wehub-resource-sync
101 lines
2.9 KiB
ReStructuredText
101 lines
2.9 KiB
ReStructuredText
.. _recipe_gemma4:
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Gemma 4
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=======
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Validated models
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----------------
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- `google/gemma-4-31B-it <https://huggingface.co/google/gemma-4-31B-it>`_
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- `google/gemma-4-12B-it <https://huggingface.co/google/gemma-4-12B-it>`_
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- `google/gemma-4-E4B-it <https://huggingface.co/google/gemma-4-E4B-it>`_
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.. tab-set::
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:sync-group: engine
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.. tab-item:: vLLM
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**Engine documentation:**
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`Gemma 4 in vLLM supported models
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<https://docs.vllm.ai/en/latest/models/supported_models.html#multimodal-language-models>`_
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(architectures ``Gemma4ForConditionalGeneration`` for 31B/E4B and
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``Gemma4UnifiedForConditionalGeneration`` for 12B).
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**Status:** Validated with LMCache.
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Start the LMCache MP server:
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.. code-block:: bash
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lmcache server --l1-size-gb 100 --eviction-policy LRU
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Start vLLM with the LMCache MP connector:
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.. code-block:: bash
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vllm serve google/gemma-4-31B-it \
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--tensor-parallel-size 2 \
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--kv-transfer-config \
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'{"kv_connector":"LMCacheMPConnector", "kv_role":"kv_both"}'
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The smaller ``google/gemma-4-12B-it`` and ``google/gemma-4-E4B-it`` run on
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a single GPU:
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.. code-block:: bash
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vllm serve google/gemma-4-12B-it \
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--kv-transfer-config \
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'{"kv_connector":"LMCacheMPConnector", "kv_role":"kv_both"}'
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Adjust ``--tensor-parallel-size`` to match your hardware. For the
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generic LMCache + vLLM wiring (ports, remote hosts),
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see :doc:`../getting_started/quickstart`.
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If there are any issues with vLLM setup, please refer to the
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`vLLM Recipes <https://docs.vllm.ai/projects/recipes/en/latest/index.html>`_
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for more details.
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.. tab-item:: SGLang
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**Status:** Not validated with LMCache.
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.. tab-item:: TRT-LLM
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**Status:** Supported. See :doc:`../getting_started/quickstart` for TRT-LLM + LMCache setup.
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CacheBlend support
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------------------
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Compression support
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-------------------
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.. list-table::
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:header-rows: 1
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:widths: 25 20 55
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* - Method
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- Status
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- Notes
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* - :doc:`CacheGen <../kv_cache_optimizations/compression/cachegen>`
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- Not validated
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-
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Caveats
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-------
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- **Hybrid KV cache with heterogeneous block sizes.** Gemma 4 interleaves
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sliding-window and full-attention layers whose head dimensions differ
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(sliding 256, full 512), so vLLM unifies the physical page size by giving the
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two attention types different ``block_size``\ s (e.g. ``google/gemma-4-E4B-it``:
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sliding 32, full 16). LMCache stores and retrieves each KV cache group in its
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own block size; no extra flags are required.
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- **Cross-layer KV sharing.** ``google/gemma-4-E4B-it`` reuses some layers' KV
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caches across layers. LMCache stores the cache-owning layers only; the sharing
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layers' KV lives in the same blocks and is restored automatically.
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