Command-R 35B — SFT (Supervised Fine-Tuning on Synthetic QA)

Name: commandr-35b-sft
Availability: InStock

Model type: Causal Language Model
Base model: CohereLabs/c4ai-command-r-v01
License: Apache 2.0
Framework: Axolotl

Overview

commandr-35b-sft is a supervised fine-tuned variant of Cohere’s Command-R 35B model.
Fine-tuning was performed on a high-quality instruction-following dataset using LoRA adapters, enabling improved conversational reasoning and question answering.

Training was conducted on the Leonardo EuroHPC system.

Training Setup

Objective: Supervised fine-tuning (instruction following)
Adapter type: LoRA
Precision: bfloat16
Hardware: 8 nodes × 2 × NVIDIA A100 64GB GPUs
Framework: DeepSpeed ZeRO-1, Axolotl, PyTorch 2.5.1+cu121
Runtime: ~6 hours
Dataset split: 70% train / 30% validation

Dataset

Name: axolotl_deduplicated_synthetic_qa.jsonl
Type: Instruction-following synthetic QA dataset

Each sample follows a QA/chat format used in the alpaca_chat.load_qa schema.

Hyperparameters

Parameter	Value
Sequence length	2048
Micro batch size	1
Gradient accumulation	2
Epochs	1
Learning rate	0.0001
LR scheduler	cosine
Optimizer	AdamW (8-bit)
Warmup steps	20
Weight decay	0.0
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
LoRA target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Gradient checkpointing	✅
Flash attention	✅
Auto resume	✅
Loss watchdog threshold	8.0
Loss watchdog patience	20

Tokenizer

Tokenizer type: AutoTokenizer
Special token: <|end_of_text|> as pad_token

ubitech-edg/commandr-35b-sft