license: apache-2.0 base_model: Qwen/Qwen3.8-27B datasets:
- digitalpipelines/samantha-1.1-uncensored language:
- en library_name: peft pipeline_tag: text-generation tags:
- lora
- peft
- qwen3_5
- samantha
- conversational
- unsloth
- gguf
Qwen3.8-27B-Samantha (Uncensored 1.1) — LoRA
A LoRA fine-tune of Qwen/Qwen3.8-27B on the digitalpipelines/samantha-1.1-uncensored dataset, giving the model Samantha's warm, empathetic, emotionally-present conversational persona.
What's in this repo
| File | Description |
|---|---|
adapter_model.safetensors | The LoRA adapter (PEFT format), ~304 MB |
adapter_config.json | PEFT config (r=16, α=32, all-linear) |
Qwen3.8-27B-Samantha-LoRA-qwen35.gguf | LoRA adapter in GGUF, arch-tagged qwen35 for llama.cpp |
tokenizer*.json, chat_template.jinja | Tokenizer + chat template |
Related repositories
| Repo | What it is |
|---|---|
| Lathly/Qwen3.8-27B-Samantha | The full merged model (bf16 safetensors) — this adapter baked into the base weights, ready to run with Transformers / vLLM / SGLang |
| Lathly/Qwen3.8-27B-Samantha-GGUF | GGUF builds for llama.cpp — f16 (for rolling your own quants) + Q5_K_M (tested production build) + mmproj for multimodal |
If you want the persona without managing an adapter, grab the merged model or a GGUF. Additional community GGUF quantizations are welcome.
Usage
🤗 Transformers + PEFT
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B", torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(base, "Lathly/Qwen3.8-27B-Samantha_Uncensored_1.1_LoRA")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.8-27B")
🦙 llama.cpp (GGUF)
You need a GGUF of the base model (e.g. a community Qwen3.8-27B Q5_K_M) plus this LoRA GGUF:
llama-server \
-m Qwen3.8-27B-Q5_K_M.gguf \
--lora Qwen3.8-27B-Samantha-LoRA-qwen35.gguf \
-ngl 99 --host 0.0.0.0 --port 8080
Qwen LoRA merge fix
llama.cpp branch: qwen-lora-vhead-fix" Commit: f839835a3 Description: convert : reorder V heads for LoraTorchTensor Required to successfully merge certain Qwen LoRA adapters.
Important: the adapter GGUF here is already tagged with
general.architecture = qwen35to match the base GGUF. If you regenerate the adapter yourself with an olderconvert_lora_to_gguf.py, it may tag asqwen3and llama.cpp will refuse to load it withmodel arch and LoRA arch mismatch. Retaggeneral.architecturetoqwen35to fix.
Training
Fine-tuned with Unsloth (QLoRA, 4-bit) on dual RTX 5070 Ti. Settings were chosen to closely match the reference Samantha training spec. The adapter was trained on text-only conversational data with a sequence length of 2048; this does not change the base model's supported context length, but Samantha-style behavior at long context has not been systematically evaluated:
| Parameter | Value |
|---|---|
| LoRA rank (r) | 16 |
| LoRA alpha | 32 |
| Target modules | all-linear (q, k, v, o, gate, up, down) |
| LoRA dropout | 0.05 |
| Bias | none |
| Task type | CAUSAL_LM |
| Optimizer | 8-bit Paged AdamW |
| Learning rate | 2e-5 |
| LR scheduler | linear |
| Epochs | 1 |
| Effective batch size | 32 (grad accumulation) |
| Sequence length | 2048 |
| Warmup steps | 50 |
| Weight decay | 0.01 |
| Final train loss | ~1.42 |
License & Attribution
Released under Apache-2.0, inheriting from both parents:
- Base model: Qwen/Qwen3.8-27B — Apache-2.0, © Alibaba Cloud / Qwen Team
- Dataset: digitalpipelines/samantha-1.1-uncensored — Apache-2.0
- The original Samantha persona/dataset concept is by Eric Hartford.
This is a modified derivative (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.