How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="schneewolflabs/Qwen3.6-27B-Stimme-LoRA")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("schneewolflabs/Qwen3.6-27B-Stimme-LoRA", device_map="auto")
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Qwen3.6-27B-Stimme-LoRA

Experimental LoRA trained on various human writing datasets.

This job halted at 20% due to a bug but is an intact adapter and the reward function had peaked anyway.

Training Configuration

Parameter Value
Training Mode POST_HOC_UPLOAD
Base Model nbeerbower/Qwen3.6-27B-TIES
LoRA Rank (r) 32
LoRA Alpha 64
LoRA Dropout 0.0
Target Modules down_proj, k_proj, v_proj, gate_proj, up_proj, q_proj, o_proj
GPU NVIDIA GB10

Trained with Merlina

Merlina on GitHub

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