schneewolflabs/hecke-dpo
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How to use schneewolflabs/Qwen3.6-27B-Stimme-LoRA with Transformers:
# 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")How to use schneewolflabs/Qwen3.6-27B-Stimme-LoRA with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/Qwen3.6-27B-Stimme-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "schneewolflabs/Qwen3.6-27B-Stimme-LoRA",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'docker model run hf.co/schneewolflabs/Qwen3.6-27B-Stimme-LoRA
How to use schneewolflabs/Qwen3.6-27B-Stimme-LoRA with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "schneewolflabs/Qwen3.6-27B-Stimme-LoRA" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "schneewolflabs/Qwen3.6-27B-Stimme-LoRA",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "schneewolflabs/Qwen3.6-27B-Stimme-LoRA" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "schneewolflabs/Qwen3.6-27B-Stimme-LoRA",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'How to use schneewolflabs/Qwen3.6-27B-Stimme-LoRA with Docker Model Runner:
docker model run hf.co/schneewolflabs/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.
| 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 |
Base model
nbeerbower/Qwen3.6-27B-TIES
docker model run hf.co/schneewolflabs/Qwen3.6-27B-Stimme-LoRA