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Model: "jamba"
Evals-based AI Engineering
jamba bamboo qwen-1.5-moe grok-1.5 llama2-7b openai mistral-ai x-ai llamaindex evaluation fine-tuning prompt-engineering voice-cloning quantization model-optimization code-generation context-windows hamel-husain alec-radford
Hamel Husain emphasizes the importance of comprehensive evals in AI product development, highlighting evaluation, debugging, and behavior change as key iterative steps. OpenAI released a voice engine demo showcasing advanced voice cloning from small samples, raising safety concerns. Reddit discussions introduced new models like Jamba (hybrid Transformer-SSM with MoE), Bamboo (7B LLM with high sparsity based on Mistral), Qwen1.5-MoE (efficient parameter activation), and Grok 1.5 (128k context length, surpassing GPT-4 in code generation). Advances in quantization include 1-bit Llama2-7B models outperforming full precision and the QLLM quantization toolbox supporting GPTQ/AWQ/HQQ methods.
Jamba: Mixture of Architectures dethrones Mixtral
jamba dbrx mixtral animatediff fastsd sdxs512-0.9 b-lora supir ai21-labs databricks together-ai hugging-face midjourney mixture-of-experts model-architecture context-windows model-optimization fine-tuning image-generation video-generation cpu-optimization style-content-separation high-resolution-upscaling
AI21 labs released Jamba, a 52B parameter MoE model with 256K context length and open weights under Apache 2.0 license, optimized for single A100 GPU performance. It features a unique blocks-and-layers architecture combining transformer and MoE layers, competing with models like Mixtral. Meanwhile, Databricks introduced DBRX, a 36B active parameter MoE model trained on 12T tokens, noted as a new standard for open LLMs. In image generation, advancements include Animatediff for video-quality image generation and FastSD CPU v1.0.0 beta 28 enabling ultra-fast image generation on CPUs. Other innovations involve style-content separation using B-LoRA and improvements in high-resolution image upscaling with SUPIR.