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MD-Mushfiqur123/lychee-gpt
lychee-gpt is a text generation model from MD-Mushfiqur123. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
Lychee GPT is a state-of-the-art conversational AI model, fine-tuned on a massive 31B parameter foundation model. Developed by MX LLMS, it delivers exceptional performance in both Bengali and English, featuring a mass…
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Updated Jun 27, 2026
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From the Hugging Face model README
Lychee GPT is a state-of-the-art conversational AI model, fine-tuned on a massive 31B parameter foundation model. Developed by MX LLMS, it delivers exceptional performance in both Bengali and English, featuring a massive 256K context window and stable generation of 11,000+ output tokens in a single pass.
Lychee GPT is the flagship model of MX LLMS, created to push the boundaries of long-context and long-output text generation for Bengali and English speakers. It is optimized for advanced instruction-following, complex reasoning, and dynamic conversational AI.
This model is designed for:
Can be fine-tuned further for specific domains like legal, medical, or customer support.
Lychee GPT inherits biases from its base foundation model. While fine-tuning on curated datasets reduces some harmful outputs, users should:
Users (both direct and downstream) should implement content moderation and validation layers when deploying in production. Always verify factual claims generated by the model.
Use the code below to load and run Lychee GPT instantly. The LoRA adapter will auto-detect the base model.
**Installation:**https://colab.research.google.com/#fileId=https://huggingface.co/mahin1234/lychee-gpt.ipynb kaggle.com/notebooks/new?accelerator=nvidiaTeslaT4&src=https://huggingface.co/mahin1234/lychee-gpt.ipynb
The model was fine-tuned on a custom conversational dataset curated from publicly available sources, including Wikipedia (Bengali & English), open-source GitHub repositories, and curated prompt-response pairs from open foundation models. All data was filtered for quality and cultural relevance.