#30: Mistral-7B Benchmarks ??
Benchmarking Mistral-7B
This blog benchmarks Mistral-7B to give you data points to assess areas where a model excels and where it struggles. In this blog, we have benchmarked the Mistral-7B-Instruct-v0.1 model from mistralai. The metrics that we have benchmarked for are:?
? Requests per second. (RPS)
? Latency
?Cost of deployment
Below is a quick snapshot of the benchmarking results for Mistral-7B-Instruct:
True ML Talks: LLMs at Intel ??
In this video, we have Ezequiel Lanza, AI Open Source Evangelist at Intel Corporation, to talk about:
? Emerging developments in generative AI training and deployment
? Optimization in open source LLMs
? Enhancing model performance, quantization, and adapting to low-rank settings
? LLMs performance enhancements - hardware and software interfaces
? In-depth analysis and practical applications of BigDL LLM
MLOps.Community Slack Discussions ??
Below are a summary of some of the informative conversations on the most popular MLOps community:
?? Success with the OpenAI fine-tuning feature
领英推荐
Summary: Fine-tuning LLMs like OpenAI's is complex, aiming to adapt general models for specific tasks or domains. Challenges include creating high-quality, domain-specific training data, managing hyperparameters to prevent overfitting, and quantifying performance, as LLM evaluations are often qualitative. Solutions that worked are RAG (finance).?
????LLMs to assess the quality of text
Summary: Using LLMs to assess the quality of sales emails offers potential efficiency gains but comes with limitations. LLMs can automate aspects of grammar, style, and basic coherence checks, yet they struggle with nuanced understanding of user-specific queries and intent. Currently, the best approach combines AI-driven analysis with human oversight, ensuring a balance between efficiency and accuracy.?
? ????Solving for value engineering GPT 3.5
Summary: In the context of developing AI-powered features for mobile applications, key challenges include managing the AI's verbosity and its tendency to generate list-based responses. Effective strategies to address these issues involve prompt engineering, where the AI is guided to produce more concise, conversational outputs. Implementing a post-response formatting step using a smaller, more efficient AI model can also help convert verbose or list-like outputs into more user-friendly, personalized advice.
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Brief about TrueFoundry!
Just as a reminder, for the new members of our community, TrueFoundry is a comprehensive ML/LLM Deployment PaaS, empowering ML Teams in enterprises to test, and deploy ML/LLMs?with ease while ensuring the following benefits:
??Full security for the infra team.
??40% lower costs due to Resource Management.
??90% faster deployments with SRE best practices.
For LLM/GPT style model deployment, we allow users to select pre-configured models from our catalog and fine-tune them on their datasets.
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