Feature Extraction
Transformers
English
retrieval
reasoning
embedding
BRIGHT
information-retrieval
Eval Results (legacy)
Instructions to use ForwardAILabs/MRE-T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ForwardAILabs/MRE-T1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ForwardAILabs/MRE-T1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ForwardAILabs/MRE-T1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
fix: correct base_model to Qwen3-4B-Instruct-2507
Browse files
README.md
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- information-retrieval
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library_name: transformers
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pipeline_tag: feature-extraction
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base_model: Qwen/Qwen3-4B
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datasets:
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- xlangai/BRIGHT
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model-index:
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## Training
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MRE-T1 is trained using a two-stage approach on the Qwen3-4B base model:
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1. **Stage 1**: Supervised fine-tuning with task-specific reasoning prompts
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2. **Stage 2**: Reinforcement learning to optimize retrieval quality
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- information-retrieval
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library_name: transformers
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pipeline_tag: feature-extraction
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- xlangai/BRIGHT
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model-index:
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## Training
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MRE-T1 is trained using a two-stage approach on the Qwen3-4B-Instruct-2507 base model:
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1. **Stage 1**: Supervised fine-tuning with task-specific reasoning prompts
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2. **Stage 2**: Reinforcement learning to optimize retrieval quality
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