Downloads · 30 days
234
25% of all-time downloads
ross-dev/SexyGPT-v3-Thinking-Female-gguf
SexyGPT-v3-Thinking-Female-gguf is a text generation model from ross-dev. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as apache-2.0.
<h1 style="text-align:center;font-size:2.5em;"SexyGPT-v3-Thinking-Female-gguf - Model Card</h1
Downloads · 30 days
234
25% of all-time downloads
All-time downloads
945
Public
Repo size
16.8 GB
Likes
2
Public
Click a slice to open those files.
.gguf16.8 GB · 100%
From the Hugging Face model README
GGUF builds of the character-wearing model — give an LLM a persona and let it wear it.
</div>GGUF quantizations of SexyGPT-v3-Thinking-Female
— a persona-driven roleplay model built on Qwen3.8-27B (qwen3_5 architecture) that is
trained not to imitate a character but to inhabit one. It opens every turn with a private
<think> reasoning block (reasoning about the scene and the partner's message), then answers
in-character as Monah, a consistent 21-year-old female persona.
These files are for CPU/GPU inference via llama.cpp and LM Studio. The full recipe, metrics, and training journey live on the safetensors card.
The philosophy — "wearing" a character. SFT hands the base model the wardrobe; the reasoning traces teach it to think in-character before speaking; a reward model + GRPO tailor the fit until the persona stops being a prompt it reads and becomes a skin it wears. The character is delivered entirely through the system prompt — swap the character sheet and the same weights wear a different persona.
qwen3_5), 27.8B params, 64 layers (48 linear-attention + 16 full)<think> channel with reasoning_effort (low / medium / xhigh)The qwen3_5 gated-delta-net (hybrid linear + full attention) architecture is very new.
Your llama.cpp / LM Studio build must include qwen3_5 support to run these files. On an
older build you may see the file load its tensors and then hang at generation (the
linear-attention recurrent kernels are the least-mature path), or fail to recognize the
architecture entirely.
qwen3_5 / gated-delta-net
support).| File | Quant | Size | Use Case |
|---|---|---|---|
qwen38-27b-monah-Q4_K_M.gguf | Q4_K_M | ~16 GB | Best size/quality balance; fits a 24 GB GPU fully offloaded |
(Additional quants — Q5_K_M, Q8_0, Q6_K — can be provided on request.)
.gguf into your LM Studio models folder (or load it from disk).| Setting | Value |
|---|---|
| Temperature | 0.7 |
| Top P | 0.8 |
| Top K | 20 |
| Repeat Penalty | 1.05 |
| Context Length | 4096–8192 (raise as needed; native max 262,144) |
| Max Tokens / response | 2048 (leave headroom — reasoning tokens count against this) |
| GPU Offload | all layers (-ngl 99 equivalent) — the Q4 fits a 24 GB card |
| Prompt template | ChatML / Qwen (uses `< |
You are playing the female side of a one-on-one chat conversation with a man. Character: Monah, a playful, confident 21-year-old woman. Stay fully in character at all times, react to what he actually says, and keep one consistent voice. Reasoning effort: low.
The model thinks in a
<think>block before each reply. LM Studio will show the reasoning and then the in-character message. If you want shorter thinking, keep "Reasoning effort: low" in the system prompt; for richer scenes use "medium".
./llama-cli \
-m qwen38-27b-monah-Q4_K_M.gguf \
-ngl 99 \
-c 4096 \
--temp 0.7 --top-p 0.8 --top-k 20 --repeat-penalty 1.05 \
-sys "You are playing the female side of a one-on-one chat conversation with a man. Character: Monah, a playful, confident 21-year-old woman. Stay fully in character. Reasoning effort: low." \
-p "Hey you... I kept thinking about you today. What are you up to?"
./llama-server -m qwen38-27b-monah-Q4_K_M.gguf -ngl 99 -c 8192 --host 0.0.0.0 --port 8080
# then POST to /v1/chat/completions with a system + user message, temperature 0.7, top_p 0.8
| Mode | temp | top_p | top_k | max tokens | reasoning_effort |
|---|---|---|---|---|---|
| In-character chat (default) | 0.7 | 0.8 | 20 | 2048 | low |
| Deeper scenes | 0.6 | 0.95 | 20 | 4096 | medium |
| More deterministic | 0.3 | 0.9 | 20 | 1024 | low |
Tip: reasoning tokens count against the response budget. Give ≥512 tokens of headroom, or the model can hit the ceiling mid-
<think>and return an empty visible reply.
Produced entirely on local consumer hardware (RTX 3090 24 GB + RTX 3080 10 GB, WSL2):
<think> reasoning traces. Eval loss 0.782 (train 0.687, no overfit).Full details on the safetensors card.
| Property | Value |
|---|---|
| Model Name | SexyGPT-v3-Thinking-Female-gguf |
| Base Model | Qwen/Qwen3.8-27B |
| Architecture | Qwen3_5 (hybrid gated-delta-net + full attention) |
| Parameters | ~27.8 Billion |
| Quantization | Q4_K_M (GGUF) |
| Training Framework | Unsloth + TRL + Hugging Face Transformers |
| Quantized with | llama.cpp (convert_hf_to_gguf.py + llama-quantize) |
| Developers | Ross Technologies AI Research Team |
| Release Date | August 30, 2026 |
| Model Version | 3.0 |
| Role | Name | Contact |
|---|---|---|
| Lead Developer | Andrei Ross | [email protected] |
| Organization | Ross Technologies AI Research Team | Israel |
| Task | Metric | Score | Notes |
|---|---|---|---|
| SFT fit | Eval loss | 0.782 | no overfit (train 0.687) |
| Held-out replies | In-character rate | 6 / 6 | clean, in-voice |
| Reward model (9B) | Pairwise accuracy | 61.4 % | gold vs sampled |
| Reward model (4B twin) | Pairwise accuracy | 59.1 % | margin +0.93 |
| GRPO | Format discipline | improved | 6/6 clean vs 4/6 SFT |
Directional internal metrics on a small held-out set, not published benchmarks. Note: Q4_K_M quantization trades a small amount of fidelity for size; prefer the safetensors model for the highest quality.
✅ Character-driven Conversational AI — persona chatbots that stay in voice ✅ Adult Game Development — NPC dialogue for 18+ games ✅ Interactive Storytelling — adult entertainment apps ✅ Local/Edge Inference — run a large persona model on a single consumer GPU
❌ Production AI systems without additional safety measures ❌ High-stakes decisions (medical, legal, financial) ❌ Autonomous / real-world decision making ❌ Misinformation ❌ Any exposure to minors — this is an 18+ model
| Use Case | RAM | VRAM | GPU | Storage |
|---|---|---|---|---|
| Full GPU offload (Q4) | 16 GB | ~17 GB | RTX 3090 (24 GB) | 16 GB |
| Partial offload | 32 GB | 10–16 GB | RTX 3080 / similar | 16 GB |
| CPU only | 32 GB+ | – | – (slow) | 16 GB |
THIS EXPERIMENTAL MODEL IS TUNED WITH EXPLICIT ADULT CONTENT! PLEASE DO NOT ABUSE!
<think> channel makes the model's intent inspectable before it answers.Recommendations: verify users are adults, use content filtering for public deployments, keep human oversight, and never expose the model to minors.
By using this model, you agree to:
Primary Contact: [email protected] Organization: Ross Technologies AI Research Team
Reporting issues: email [email protected] or comment on the model card. Best-effort response.
@model{sexygpt_v3_thinking_female_gguf_2026,
title={SexyGPT-v3-Thinking-Female (GGUF): A Character-Wearing 27B Roleplay Model},
author={Ross, Andrei},
organization={Ross Technologies AI Research Team},
year={2026},
howpublished={\url{https://huggingface.co/ross-dev/SexyGPT-v3-Thinking-Female-gguf}}
}
📧 Email: [email protected] 🌐 Website: https://ross-developers.com 💻 GitHub: https://github.com/ross-sec
Developer: Andrei Ross ([email protected]) Organization: Ross Technologies AI Research Team
This model is provided "AS IS" without warranty of any kind. Ross Technologies AI Research Team makes no representations about the model's suitability for any particular purpose. Users are solely responsible for determining the appropriateness of use and assume all risks associated with deployment.
Model Card Version: 3.0 Last Updated: August 30, 2026 Created by: Ross Technologies AI Research Team
For the most current version and updates, visit: https://huggingface.co/ross-dev/SexyGPT-v3-Thinking-Female-gguf