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updated and tested playbook

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Robert McDermott 2025-11-01 17:56:51 -07:00
parent af0833dd53
commit 3c3da0a0a0
1 changed files with 10 additions and 7 deletions
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nvidia/unsloth/README.md
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@ -79,13 +79,16 @@ docker pull nvcr.io/nvidia/pytorch:25.09-py3
## Step 3. Launch Docker
```bash
docker run --gpus all --ulimit memlock=-1 -it --ulimit stack=67108864 --entrypoint /usr/bin/bash --rm nvcr.io/nvidia/pytorch:25.09-py3
mkdir -p ./outputs # directory to save the unsloth finetuned model results
docker run --gpus all \
--ulimit memlock=-1 --ulimit stack=67108864 -it --entrypoint /usr/bin/bash --rm \
-v "$(pwd)/outputs:/workspace/outputs" nvcr.io/nvidia/pytorch:25.09-py3
```
## Step 4. Install dependencies inside Docker
```bash
pip install transformers peft datasets "trl==0.19.1"
pip install transformers peft datasets 'trl==0.23.1' hf_transfer
pip install --no-deps unsloth unsloth_zoo
```
@ -96,10 +99,10 @@ pip install --no-deps bitsandbytes
## Step 6. Create Python test script
Curl the test script [here](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/unsloth/assets/test_unsloth.py) into the container.
Curl the test script [here](https://raw.githubusercontent.com/NVIDIA/dgx-spark-playbooks/refs/heads/main/nvidia/unsloth/assets/test_unsloth.py) into the container.
```bash
curl -O https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/unsloth/assets/test_unsloth.py
curl -O https://raw.githubusercontent.com/NVIDIA/dgx-spark-playbooks/refs/heads/main/nvidia/unsloth/assets/test_unsloth.py
```
We will use this test script to validate the installation with a simple fine-tuning task.
@ -123,13 +126,13 @@ Expected output in the terminal window:
Test with your own model and dataset by updating the `test_unsloth.py` file:
```python
## Replace line 32 with your model choice
## Replace line 49 with your model choice
model_name = "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
## Load your custom dataset in line 8
## Load your custom dataset in line 25
dataset = load_dataset("your_dataset_name")
## Adjust training parameter args at line 61
## Adjust training parameter args at line 80, and max_steps at line 83
per_device_train_batch_size = 4
max_steps = 1000
```