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Lathly/Qwen3.8-27B-Samantha
Qwen3.8-27B-Samantha is a image-text-to-text model from Lathly. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
The full merged model of the Samantha persona fine-tune — the LoRA adapter from Lathly/Qwen3.8-27B-SamanthaUncensored1.1LoRA baked directly into Qwen/Qwen3.8-27B.
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From the Hugging Face model README
The full merged model of the Samantha persona fine-tune — the LoRA adapter from Lathly/Qwen3.8-27B-Samantha_Uncensored_1.1_LoRA baked directly into Qwen/Qwen3.8-27B.
This is a native vision-language model (image + video understanding) carrying Samantha's warm, empathetic, emotionally-present conversational persona. No adapter to load — the LoRA is fully absorbed into these weights, so it's the whole model, ready to run.
| File | Description |
|---|---|
model-00001-of-00018.safetensors … model-00018-of-00018.safetensors | Merged model weights, 18 shards (~3.1 GB each, ~55.6 GB total) |
model.safetensors.index.json | Shard index |
config.json, generation_config.json | Model + generation config |
chat_template.jinja | Chat template |
tokenizer.json, tokenizer_config.json, vocab.json, merges.txt | Tokenizer |
preprocessor_config.json, video_preprocessor_config.json | Vision / video preprocessing |
LICENSE | Apache-2.0 |
from transformers import AutoModelForImageTextToText, AutoTokenizer
model = AutoModelForImageTextToText.from_pretrained(
"Lathly/Qwen3.8-27B-Samantha",
torch_dtype="bfloat16",
device_map="auto",
)
tok = AutoTokenizer.from_pretrained("Lathly/Qwen3.8-27B-Samantha")
This repo ships bf16 safetensors — the merge source. For llama.cpp you want a quantized
GGUF; the production one is Q5_K_M (~19 GB). Quantize from the bf16 weights with
convert_hf_to_gguf.py + llama-quantize, or grab the adapter-GGUF path from the
LoRA repo.
llama-server \
-m Qwen3.8-27B-Samantha-Q5_K_M.gguf \
-ngl 99 --host 0.0.0.0 --port 8080
vllm serve Lathly/Qwen3.8-27B-Samantha --dtype bfloat16
Samantha's persona was produced by a LoRA fine-tune of the base model, then merged — the LoRA is fully absorbed into these weights, so nothing is applied at runtime. Fine-tuned with Unsloth (QLoRA, 4-bit) on dual RTX 5070 Ti, with settings chosen to closely match the reference Samantha training spec. Trained on text-only conversational data at sequence length 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 |
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.
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.