minjaechoi/qwen36-recovery-v1

TEXT GENERATIONPricing:Input $0.4 / Cached $0.07 / Output $4Concurrent Unit Cost:2Model Size:35.1BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 12, 2026Architecture:Transformer Featherless Exclusive Cold

The minjaechoi/qwen36-recovery-v1 is a 35.1 billion parameter language model based on the Qwen architecture. This model appears to be a specialized variant, with its file structure suggesting a focus on recovery mechanisms or specific data processing tasks, potentially related to code or structured data. Its primary differentiation lies in its internal codebase, indicating a fine-tuned or experimental version for particular research or application domains. Developers might consider this model for tasks requiring specialized handling or analysis as indicated by its unique file references.

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Model Overview

The minjaechoi/qwen36-recovery-v1 is a 35.1 billion parameter model built upon the Qwen architecture. While specific training details and performance benchmarks are not provided in the README, the model's file structure offers insights into its potential focus and development.

Key Characteristics

The provided local code references suggest that this model is part of a project involving:

  • BranchKL On-Policy Prompts: Files like build_branchkl_onpolicy_prompts.py and generate_branchkl_rollouts.py indicate work related to reinforcement learning from human feedback (RLHF) or similar policy-based generation techniques, potentially for improving response quality or alignment.
  • Linear Recovery Mechanisms: The presence of qwen36_linear_recovery.py and train_qwen36_branchkl_recovery.py points towards specialized training or fine-tuning methods aimed at 'recovery,' which could imply error correction, data reconstruction, or robust performance under specific conditions.
  • Inference and Data Preparation: Scripts such as inference.py and prepare_nogpqa_training_corpus.py suggest a pipeline for model deployment and the preparation of unique training datasets, possibly for question-answering or specific domain adaptation.

Potential Use Cases

Given the specialized nature of its associated code, minjaechoi/qwen36-recovery-v1 could be suitable for:

  • Research in LLM Robustness: Exploring methods for model recovery from errors or handling noisy inputs.
  • Specialized Data Processing: Applications requiring specific data preparation or generation strategies as indicated by the nogpqa corpus preparation.
  • Advanced Fine-tuning Experiments: Developers interested in applying or extending the 'BranchKL' and 'linear recovery' techniques for custom tasks.

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