llm-layer-collector

Load and dispatch individual transformer layers from HuggingFace model checkpoints, rather than instantiating a whole model at once. Given a model directory (config + safetensors shards), it can materialize just the embedding, a range of decoder layers, the final norm, or the LM head — each as a standalone torch.nn.Module — and run computation through them.

This powers the layer-sharding used by language-pipes for distributed inference, but has no dependency on it and can be used on its own.

Install

pip install llm-layer-collector

Usage

from llm_layer_collector import LlmLayerCollector

collector = LlmLayerCollector(model_dir="/path/to/model", cache_file="cache.json")
embedding = collector.load_input_embedding()
layers = collector.load_layer_set(0, 4)   # decoder layers 0..4 (end inclusive)
norm = collector.load_norm()
head = collector.load_head()

Supported architectures

Llama, Phi-3, Qwen3, Qwen3-MoE, Gemma3, Gemma4, Ministral3. See src/llm_layer_collector/modeling/ for per-architecture support.

License

MIT

Development Status
- 4 - Beta
Intended Audience
- Science/Research
License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python :: 3.10
Topic
- Scientific/Engineering :: Artificial Intelligence
Typing
- Typed

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