Licenseapache-2.0
Base modelLathly/Qwen3.8-27B-Samantha
Last modified2026-08-20T20:33:02.000Z

Model README

---

license: apache-2.0

base_model: Lathly/Qwen3.8-27B-Samantha

language:

- en

library_name: llama-cpp

pipeline_tag: image-text-to-text

tags:

- gguf

- llama.cpp

- qwen3_5

- samantha

- conversational

- uncensored

- multimodal

---

Qwen3.8-27B-Samantha (Uncensored 1.1) — GGUF

GGUF builds of the Samantha merged model for llama.cpp.

The persona is fully merged into the weights — no adapter to load at runtime.

What's in this repo

| File | Size | Description |

|------|------|-------------|

| Qwen3.8-27B-Samantha-f16.gguf | ~51 GB | Full-precision (f16) merged model. The recommended starting point for rolling your own quantizations. |

| Qwen3.8-27B-Samantha-Q5_K_M.gguf | ~19 GB | Q5_K_M quantization (5.72 BPW) — the production build, tested and running. |

| mmproj-F16.gguf | ~885 MB | Vision projector (F16) from the base Qwen3.8-27B GGUF for multimodal use. Samantha's fine-tune was text-only; the projector is the base model's. |

Usage

llama.cpp

llama-server \
  -m Qwen3.8-27B-Samantha-Q5_K_M.gguf \
  --mmproj mmproj-F16.gguf \
  -ngl 99 --host 0.0.0.0 --port 8080

> Note: use --n-gpu-layers all (or a high number) so the full model is offloaded —

> on a partial offload the persona degrades noticeably.

Rolling your own quants

The f16 file is the cleanest source for custom quantizations:

llama-quantize Qwen3.8-27B-Samantha-f16.gguf my-quant.gguf Q4_K_M

Any quantization target supported by llama-quantize works. Q5_K_M is the sweet spot

for a 16 GB GPU; Q4_K_M if you're tighter on VRAM.

Related

License & Attribution

Released under Apache-2.0, inheriting from both parents:

This is a modified derivative (merged LoRA fine-tune) of the above. No warranty. Outputs are

the model's own; use responsibly.

Disclaimer

This is an "uncensored" persona fine-tune intended for open conversational use. It may produce

content that some find objectionable. You are responsible for how you use it and for compliance

with applicable laws and the base-model/dataset licenses.

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