The QwQ-LCoT-7B-Instruct is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on the amphora/QwQ-LongCoT-130K dataset, focusing on chain-of-thought (CoT) reasoning. 2. Model Sharding: — The model weights are split into 4 shards (safetensors) for efficient storage and download: — model-00001-of-00004.safetensors (4.88 GB) — model-00002-of-00004.safetensors (4.93 GB) — model-00003-of-00004.safetensors (4.33 GB) — model-00004-of-00004.safetensors (1.09 GB) 3. Tokenizer: — Byte-pair encoding (BPE) based. — Files included: — vocab.json (2.78 MB) — merges.txt (1.82 MB) — tokenizer.json (11.4 MB) — Special tokens mapped in specialtokensmap.json (e.g., , ). 4. Configuration Files: — config.json: Defines model architecture and hyperparameters. — generationconfig.json`: Settings for inference and text generation tasks. 1. Instruction Following: Handle user instructions effectively, even for multi-step tasks. 2. Reasoning Tasks: Perform logical reasoning and generate detailed step-by-step solutions. 3. Text Generation: Generate coherent, context-aware responses. To run your newly created…
Модальности:
Генерация текста
Области применения:
Математика Диалог / чат Следование инструкциям
Задача: Генерация текста
Автор: prithivMLmods
Теги: gguf, qwen2, Qwen2.5, Llama-Cpp, Math, CoT, Long-CoT, text-generation-inference
Лайков: 3 | Загрузок: 143
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
