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MiniCPM-V-4.7-35B-A3B-DSpark

LLMsAI Models & LLMsOpen source · Web

MiniCPM-V-4.7-35B-A3B-DSpark is a DSpark draft checkpoint model designed for pairing with MiniCPM-V-4.7-35B-A3B. It supports vision and text prompts, offering advanced capabilities in various domains.

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MiniCPM-V-4.7-35B-A3B-DSpark screenshot
MiniCPM-V-4.7-35B-A3B-DSpark screenshot

MiniCPM-V-4.7-35B-A3B-DSpark is a sophisticated DSpark draft checkpoint model developed for seamless integration with MiniCPM-V-4.7-35B-A3B and its tokenizer. This model is tailored to handle a variety of tasks across different domains, including Vision, Text, Math, and Code. It is trained using a mixture of Vision and Text prompts, ensuring versatility in application.

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The model features five draft layers and 389,223,169 draft parameters, with a draft token capacity of 15 per forward pass. It is designed with target hidden-state layers at specific intervals, enhancing its processing capabilities. The checkpoint precision is set at BF16, optimizing performance and efficiency.

Training involved generating responses from MiniCPM-V-4.7-35B-A3B using a comprehensive training recipe that includes 2 million training sequences over six epochs. The optimizer used is AdamW, with objectives focusing on CE, L1, and confidence loss.

Evaluation of the model is conducted across various domains, such as MathVista and OCRBench, with acceptance lengths ranging from 3.501 to 7.387. This ensures the model's robustness and adaptability to different tasks.

Despite its advanced capabilities, the model has limitations. It lacks autonomous intent and its outputs are based on statistical patterns, which may lead to inaccuracies or biases. Users are advised to employ the model ethically and configure appropriate safeguards.

  • CategoryAI Models & LLMs
  • Pricing ModelOpen source
  • DeploymentCloud
  • PlatformsWeb

  • DSpark draft checkpoint model

  • Integration with MiniCPM-V-4.7-35B-A3B

  • Vision and Text prompt support

  • Five draft layers

  • 389,223,169 draft parameters

  • BF16 checkpoint precision

  • 2 million training sequences

  • Evaluation across multiple domains

  1. Access model page: Visit the Hugging Face website

  2. Authenticate: Log in to your Hugging Face account

  3. Configure: Set up the model parameters as needed

  4. Send prompts: Input Vision or Text prompts

  5. Fine-tune: Adjust model settings for specific tasks

  • Vision Processing
  • Text Analysis
  • Mathematical Computation
  • Code Generation
  • Multimodal Integration

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