Full fine-tuning recipe: SFT (supervised fine-tuning) on Mistral Small 3 via Axolotl, targeting AWS g5.12xlarge, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Qwen 2.5 7B via LitGPT, targeting AWS p4d.24xlarge, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Qwen 2.5 32B via torchtune, targeting AWS p4d.24xlarge, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Gemma 2 9B via Axolotl, targeting AWS p4d.24xlarge, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Gemma 2 27B via LitGPT, targeting Lambda Labs 8xH100, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Phi-4 via torchtune, targeting Lambda Labs 8xH100, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on DeepSeek-V3 base via Unsloth, targeting single A100 80GB, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Mixtral 8x7B via OpenRLHF, targeting single A100 80GB, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Yi 1.5 34B via DeepSpeed, targeting single A100 80GB, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Llama 3.3 70B via Unsloth, targeting single H100 80GB, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Llama 3.1 70B via OpenRLHF, targeting single H100 80GB, with data mix and eval plan.
Full fine-tuning recipe: SFT (supervised fine-tuning) on Mistral Small 3 via DeepSpeed, targeting 2x A100 80GB, with data mix and eval plan.