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viennh2012/cinemr-cardiac-sft

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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CineMR — SFT epoch 0 (LoRA on Qwen3-VL-8B-Instruct)

PEFT LoRA checkpoint from the first supervised fine-tuning epoch on top of `Qwen/Qwen3-VL-8B-Instruct`. This folder is not a full merged model: it contains adapter weights, tokenizer/processor configs, chat template, and a small tool-decision auxiliary head used during training.

Files in this directory

File	Role
`adapter_model.safetensors`	LoRA weights
`adapter_config.json`	PEFT metadata (`peft_version`, `r`, `lora_alpha`, `target_modules`, …)
`tokenizer.json`, `tokenizer_config.json`	Tokenizer
`processor_config.json`	`Qwen3VLProcessor` (image + video blocks; `processor_class`: `Qwen3VLProcessor`)
`chat_template.jinja`	Chat template
`tool_decision_head.pt`	Auxiliary classifier weights (`tool_decision_aux`)
`tool_decision_head.json`	Head spec (`hidden_size`, `kind`)

LoRA configuration (`adapter_config.json`)

Field	Value
PEFT	LoRA, `task_type`: `CAUSAL_LM`
Base	`Qwen/Qwen3-VL-8B-Instruct`
Rank / α / dropout	`r=16`, `lora_alpha=32`, `lora_dropout=0.05`
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Vision	No `exclude_modules` — vision + language linear targets follow PEFT defaults for this export
PEFT version	`0.18.1`

Loading the LoRA adapter

python

import torch
from transformers import AutoModelForVision2Seq, AutoProcessor
from peft import PeftModel

base_id = "Qwen/Qwen3-VL-8B-Instruct"
adapter_dir = "."  # path to this `epoch0` directory

base = AutoModelForVision2Seq.from_pretrained(
    base_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, adapter_dir, is_trainable=False)
processor = AutoProcessor.from_pretrained(adapter_dir, trust_remote_code=True)

Match torch_dtype / device_map to your inference or merge pipeline. The `tool_decision_head.pt` head is not loaded by PeftModel.from_pretrained; if your runtime expects it, load it with the same training code path that produced this checkpoint.

Downstream

Merged full-weight exports used elsewhere in this tree (e.g. `Data/cardiac_outputs/sft_merged_easyr1`) may combine multiple SFT stages; this `epoch0` snapshot is the first-epoch LoRA only. Align training data and licenses with your Cardiac / VLM SFT recipe.

Limitations

—Medical / imaging: outputs are not clinical devices; verify on your data and governance rules.
—Tool head: auxiliary weights are training-specific; document behavior if you ship a public API.

License

Use consistent with Qwen3-VL, PEFT, and the datasets you used for SFT.

Citation

bibtex

@misc{cinemr_sft_epoch0_lora_qwen3vl8b,
  title        = {CineMR SFT epoch 0 --- LoRA on Qwen3-VL-8B-Instruct},
  howpublished = {Local PEFT adapter export},
  year         = {2026},
  note         = {Includes tool\_decision\_head auxiliary checkpoint},
}