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# Command-R 35B — CPT (Continual Pretraining)
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**Model type:** Causal Language Model
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**Base model:** [CohereLabs/c4ai-command-r-v01](https://huggingface.co/CohereLabs/c4ai-command-r-v01)
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## Overview
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`commandr-CPT` is a **continual-pretrained** version of Cohere's Command-R 35B model, trained
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The continual pretraining was performed using Axolotl on the Leonardo EuroHPC system.
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**Adapter type:** LoRA
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**Precision:** bfloat16
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**Hardware:** 8 nodes × 2 × NVIDIA A100 64GB GPUs
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**Training duration:** 24 hours
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**Framework:** DeepSpeed ZeRO-1, Axolotl, PyTorch 2.5.1+cu121
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| Sequence length | 2048 |
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| Micro batch size | 1 |
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| Gradient accumulation | 4 |
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| LR scheduler | cosine |
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| Optimizer | AdamW (8-bit) |
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| LoRA rank (r) | 16 |
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| LoRA alpha | 32 |
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| LoRA dropout | 0.05 |
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# Command-R 35B — CPT (Continual Pretraining with LoRA)
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**Model type:** Causal Language Model
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**Base model:** [CohereLabs/c4ai-command-r-v01](https://huggingface.co/CohereLabs/c4ai-command-r-v01)
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## Overview
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`commandr-CPT` is a **continual-pretrained** version of Cohere's Command-R 35B model, trained with LoRA adapters for efficient enregy doman adaptation.
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The goal of CPT is to extend the model’s general reasoning, factual grounding, and domain knowledge across science, governance, and energy-domain text.
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The continual pretraining was performed using Axolotl on the Leonardo EuroHPC system.
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---
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**Adapter type:** LoRA
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**Precision:** bfloat16
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**Hardware:** 8 nodes × 2 × NVIDIA A100 64GB GPUs
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**Framework:** DeepSpeed ZeRO-1, Axolotl, PyTorch 2.5.1+cu121
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**Runtime:** ~24 hours
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**Checkpoints:** Saved every 1/5 of an epoch
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---
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| Sequence length | 2048 |
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| Micro batch size | 1 |
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| Gradient accumulation | 4 |
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| Epochs | 1 |
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| Max steps | 10000 |
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| Learning rate | 0.0002 |
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| LR scheduler | cosine |
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| Optimizer | AdamW (8-bit) |
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| Warmup steps | 10 |
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| Weight decay | 0.0 |
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| LoRA rank (r) | 16 |
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| LoRA alpha | 32 |
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| LoRA dropout | 0.05 |
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| LoRA target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| Gradient checkpointing | ✅ |
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| Flash attention | ✅ |
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| Auto resume | ✅ |
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| Loss watchdog threshold | 5.0 |
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| Loss watchdog patience | 3 |
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