Summary of Reddit Comments on "benchmark of qwen 2 5 72b"

Qwen 2.5 72B

• Description: Qwen 2.5 72B is an unquantized model that has received mixed reviews and comparisons with other models.
• Pros:
  • It has been praised for its high performance in some benchmarks.
  • Users appreciate the efforts made by the Qwen team to continuously improve and release new models.
• Cons:
  • Some users have expressed skepticism about benchmark numbers and trust issues with Chinese models.
  • Concerns have been raised about the lack of GQA and other features in Qwen models, leading some to consider them less useful for certain tasks.

Replete-LLM-V2.5-Qwen-32b

• Description: A model that is a continuous fine-tuned version of Qwen2.5-32B, incorporating continuous fine-tuning methods for better performance.
• Pros:
  • Integration of continuous fine-tuning methods for improved outcomes.
• Cons:
  • Questions about the effectiveness of tying base and instruct models without actual fine-tuning.

Mistral Large 2

• Description: An alternative model that has been noted to have higher Token Per Second (TPS) compared to other models.
• Pros:
  • Shows better TPS performance.
• Cons:
  • Some users feel conflicted about benchmarks and trust issues with leaderboards.

Mistral Small

• Description: Mentioned as an alternative that is potentially worth exploring for specific tasks.
• Pros:
  • Suggested for certain use cases or comparisons.
• Cons:
  • Limited details provided in the comments.

Gemma2:27b

• Description: Another model mentioned for potential comparison or consideration for tasks.
• Pros:
  • Noted as a good model for certain tasks.
• Cons:
  • Limited details provided in the comments.

General Insights

• Users express mixed trust in benchmark numbers and leaderboards.
• Concerns raised about Chinese models, dataset contamination, and the need for more transparent benchmarks.
• Some users question the effectiveness of certain training methods and the reliability of specific models for various tasks.
• Suggestions for using specific models based on performance, metrics, or individual preferences.

Overall, the comments provide a range of opinions and insights regarding the Qwen 2.5 72B model, its comparisons to other models, concerns about benchmark accuracy, and preferences for alternative models for various tasks.