qingy2024/SynGen-14B
SynGen-14B by qingy2024 is a 14 billion parameter large language model based on Qwen3-14B, specifically designed for synthetic grounded reasoning generation. It excels at transforming chat datasets into reasoning datasets, mimicking styles like DeepSeek R1 or OpenAI's GPT OSS. With a 32K context length, this model is optimized for tasks requiring explicit reasoning between user prompts and final outputs, particularly for dataset modification and generation.
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SynGen-14B: Synthetic Reasoning Generation
SynGen-14B is a 14 billion parameter language model developed by qingy2024, built upon the Qwen/Qwen3-14B architecture. Its core purpose is to generate synthetic grounded reasoning, acting as an intermediary step between a user's prompt and the model's final output. This capability is particularly useful for dataset modification, allowing users to convert standard chat datasets into reasoning-rich datasets, emulating the style of models like DeepSeek R1 or OpenAI's GPT OSS.
Key Capabilities
- Synthetic Reasoning Generation: Inserts explicit reasoning steps into model outputs.
- Dataset Transformation: Converts existing chat datasets into reasoning-focused datasets.
- Style Emulation: Can generate reasoning in the style of DeepSeek R1 or GPT OSS.
- Flexible Prompt Format: Utilizes specific tags (
<reasoning_style>,<system_prompt>,<user>,<assistant>,<think>) for structured input and output.
Training Details
- Base Model: Qwen/Qwen3-14B
- Training Method: Full Fine-Tune (FFT) over 2 epochs.
- Dataset: Based on Pinkstack/syngen-reasoning-0.6b-dataset.
- Total Tokens Trained: Approximately 211 million tokens.
Recommended Usage
For optimal performance and to prevent repetitive loops, it is recommended to use a temperature = 1.0 with default sampler settings when interacting with SynGen-14B.