diff --git a/examples/skypilot/README.md b/examples/skypilot/README.md
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+# Skypilot
+
+[SkyPilot](https://github.com/skypilot-org/skypilot) is a framework for easily running machine learning workloads on any cloud through a unified interface which makes it perfect for qlora finetunes.
+
+## Usage
+
+    # use pip install "skypilot[gcp,aws]" for whatever cloud you want to support
+    pip install "skypilot"
+
+    # make sure that sky check returns green for some providers
+    ./skypilot.sh
+
+This should give you something like this, depending on your cloud and settings and parameters:
+
+    ./skypilot.sh --cloud lambda --gpu H100:1
+    Task from YAML spec: qlora.yaml
+    == Optimizer ==
+    Target: minimizing cost
+    Estimated cost: $2.4 / hour
+        Considered resources (1 node):
+    ------------------------------------------------------------------------------------------------
+     CLOUD    INSTANCE           vCPUs   Mem(GB)   ACCELERATORS   REGION/ZONE   COST ($)   CHOSEN
+    ------------------------------------------------------------------------------------------------
+     Lambda   gpu_1x_h100_pcie   26      200       H100:1         us-east-1     2.40          ✔
+    ------------------------------------------------------------------------------------------------
+    Launching a new cluster 'qlora'. Proceed? [Y/n]: y
+
+
+Other, very sensible things to do are to pass --idle-minutes-to-autostop 60 so that the cluster shuts down after it's done. If your cloud provider supports spot instances than --use-spot can be ideal.
+
+Make sure that you either mount a /outputs directory or setup an automated upload to a cloud bucket after the training is done.
\ No newline at end of file
diff --git a/examples/skypilot/qlora.yaml b/examples/skypilot/qlora.yaml
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+name: qlora
+
+resources:
+  # any ampere+ works well, but since this is an example,
+  accelerators: A100:4
+
+  #add this on gcp:
+  disk_size: 100
+  disk_tier: 'high'
+
+num_nodes: 1
+
+#file_mounts:
+  # uplaod the latest training dataset if you have your own
+  # and then specifiy DATASET and DATASET_FORMAT below to match
+  # /data/train.jsonl: ./train.jsonl
+
+  # mount a bucket for saving results to
+  # /outputs:
+  #   name: outputs
+  #   mode: MOUNT
+
+setup: |
+
+  # Setup the environment
+  conda create -n qlora python=$PYTHON -y
+  conda activate qlora
+
+  pip install -U torch
+
+  git clone https://github.com/tobi/qlora.git
+  cd qlora
+  pip install -U -r requirements.txt
+
+  # periodic checkpoints go here
+  mkdir -p ~/local-checkpoints
+
+run: |
+
+  # Activate the environment
+  conda activate qlora
+  cd qlora
+
+  # let's double check that the output bucket exists,
+  # otherwise this trainig run will be for nothing
+  NUM_NODES=`echo "$SKYPILOT_NODE_IPS" | wc -l`
+  HOST_ADDR=`echo "$SKYPILOT_NODE_IPS" | head -n1`
+  LOCAL_CHECKPOINTS=~/local-checkpoints
+
+  echo "batch side: $PER_DEVICE_BATCH_SIZE"
+  echo "gradient steps: $GRADIENT_ACCUMULATION_STEPS"
+
+  # Turn off wandb if no api key is provided,
+  # add it with --env WANDB=xxx parameter to sky launch
+  if [ $WANDB_API_KEY == "" ]; then
+    WANDB_MODE="offline"
+  fi
+
+  # Run the training through torchrun for
+  torchrun \
+    --nnodes=$NUM_NODES \
+    --nproc_per_node=$SKYPILOT_NUM_GPUS_PER_NODE \
+    --master_port=12375 \
+    --master_addr=$HOST_ADDR \
+    --node_rank=$SKYPILOT_NODE_RANK \
+    qlora.py \
+    --model_name_or_path $MODEL_NAME \
+    --output_dir $LOCAL_CHECKPOINTS \
+    --logging_steps 10 \
+    --save_strategy steps \
+    --data_seed 42 \
+    --save_steps 500 \
+    --save_total_limit 3 \
+    --evaluation_strategy steps \
+    --eval_dataset_size 1024 \
+    --max_eval_samples 1000 \
+    --max_new_tokens 32 \
+    --dataloader_num_workers 3 \
+    --group_by_length \
+    --logging_strategy steps \
+    --remove_unused_columns False \
+    --do_train \
+    --do_eval \
+    --do_mmlu_eval \
+    --lora_r 64 \
+    --lora_alpha 16 \
+    --lora_modules all \
+    --double_quant \
+    --quant_type nf4 \
+    --bf16 \
+    --bits $BITS \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type constant \
+    --gradient_checkpointing False \
+    --dataset $DATASET \
+    --dataset-format $DATASET_FORMAT \
+    --source_max_len 16 \
+    --target_max_len 512 \
+    --per_device_train_batch_size $PER_DEVICE_BATCH_SIZE \
+    --per_device_eval_batch_size $PER_DEVICE_BATCH_SIZE \
+    --gradient_accumulation_steps $GRADIENT_ACCUMULATION_STEPS \
+    --max_steps 1875 \
+    --eval_steps 187 \
+    --learning_rate 0.0002 \
+    --adam_beta2 0.999 \
+    --max_grad_norm 0.3 \
+    --lora_dropout 0.05 \
+    --weight_decay 0.0 \
+    --run_name $SKYPILOT_JOB_ID \
+    --ddp_find_unused_parameters False \
+    --report_to wandb \
+    --seed 0
+
+
+  returncode=$?
+  RSYNC=rsync
+  # Sync any files not in the checkpoint-* folders, if we are on gcp use
+  # gsutil so that we can sync to a gs bucket. You can replace this
+  # code with anything that puts the model somewhere useful and permanent.
+  if command -v gsutil &> /dev/null; then
+      RSYNC="gsutil -m rsync"
+  fi
+  $RSYNC -r $LOCAL_CHECKPOINTS/ $OUTPUT_RSYNC_TARGET/
+  exit $returncode
+
+
+envs:
+  PYTHON: "3.10"
+  CUDA_MAJOR: "12"
+  CUDA_MINOR: "1"
+  MODEL_NAME: huggyllama/llama-7B
+
+  DATASET: alpaca
+  DATASET_FORMAT: alpaca
+
+  OUTPUT_RSYNC_TARGET: /outputs/qlora
+
+  BITS: 4
+  PER_DEVICE_BATCH_SIZE: 1
+  GRADIENT_ACCUMULATION_STEPS: 16 # apparently best to be 16x batch size, reduce for lower memory requirement
+
diff --git a/examples/skypilot/skypilot.sh b/examples/skypilot/skypilot.sh
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+sky launch -c qlora qlora.yaml \
+  --env WANDB_API_KEY=$WANDB_API_KEY \
+  $@