diff --git a/README.md b/README.md
index 26927f3..f7be4c0 100644
--- a/README.md
+++ b/README.md
@@ -28,24 +28,26 @@
 
 ### Examples
 
-Solving a sequence classification task via soft prompt tuning of BLOOM-176B:
+Petals integrates seamlessly with PyTorch and the Hugging Face [Transformers](https://github.com/huggingface/transformers) library.
+
+This snippet shows how to **(a)** generate text with BLOOM and **(b)** solve a sequence classification task via soft prompt tuning:
 
 ```python
-# Initialize distributed BLOOM with soft prompts
-model = AutoModelForPromptTuning.from_pretrained(
-       "bigscience/distributed-bloom")
-# Define optimizer for prompts and linear head
-optimizer = torch.optim.AdamW(model.parameters())
+# Initialize distributed BLOOM and connect to the swarm
+model = DistributedBloomForCausalLM.from_pretrained(
+    "bigscience/distributed-bloom", tuning_mode="ptune", initial_peers=SEE_BELOW
+)  # Embeddings & prompts are on your device, BLOOM blocks are distributed
+
+print("Generated:", model.generate(tokenized_prefix, max_new_tokens=5))
 
+# Training (updates only local prompts / adapters)
+optimizer = torch.optim.AdamW(model.parameters())
 for input_ids, labels in data_loader:
-    # Forward pass with local and remote layers
     outputs = model.forward(input_ids)
     loss = cross_entropy(outputs.logits, labels)
-
-    # Distributed backward w.r.t. local params
-    loss.backward() # Compute model.prompts.grad
-    optimizer.step() # Update local params only
     optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
 ```
 
 ### 🚧 This project is in active development
@@ -76,6 +78,8 @@ This is important because it's technically possible for peers serving model laye
 
 ## Installation
 
+__[To be updated soon]__
+
 ```bash
 conda install -y -c conda-forge cudatoolkit-dev==11.3.1 cudatoolkit==11.3.1 cudnn==8.2.1.32
 pip install torch==1.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html