Fix qwen3-0.6b LoRA training example

examples/gemma-27b-axolotl/training/README.md

@@ -1,14 +1,28 @@
-## Run instructions
+# Gemma 27B Fine-Tuning with Axolotl
 
-### Update config.yaml 
-- If pushing your checkpoints to Huggingface, make sure to update the `hub_model_id` in `config.yaml`, else comment out the lines with `hub_model_id` and `hub_strategy`. 
-- If using Weights&Biases to track your run, update `use_wandb`, `wandb_project` and `wandb_entity` accordingly. 
-- Hyper parameters, dataset processing and other important fields are defined in this yaml. Please go over all fields to make sure they work for your training use-case. 
+This example fine-tunes Google's Gemma 27B model using the Axolotl framework on Baseten.
 
-### Launch run 
+**Resources:** 2 nodes, 8x H100 GPUs each (16 GPUs total)
 
-```
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples gemma-27b-axolotl
+cd gemma-27b-axolotl
 truss train push config.py
 ```
 
-Upon successful submission, the CLI will output helpful information about your job, including the job-id to track your run.
+> **Note:** This example requires more than 4x H100 GPUs. You may need to [contact Baseten](https://www.baseten.co/contact) to get approval for this instance type before running the job.

examples/glm-4-6-msswift/training/README.md

@@ -0,0 +1,28 @@
+# GLM-4-6 Fine-Tuning with MS-Swift
+
+This example fine-tunes the GLM-4-6 model using the MS-Swift framework with MegatronLM on Baseten.
+
+**Resources:** 2 nodes, 8x H200 GPUs each (16 GPUs total)
+
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples glm-4-6-msswift
+cd glm-4-6-msswift
+truss train push config.py
+```
+
+> **Note:** This example requires H200 GPUs. You may need to [contact Baseten](https://www.baseten.co/contact) to get approval for this instance type before running the job.

examples/glm-4.7-flash-msswift/training/README.md

@@ -0,0 +1,28 @@
+# GLM-4.7 Flash Fine-Tuning with MS-Swift
+
+This example fine-tunes the GLM-4.7 Flash model using the MS-Swift framework with MegatronLM on Baseten.
+
+**Resources:** 1 node, 8x H100 GPUs
+
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples glm-4.7-flash-msswift
+cd glm-4.7-flash-msswift
+truss train push config.py
+```
+
+> **Note:** This example requires more than 4x H100 GPUs. You may need to [contact Baseten](https://www.baseten.co/contact) to get approval for this instance type before running the job.

examples/glm-4.7-msswift/training/README.md

@@ -0,0 +1,28 @@
+# GLM-4.7 Fine-Tuning with MS-Swift
+
+This example fine-tunes the GLM-4.7 model using the MS-Swift framework with MegatronLM on Baseten.
+
+**Resources:** 2 nodes, 8x H200 GPUs each (16 GPUs total)
+
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples glm-4.7-msswift
+cd glm-4.7-msswift
+truss train push config.py
+```
+
+> **Note:** This example requires H200 GPUs. You may need to [contact Baseten](https://www.baseten.co/contact) to get approval for this instance type before running the job.

examples/llama-8b-lora-unsloth/training/README.md

@@ -1,31 +1,26 @@
-# Llama 3.1 8B LoRA Training with Unsloth
+# Llama 3.1 8B LoRA Fine-Tuning with Unsloth
 
-This example demonstrates how to fine-tune [Llama 3.1 8B](https://huggingface.co/Meta-Llama-3.1-8B-Instruct) using LoRA (Low-Rank Adaptation) with [Unsloth](https://github.com/unslothai/unsloth), which provides significant speedups for training.
+This example fine-tunes Meta's Llama 3.1 8B Instruct model using LoRA with the Unsloth framework on Baseten.
 
-## Running the example
+**Resources:** 1 node, 1x H100 GPU
 
-### Install `truss` 
-Use the appropriate command for your package manager
-```bash
-# pip
-pip install -U truss
-# uv
-uv add truss && uv sync --upgrade-package truss
-```
-
-### Create the workspace for your training project
+## Prerequisites
 
-```bash
-# for the unsloth example
-truss train init --examples llama-8b-lora-unsloth && cd llama-8b-lora-unsloth
-```
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
 
-### Kick off the job
+## Getting Started
 
-Make sure you've plugged in proper secrets (e.g. wandb api key, huggingface token) via Baseten Secrets and Environment Variables, and kick off your job
+Initialize the example, navigate into the directory, and push the training job:
 
 ```bash
+truss train init --examples llama-8b-lora-unsloth
+cd llama-8b-lora-unsloth
 truss train push config.py
 ```
-
-For more details, take a look at the [docs](https://docs.baseten.co/training/overview)

examples/mnist-pytorch/training/README.md

@@ -0,0 +1,26 @@
+# MNIST Digit Classifier with PyTorch
+
+This example trains an MNIST digit classifier using PyTorch on Baseten.
+
+**Resources:** CPU only (4 vCPU, 16Gi memory)
+
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples mnist-pytorch
+cd mnist-pytorch
+truss train push config.py
+```

examples/nemotron3-30b-torchtitan/training/README.md

@@ -0,0 +1,46 @@
+# Nemotron-3 Nano 30B Training with TorchTitan
+
+This example demonstrates how to fine-tune NVIDIA's Nemotron-3 Nano 30B model using TorchTitan on Baseten.
+
+**Resources:** 1 node, 8x H200 GPUs
+
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples nemotron3-30b-torchtitan
+cd nemotron3-30b-torchtitan
+truss train push config.py
+```
+
+> **Note:** This example requires H200 GPUs. You may need to [contact Baseten](https://www.baseten.co/contact) to get approval for this instance type before running the job.
+
+## Serving the Model
+
+To serve this model after training:
+
+1. Generate the deployment config using the dry-run flag:
+   ```bash
+   truss train deploy_checkpoints --job-id=<your-job-id> --dry-run
+   ```
+
+2. Take the generated config and add the `--trust-remote-code` flag to the vLLM serve command.
+
+3. Deploy using `truss push`:
+   ```bash
+   truss push
+   ```
+
+**Note:** Deploying from the UI does not currently work for this model. We are adding support for this soon.

examples/orpheus-transformers/training/README.md

@@ -1,16 +1,26 @@
-# Orpheus + Transformers library
-This example demonstrates how to train [Orpheus](https://github.com/canopyai/Orpheus-TTS) on a specific voice dataset by training a [LoRA module](https://www.ibm.com/think/topics/lora). This allows you to customize the powerful Text-To-Speech (TTS) model to use accents and tones that fit your use case best.
+# Orpheus Fine-Tuning with Transformers
 
-## Run instructions
+This example fine-tunes the Orpheus audio model using the HuggingFace Transformers framework on Baseten.
 
-### Update config.py 
-- Make sure the environment variables in `config.py` is updated with the same name of secrets as saved in Baseten secrets.
-- `train.py` has the data processing calls, training code and hyper parameters defined. Update datasets, flags etc here or pass them as arguments through `run.sh` to change them. 
+**Resources:** 1 node, 1x H100 GPU
 
-### Launch run 
+## Prerequisites
 
-```
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples orpheus-transformers
+cd orpheus-transformers
 truss train push config.py
 ```
-
-Upon successful submission, the CLI will output helpful information about your job, including the job-id to track your run.

examples/oss-gpt-120b-axolotl/training/README.md

@@ -1,14 +1,28 @@
-## Run instructions
+# OSS GPT 120B Fine-Tuning with Axolotl
 
-### Update config.yaml 
-- If pushing your checkpoints to Huggingface, make sure to update the `hub_model_id` in `config.yaml`, else comment out the lines with `hub_model_id` and `hub_strategy`. 
-- If using Weights&Biases to track your run, update `use_wandb`, `wandb_project` and `wandb_entity` accordingly. 
-- Hyper parameters, dataset processing and other important fields are defined in this yaml. Please go over all fields to make sure they work for your training use-case. 
+This example fine-tunes the OSS GPT 120B model using the Axolotl framework on Baseten.
 
-### Launch run 
+**Resources:** 2 nodes, 8x H100 GPUs each (16 GPUs total)
 
-```
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples oss-gpt-120b-axolotl
+cd oss-gpt-120b-axolotl
 truss train push config.py
 ```
 
-Upon successful submission, the CLI will output helpful information about your job, including the job-id to track your run.
+> **Note:** This example requires more than 4x H100 GPUs. You may need to [contact Baseten](https://www.baseten.co/contact) to get approval for this instance type before running the job.

examples/oss-gpt-20b-axolotl/training/README.md

@@ -1,14 +1,26 @@
-## Run instructions
+# OSS GPT 20B Fine-Tuning with Axolotl
 
-### Update config.yaml 
-- If pushing your checkpoints to Huggingface, make sure to update the `hub_model_id` in `config.yaml`, else comment out the lines with `hub_model_id` and `hub_strategy`. 
-- If using Weights&Biases to track your run, update `use_wandb`, `wandb_project` and `wandb_entity` accordingly. 
-- Hyper parameters, dataset processing and other important fields are defined in this yaml. Please go over all fields to make sure they work for your training use-case. 
+This example fine-tunes the OSS GPT 20B model using the Axolotl framework on Baseten.
 
-### Launch run 
+**Resources:** 1 node, 4x H100 GPUs
 
-```
+## Prerequisites
+
+1. [Create a Baseten account](https://baseten.co/signup) if you don't already have one.
+2. Install the Truss CLI:
+   ```bash
+   # pip
+   pip install -U truss
+   # or uv
+   uv add truss
+   ```
+
+## Getting Started
+
+Initialize the example, navigate into the directory, and push the training job:
+
+```bash
+truss train init --examples oss-gpt-20b-axolotl
+cd oss-gpt-20b-axolotl
 truss train push config.py
 ```
-
-Upon successful submission, the CLI will output helpful information about your job, including the job-id to track your run.