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CELL-LAB/C_SERVER-GRPO-MERGED
C_SERVER-GRPO-MERGED is a image-text-to-text model from CELL-LAB. 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 gemma.
This repository packages a GRPO LoRA adapter merged into CELL-LAB/lora-plus-f2f-backup.
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Updated Aug 18, 2026
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From the Hugging Face model README
This repository packages a GRPO LoRA adapter merged into CELL-LAB/lora-plus-f2f-backup.
Two folders are included:
merged/: standalone BF16 checkpoint for vLLM serving.adapter/: PEFT LoRA adapter files kept for reproducibility and adapter-only loading.The LoRA has already been merged in merged/. Do not pass an adapter or enable
LoRA when loading the merged/ folder.
This repository is prepared for the requested serving stack:
pip install "vllm==0.8.1" "transformers==4.50.0"
Because the full checkpoint is stored in the merged/ subfolder, download the
repository first and point vllm serve at that local folder:
python - <<'PY'
from huggingface_hub import snapshot_download
snapshot_download("CELL-LAB/C_SERVER-GRPO-MERGED", local_dir="./C_SERVER-GRPO-MERGED")
PY
vllm serve ./C_SERVER-GRPO-MERGED/merged \
--served-model-name C_SERVER-GRPO-MERGED \
--dtype bfloat16 \
--max-model-len 8192 \
--trust-remote-code
For multiple GPUs, add --tensor-parallel-size GPU_COUNT.
The BF16 weight shards are large. Make sure the GPU has enough memory for the model weights plus KV cache.
from huggingface_hub import snapshot_download
from vllm import LLM, SamplingParams
repo_dir = snapshot_download("CELL-LAB/C_SERVER-GRPO-MERGED")
model_dir = repo_dir + "/merged"
llm = LLM(model=model_dir, trust_remote_code=True, dtype="bfloat16")
sampling = SamplingParams(max_tokens=512, temperature=0.0)
outputs = llm.generate(["안녕하세요"], sampling)
print(outputs[0].outputs[0].text)
from transformers import AutoTokenizer, Gemma3ForConditionalGeneration
from peft import PeftModel
base_id = "CELL-LAB/lora-plus-f2f-backup"
adapter_dir = "adapter"
tokenizer = AutoTokenizer.from_pretrained(base_id)
base = Gemma3ForConditionalGeneration.from_pretrained(
base_id,
device_map="auto",
torch_dtype="auto",
)
model = PeftModel.from_pretrained(base, adapter_dir)
The repository root is a container layout. For vLLM, use the merged/
subfolder, not the repo root.