Welcome to the future of AI, where models like CausalLM 34B offer groundbreaking capabilities! This guide will walk you through its use, provide troubleshooting tips, and help you navigate potential pitfalls.
Getting Started with CausalLM 34B βDemo
The CausalLM 34B βDemo is designed to handle complex language tasks, making it crucial to understand how to set it up correctly. Here’s what you need to know:
Prompt Format
The model utilizes ChatML for structuring prompts. This ensures a coherent interaction with the model.
The Model Weight Caveat
Currently, there are known issues regarding precision with the model weights. If your outputs are subpar, it may stem from this. Future updates will address these problems, but patience is key.
Inferences and Frameworks
When using CausalLM 34B, the choice of inference framework is critical:
- **Avoid Accelerated Inference Frameworks:** Please refrain from using frameworks like **VLLM** for now, as they can significantly degrade output quality due to the precision issues mentioned earlier.
- **Preferred Inference Method:** Use the Transformers library for inference to maintain the quality of output.
- **For Faster Inference:** Consider utilizing the q8_0 quantization option which might yield better speed and performance compared to bf16 vllm in this scenario. Again, llama.cpp is a good alternative to explore during this interim period.
Special Considerations
It’s essential to note a few more restrictions:
- **No Repetition Penalty:** Ensure that the repetition penalty is set to zero for optimal output generation.
- **Wikitext Warning:** Do not utilize wikitext for quantization calibration. The model’s training dataset is misaligned with the characteristics of the original wikitext, which can lead to inconsistent results.
Performance Metrics
To measure the efficacy of different models against CausalLM 34B, we can examine contamination metrics:
| Models | MMLU Score (ref: llama-7b) |
|---|---|
| microsoftOrca-2-7b | 0.77 |
| mistralaiMistral-7B-v0.1 | 0.46 |
| CausalLM34b-beta | 0.38 |
| 01-aiYi-6B-200K | 0.3 |
This table illustrates the current performance landscape, highlighting that while CausalLM 34B has its limitations, ongoing improvements may change this in the future.
Troubleshooting Common Issues
Despite the sophisticated capabilities of CausalLM 34B, issues may arise. Here’s how to address them:
- Output Quality Degradation: If you notice a drop in output quality, recheck your chosen inference method and ensure you are not using accelerated frameworks.
- Precision Problems: Remember that some weight issues exist, and the model is continuously being improved to rectify these. Late breaking issues could be mitigated by opting for the q8_0 quantization option temporarily.
- Unfamiliarity with Prompt Format: Refer back to the ChatML documentation for effective structure.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.