This is a Chinese character-style conversational model fine-tuned from a Qwen3-8B-series base model.
The model is intended for roleplay, emotional companion-style dialogue, and daily multi-turn Chinese conversation.

The fine-tuning goal is not general knowledge enhancement, but a more stable conversational style with the following traits:

gentle, direct, cute, and earnest tone
light robotic-style expression
companion-oriented and affectionate interaction style
short daily dialogue and roleplay scenarios

The released file is a GGUF quantized model for local inference with llama.cpp-compatible runtimes.

Derivation

This model is derived from Qwen/Qwen3-8B.

The workflow was:

Download the original Qwen3-8B base model.
Apply a heretic / decensor-style weight modification.
Fine-tune the resulting model with SFT + QLoRA on a Chinese roleplay / companion-style ShareGPT dataset.
Merge the LoRA adapter back into the model.
Convert and quantize the merged model to GGUF Q5_K_M with llama.cpp.

The released GGUF is therefore not the original Qwen3-8B model. It is a modified and fine-tuned derivative intended for local roleplay-style chat.

Model Files

Main release file:

HERETIC_Qwen3-8B_decensored-Q5_K_M.gguf

Model format:

Architecture: Qwen3
Parameters: about 8.2B
Quantization: Q5_K_M
Format: GGUF
Context length in GGUF metadata: 40960
SHA256: bb1ed5d4d64ac0dc9a4a354dc7b883b8bd5a35f782d89ecf6317ef5a488b3cd3

Intended Use

This model is intended for:

local Chinese roleplay chat
fictional companion-style dialogue
personal experimentation with Qwen3 LoRA/QLoRA fine-tuning
llama.cpp / LM Studio / Ollama-style local inference workflows

This model is not intended for:

factual question answering that requires high reliability
medical, legal, financial, or safety-critical advice
production use without additional evaluation
impersonating real people
generating harmful, illegal, or non-consensual content

Training Data

The final fine-tuning dataset uses ShareGPT-style multi-turn conversations.

Dataset summary:

Format: ShareGPT JSON
Conversations: 584
Total turns: 2606
Human turns: 1303
Assistant turns: 1303
Average turns per conversation: 4.46
Shortest conversation: 2 turns
Longest conversation: 8 turns

The dataset is a mixture of:

cleaned, refined, and deduplicated character-style dialogue data
synthetic romance / companion-style dialogue generated for broader daily interaction coverage

The dataset was checked for human/assistant alternation and selected banned source names.

The training dataset is not included in this model repository.

Training Procedure

The model was fine-tuned with LLaMA-Factory using SFT + QLoRA.

Main training settings:

Stage: SFT
Fine-tuning type: LoRA
LoRA target: all
LoRA rank: 16
LoRA alpha: 32
LoRA dropout: 0.10
Quantization during training: 4-bit
Template: qwen3_nothink
Cutoff length: 1024
Packing: true
Epochs: 3
Learning rate: 8e-5
Scheduler: cosine
Warmup ratio: 0.10
Weight decay: 0.01
Optimizer: paged_adamw_8bit
bf16: true
Gradient checkpointing: true
Validation size: 0.08

Training result:

Global steps: 39
Train loss: 3.9289
Eval loss: 2.9800

Eval loss by checkpoint:

Step 10: 3.9788
Step 20: 3.2850
Step 30: 3.0136
Step 39: 2.9800

Conversion and Quantization

After training, the LoRA adapter was merged into the base model and converted to GGUF with llama.cpp.

Pipeline:

SFT + QLoRA training with LLaMA-Factory
LoRA merge into Hugging Face safetensors format
Conversion to F16 GGUF
Quantization to Q5_K_M GGUF

Usage

Example with llama.cpp:

bash

./llama-cli \
  -m HERETIC_Qwen3-8B_decensored-Q5_K_M.gguf \
  -p "<|im_start|>user\n你好。<|im_end|>\n<|im_start|>assistant\n" \
  -n 512 \
  --temp 0.7 \
  --top-p 0.8 \
  --top-k 20

strand828/Qwen3_8b_Heretic_Astria-Q5_K_M

HERETIC-Qwen3-8B-Astria-GGUF

Model Description