Instructions to use brics-edtech/Qwen3-VL-8B-Instruct-metal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use brics-edtech/Qwen3-VL-8B-Instruct-metal with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") model = PeftModel.from_pretrained(base_model, "brics-edtech/Qwen3-VL-8B-Instruct-metal") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: Qwen/Qwen3-VL-8B-Instruct
library_name: peft
tags:
- qwen3-vl
- vision-language
- lora
- sft
- grpo
- scireason
datasets:
- top-papers/top-papers-graph-experts-data
top-papers/Qwen3-VL-8B-Instruct-scireason
This repository contains the fine-tuned SciReason VLM artifacts produced by the DataSphere SFT + GRPO pipeline.
Contents
- Root files: final GRPO adapter and processor files copied from
outputs/hf_top_papers_qwen3vl_8b_grpo_lorafor convenient loading. artifacts/sft_lora/: SFT LoRA adapter directory copied fromoutputs/hf_top_papers_qwen3vl_8b_sft_lora.artifacts/grpo_lora/: complete final GRPO output directory copied fromoutputs/hf_top_papers_qwen3vl_8b_grpo_lora.artifacts/archives/: compressed.tar.gzarchives produced by the job.artifacts/data/: generated train/eval JSONL files and dataset summary.artifacts/reports/: budget, final summary, upload manifest and runtime reports.
Training metadata
- Base model:
Qwen/Qwen3-VL-8B-Instruct - Dataset:
top-papers/top-papers-graph-experts-data - Output prefix:
hf_top_papers_qwen3vl_8b - Uploaded at UTC:
2026-06-22T07:38:23Z
Loading note
The root of this repository is prepared as the final GRPO adapter directory. For
LoRA/PEFT loading, use the same base model listed above and load this repository
as the adapter. The complete SFT and GRPO directories are also preserved under
artifacts/ for auditability and reproducibility.