Workflow:Bigscience workshop Petals Distributed Text Generation
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Distributed_Inference, Text_Generation |
| Last Updated | 2026-02-09 13:00 GMT |
Overview
End-to-end process for generating text from large language models (Llama, Falcon, BLOOM, Mixtral) using Petals' distributed swarm of volunteer-hosted GPU servers.
Description
This workflow enables users to run inference on models too large for a single machine by connecting to a decentralized network of servers, each hosting a contiguous range of transformer blocks. The client loads only the embedding and language model head locally while the hidden layers are executed remotely across the swarm. An inference session maintains persistent gRPC streams with servers, keeping KV caches alive across autoregressive generation steps. Routing is handled automatically via Dijkstra-based shortest-path algorithms over a hivemind DHT, with automatic failover if a server becomes unreachable.
Usage
Execute this workflow when you need to generate text from a large language model (7B to 405B+ parameters) but lack the GPU memory to load the full model locally. You have a prompt or set of prompts and want to produce completions using standard HuggingFace generation strategies (greedy, sampling, beam search). The public Petals swarm or a private swarm must be available.
Execution Steps
Step 1: Environment_Setup
Install the Petals library and its dependencies (PyTorch, HuggingFace Transformers, hivemind). If the target model requires gated access (e.g., Llama), authenticate with HuggingFace using a personal access token.
Key considerations:
- PyTorch version must match CUDA availability
- For gated models, run the HuggingFace CLI login before loading
- The hivemind library provides the DHT backbone for peer discovery
Step 2: Model_Loading
Load the distributed model and tokenizer using the HuggingFace AutoModel pattern. The client downloads only the embedding layer and LM head weights locally; all transformer block weights remain on remote servers. The model connects to the DHT to discover available servers.
Key considerations:
- Use AutoDistributedModelForCausalLM.from_pretrained() which wraps the standard HF loading
- The model's transformer blocks are replaced with a RemoteSequential module
- Initial peers default to the public swarm bootstrap nodes
- Only non-transformer-block weights are loaded into local memory
Step 3: Tokenization
Tokenize the input prompt into input IDs using the model's tokenizer. The tokenizer is loaded from the same HuggingFace model repository and runs entirely on the client side.
Key considerations:
- Use the correct tokenizer class for the model family
- Set appropriate padding and truncation for batch processing
- Return tensors in PyTorch format
Step 4: Inference_Session_Creation
Open an inference session that establishes persistent bidirectional gRPC streams with the servers covering all transformer blocks. The session pre-allocates KV cache memory on each server for the expected maximum sequence length.
Key considerations:
- Specify max_length to reserve server-side attention caches
- The RemoteSequenceManager uses Dijkstra routing to find the optimal server path
- Sessions are context-managed to ensure proper cleanup of server resources
Step 5: Autoregressive_Generation
Generate tokens one at a time using the model.generate() method. Each step sends the latest hidden states through the chain of remote servers, which process them through their hosted transformer blocks and return the updated hidden states. The LM head then produces the next token locally.
Key considerations:
- All standard HuggingFace generation parameters are supported (temperature, top_k, top_p, etc.)
- The session automatically handles server failures with retry logic and route reconstruction
- Each generation step reuses the KV caches from previous steps for efficiency
- Multiple generate() calls can be made within a single session for interactive use
Step 6: Output_Decoding
Decode the generated token IDs back into human-readable text using the tokenizer. Close the inference session to release server-side KV cache memory.
Key considerations:
- The session context manager handles cleanup automatically
- Server-side caches are freed when the session closes