“Llama-3.2-1B-4bit → 464 tok/s”

“Cloudflare Workers API server was blocked from directly accessing Groq API. Resolved by using Cloudflare AI Gateway.”

“The results show that attention-based adversarial examples lead to measurable drops in evaluation performance while remaining semantically similar to the original inputs.”

“By varying epsilon on this one dim: Negative ε: outputs become restrained, procedural, and instruction-faithful Positive ε: outputs become more verbose, narrative, and speculative”

“Instruction-following capabilities improve substantially (+46% to +75% in IFEval for Llama-3.2-1B and 3B models).”

“Modern language models preserve the geometric substrate that enables Bayesian inference in wind tunnels, and organize their approximate Bayesian updates along this substrate.”

“The article's context, being a Hacker News post, likely focuses on technical details and community discussions regarding Llama-3.3-70B-Instruct.”

“Using Llama-3.1 70B on Groq to create o1-like reasoning chains.”

“This section would contain a direct quote from the article, likely highlighting a specific cost figure or a key finding about the economics of self-hosting.”

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“The article uses Monte Carlo Self-Refinement with LLaMA-3 8B.”

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“The current 7th-generation Phind Model is built on top of our open-source CodeLlama-34B fine-tunes that were the first models to beat GPT-4’s score on HumanEval and are still the best open source coding models overall by a wide margin.”

“We have fine-tuned CodeLlama-34B and CodeLlama-34B-Python on an internal Phind dataset that achieved 67.6% and 69.5% pass@1 on HumanEval, respectively. GPT-4 achieved 67%.”