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Add Laguna XS.2.1 DFlash drafter support - #46853

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Add Laguna XS.2.1 DFlash drafter support#46853
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adamkbaranowski:adamkbaranowski/laguna-dflash

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@adamkbaranowski

@adamkbaranowski adamkbaranowski commented Jun 26, 2026 •
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Purpose

Add vLLM model support for Poolside Laguna XS.2.1 DFlash draft checkpoints.

This registers DFlashLagunaForCausalLM and implements the Laguna-specific DFlash drafter used with method="dflash" speculative decoding. The drafter consumes verifier auxiliary hidden states from the Laguna target model, precomputes context K/V for DFlash attention, and shares the target model token embeddings and LM head.

Changes:

  • Add vllm/model_executor/models/laguna_dflash.py.
  • Register DFlashLagunaForCausalLM in the model registry.
  • Mark Laguna DFlash as sharing target embed_tokens and lm_head.
  • Support Laguna attention gating, RMSNorm, SWA drafter layers, and DFlash context K/V precompute.

Test Plan

Test Result

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  • The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
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  • The test results, such as pasting the results comparison before and after, or e2e results
  • (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

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@mergify mergify Bot added new-model Requests to new models speculative-decoding v1 labels Jun 26, 2026
@adamkbaranowski
adamkbaranowski marked this pull request as ready for review June 26, 2026 18:31
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Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

benchislett
benchislett requested changes Jun 26, 2026
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Might be worth rebasing on top of #46104 which adds support for all-sliding-window DFlash.

Is there really enough complexity here to warrant a completely new model definition here? Ideally we'd be reusing a decent chunk of components and complexity from the existing dflash implementation.

benchislett
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Also, needs some kind of E2E test coverage or else it will be broken for sure

@adamkbaranowski

adamkbaranowski commented Jun 29, 2026

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Might be worth rebasing on top of #46104 which adds support for all-sliding-window DFlash.

Thanks @benchislett, I looked into #46104. Once it lands, I'm happy to rebase on top of it. I also tested cherry-picking #46104's non-merge commits locally and the Laguna-side adaptation looks fairly small: remove the local self.attn.sliding_window = None workaround and resolve the SWA window through the native DFlash/SWA config path. Given that, I don't think #46104 needs to block initial Day-0 Laguna DFlash support. This PR should be straightforward to rebase once that generic SWA path lands.

Is there really enough complexity here to warrant a completely new model definition here? Ideally we'd be reusing a decent chunk of components and complexity from the existing dflash implementation.

The part that remains model-specific is our draft module definition. We don't really fit existing DFlash implementation assumptions. Laguna has custom attention gating, LagunaMLP, different context KV precompute, a stripped draft checkpoint reusing the verifier's embeddings/lm_head, and Laguna-specific combine_hidden_states.

@adamkbaranowski

adamkbaranowski commented Jun 29, 2026

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Also, needs some kind of E2E test coverage or else it will be broken for sure

Happy to track adding one as a follow-up once the HF checkpoints are public. At the moment any test would need to be env-gated and would not run in CI by default. The changes here are scoped to the new Laguna DFlash draft model and should not affect existing DFlash users. I can add an optional smoke test now if you still prefer having it in this PR.

@benchislett

benchislett commented Jun 30, 2026

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@adamkbaranowski it still seems worthwhile to inherit from DFlash and reduce duplication in the parts that are shared.

benchislett
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Comment thread vllm/model_executor/models/laguna_dflash.py Outdated
@adamkbaranowski
adamkbaranowski force-pushed the adamkbaranowski/laguna-dflash branch from 42f87d2 to abd6f10 Compare June 30, 2026 17:55
@mergify mergify Bot added the qwen Related to Qwen models label Jun 30, 2026
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mergify Bot commented Jul 1, 2026

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This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @adamkbaranowski.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

benchislett
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Comment thread vllm/model_executor/models/laguna_dflash.py
benchislett
benchislett reviewed Jul 1, 2026
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Comment thread vllm/model_executor/models/laguna_dflash.py
benchislett
benchislett reviewed Jul 1, 2026
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Comment thread vllm/model_executor/models/laguna_dflash.py Outdated
benchislett
benchislett reviewed Jul 1, 2026
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Comment thread vllm/model_executor/models/laguna_dflash.py Outdated
@adamkbaranowski
adamkbaranowski force-pushed the adamkbaranowski/laguna-dflash branch from d31efa1 to e0ede93 Compare July 1, 2026 22:35
@adamkbaranowski
Add Laguna XS.2.1 DFlash drafter support
02f79af 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
@adamkbaranowski
Reuse Qwen DFlash control flow for Laguna
634fe63 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
@adamkbaranowski
Reuse Laguna layers for DFlash
8322425 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
@adamkbaranowski
Initialize Laguna DFlash mask embedding state
5ac9e27 
After vLLM vllm-project#46104, DFlashQwen3Model.embed_input_ids expects mask_token_id, mask_embedding, and has_separate_mask_embedding to exist. Laguna DFlash reuses that control flow without calling DFlashQwen3Model.__init__, so initialize the same mask state locally.

Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
@adamkbaranowski
adamkbaranowski force-pushed the adamkbaranowski/laguna-dflash branch from a775523 to 5ac9e27 Compare July 2, 2026 15:36
@adamkbaranowski
Add Laguna DFlash E2E smoke test
d1fb142 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
benchislett
benchislett reviewed Jul 2, 2026
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Comment thread vllm/model_executor/models/laguna_dflash.py Outdated
@adamkbaranowski
Fuse Laguna DFlash context KV precompute
17507df 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
@adamkbaranowski
Fix CI Laguna test by limiting DFlash regsitry smoke test size
fb5138c 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
@vllm-bot
vllm-bot merged commit 4c3c64f into vllm-project:main Jul 3, 2026
91 of 96 checks passed
jakki-amd pushed a commit to jakki-amd/vllm that referenced this pull request Jul 6, 2026
@adamkbaranowski
Add Laguna XS.2.1 DFlash drafter support (vllm-project#46853)
3e21fbf 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
DiegoCao added a commit to DiegoCao/vllm that referenced this pull request Jul 6, 2026
@DiegoCao @claude
[Spec Decode][DSpark] Add Gemma4-12B DSpark draft model
16c0bda 
Adds DSpark (semi-autoregressive block speculative decoding) support for
Gemma4-12B, for the deepseek-ai/dspark_gemma4_12b_block7 checkpoint.

Following the Laguna DFlash review (vllm-project#46853), this is a thin reuse of the
existing stacks rather than a standalone model:

- Gemma4DSparkModel subclasses DFlashQwen3Model and INHERITS the fused
  context-KV precompute (precompute_and_store_context_kv + _normalize_context_k
  + RoPE + cache-write). Gemma4's attention_k_eq_v is isolated behind the
  _build_context_kv_buffers / _project_context_kv hooks: the context is
  projected once through k_proj for all layers, K is returned raw (the inherited
  path applies k_norm + RoPE) and V = v_norm(that projection) with no RoPE.
  Only forward (sandwich norms + layer_scalar) and embed_input_ids
  (sqrt(hidden) scaling) are overridden.
- Gemma4DSparkForCausalLM subclasses Qwen3DSparkForCausalLM: markov_embed/
  markov_bias, embed_input_ids, forward, compute_logits and precompute
  delegation are inherited; only the self-contained checkpoint bits (own
  embed_tokens/lm_head, final-logit softcap, no draft->target id remap) and the
  flat weight loader are overridden.
- The Gemma4 attention/decoder-layer/MLP reuse the native Gemma4 MTP draft
  (Gemma4MTPAttention/Gemma4MTPDecoderLayer/Gemma4MLP); the DSpark attention
  only adds the K/V projections + norms.

registry.py registers Gemma4DSparkModel; config/speculative.py detects it as
method=dspark and normalizes target_layer_ids -> dspark_target_layer_ids and
block_size -> n_predict.

Validated on 1x H100 (TP=1): draft loads (67 params), server healthy, greedy
generation correct, and DSpark drafting/acceptance metrics recorded through the
inherited fused precompute (block-7, ~3 mean acceptance length).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
DiegoCao added a commit to DiegoCao/vllm that referenced this pull request Jul 7, 2026
@DiegoCao @claude
[Spec Decode][DSpark] Add Gemma4-12B DSpark draft model
3b006ea 
Adds DSpark (semi-autoregressive block speculative decoding) support for
Gemma4-12B, for the deepseek-ai/dspark_gemma4_12b_block7 checkpoint.

Following the Laguna DFlash review (vllm-project#46853), this is a thin reuse of the
existing stacks rather than a standalone model:

- Gemma4DSparkModel subclasses DFlashQwen3Model and INHERITS the fused
  context-KV precompute (precompute_and_store_context_kv + _normalize_context_k
  + RoPE + cache-write). Gemma4's attention_k_eq_v is isolated behind the
  _build_context_kv_buffers / _project_context_kv hooks: the context is
  projected once through k_proj for all layers, K is returned raw (the inherited
  path applies k_norm + RoPE) and V = v_norm(that projection) with no RoPE.
  Only forward (sandwich norms + layer_scalar) and embed_input_ids
  (sqrt(hidden) scaling) are overridden.
- Gemma4DSparkForCausalLM subclasses Qwen3DSparkForCausalLM: markov_embed/
  markov_bias, embed_input_ids, forward, compute_logits and precompute
  delegation are inherited; only the self-contained checkpoint bits (own
  embed_tokens/lm_head, final-logit softcap, no draft->target id remap) and the
  flat weight loader are overridden.
- The Gemma4 attention/decoder-layer/MLP reuse the native Gemma4 MTP draft
  (Gemma4MTPAttention/Gemma4MTPDecoderLayer/Gemma4MLP); the DSpark attention
  only adds the K/V projections + norms.

registry.py registers Gemma4DSparkModel; config/speculative.py detects it as
method=dspark and normalizes target_layer_ids -> dspark_target_layer_ids and
block_size -> n_predict.

Validated on 1x H100 (TP=1): draft loads (67 params), server healthy, greedy
generation correct, and DSpark drafting/acceptance metrics recorded through the
inherited fused precompute (block-7, ~3 mean acceptance length).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
DiegoCao added a commit to DiegoCao/vllm that referenced this pull request Jul 7, 2026
@DiegoCao @claude
[Spec Decode][DSpark] Add Gemma4-12B DSpark draft model
914da1d 
Adds DSpark (semi-autoregressive block speculative decoding) support for
Gemma4-12B, for the deepseek-ai/dspark_gemma4_12b_block7 checkpoint.

Following the Laguna DFlash review (vllm-project#46853), this is a thin reuse of the
existing stacks rather than a standalone model:

- Gemma4DSparkModel subclasses DFlashQwen3Model and INHERITS the fused
  context-KV precompute (precompute_and_store_context_kv + _normalize_context_k
  + RoPE + cache-write). Gemma4's attention_k_eq_v is isolated behind the
  _build_context_kv_buffers / _project_context_kv hooks: the context is
  projected once through k_proj for all layers, K is returned raw (the inherited
  path applies k_norm + RoPE) and V = v_norm(that projection) with no RoPE.
  Only forward (sandwich norms + layer_scalar) and embed_input_ids
  (sqrt(hidden) scaling) are overridden.
- Gemma4DSparkForCausalLM subclasses Qwen3DSparkForCausalLM: markov_embed/
  markov_bias, embed_input_ids, forward, compute_logits and precompute
  delegation are inherited; only the self-contained checkpoint bits (own
  embed_tokens/lm_head, final-logit softcap, no draft->target id remap) and the
  flat weight loader are overridden.
- The Gemma4 attention/decoder-layer/MLP reuse the native Gemma4 MTP draft
  (Gemma4MTPAttention/Gemma4MTPDecoderLayer/Gemma4MLP); the DSpark attention
  only adds the K/V projections + norms.

registry.py registers Gemma4DSparkModel; config/speculative.py detects it as
method=dspark and normalizes target_layer_ids -> dspark_target_layer_ids and
block_size -> n_predict.

Validated on 1x H100 (TP=1): draft loads (67 params), server healthy, greedy
generation correct, and DSpark drafting/acceptance metrics recorded through the
inherited fused precompute (block-7, ~3 mean acceptance length).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: DiegoCao <DiegoCao@users.noreply.github.com>
DiegoCao added a commit to DiegoCao/vllm that referenced this pull request Jul 7, 2026
@DiegoCao @claude
[Spec Decode][DSpark] Add Gemma4-12B DSpark draft model
4fcdb74 
Adds DSpark (semi-autoregressive block speculative decoding) support for
Gemma4-12B, for the deepseek-ai/dspark_gemma4_12b_block7 checkpoint.

Following the Laguna DFlash review (vllm-project#46853), this is a thin reuse of the
existing stacks rather than a standalone model:

- Gemma4DSparkModel subclasses DFlashQwen3Model and INHERITS the fused
  context-KV precompute (precompute_and_store_context_kv + _normalize_context_k
  + RoPE + cache-write). Gemma4's attention_k_eq_v is isolated behind the
  _build_context_kv_buffers / _project_context_kv hooks: the context is
  projected once through k_proj for all layers, K is returned raw (the inherited
  path applies k_norm + RoPE) and V = v_norm(that projection) with no RoPE.
  Only forward (sandwich norms + layer_scalar) and embed_input_ids
  (sqrt(hidden) scaling) are overridden.
- Gemma4DSparkForCausalLM subclasses Qwen3DSparkForCausalLM: markov_embed/
  markov_bias, embed_input_ids, forward, compute_logits and precompute
  delegation are inherited; only the self-contained checkpoint bits (own
  embed_tokens/lm_head, final-logit softcap, no draft->target id remap) and the
  flat weight loader are overridden.
- The Gemma4 attention/decoder-layer/MLP reuse the native Gemma4 MTP draft
  (Gemma4MTPAttention/Gemma4MTPDecoderLayer/Gemma4MLP); the DSpark attention
  only adds the K/V projections + norms.

registry.py registers Gemma4DSparkModel; config/speculative.py detects it as
method=dspark and normalizes target_layer_ids -> dspark_target_layer_ids and
block_size -> n_predict.

Validated on 1x H100 (TP=1): draft loads (67 params), server healthy, greedy
generation correct, and DSpark drafting/acceptance metrics recorded through the
inherited fused precompute (block-7, ~3 mean acceptance length).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: DiegoCao <DiegoCao@users.noreply.github.com>
DiegoCao added a commit to DiegoCao/vllm that referenced this pull request Jul 7, 2026
@DiegoCao @claude
[Spec Decode][DSpark] Add Gemma4-12B DSpark draft model
bc7d34e 
Adds DSpark (semi-autoregressive block speculative decoding) support for
Gemma4-12B, for the deepseek-ai/dspark_gemma4_12b_block7 checkpoint.

Following the Laguna DFlash review (vllm-project#46853), this is a thin reuse of the
existing stacks rather than a standalone model:

- Gemma4DSparkModel subclasses DFlashQwen3Model and INHERITS the fused
  context-KV precompute (precompute_and_store_context_kv + _normalize_context_k
  + RoPE + cache-write). Gemma4's attention_k_eq_v is isolated behind the
  _build_context_kv_buffers / _project_context_kv hooks: the context is
  projected once through k_proj for all layers, K is returned raw (the inherited
  path applies k_norm + RoPE) and V = v_norm(that projection) with no RoPE.
  Only forward (sandwich norms + layer_scalar) and embed_input_ids
  (sqrt(hidden) scaling) are overridden.
- Gemma4DSparkForCausalLM subclasses Qwen3DSparkForCausalLM: markov_embed/
  markov_bias, embed_input_ids, forward, compute_logits and precompute
  delegation are inherited; only the self-contained checkpoint bits (own
  embed_tokens/lm_head, final-logit softcap, no draft->target id remap) and the
  flat weight loader are overridden.
- The Gemma4 attention/decoder-layer/MLP reuse the native Gemma4 MTP draft
  (Gemma4MTPAttention/Gemma4MTPDecoderLayer/Gemma4MLP); the DSpark attention
  only adds the K/V projections + norms.

registry.py registers Gemma4DSparkModel; config/speculative.py detects it as
method=dspark and normalizes target_layer_ids -> dspark_target_layer_ids and
block_size -> n_predict.

Validated on 1x H100 (TP=1): draft loads (67 params), server healthy, greedy
generation correct, and DSpark drafting/acceptance metrics recorded through the
inherited fused precompute (block-7, ~3 mean acceptance length).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: DiegoCao <DiegoCao@users.noreply.github.com>
DiegoCao added a commit to DiegoCao/vllm that referenced this pull request Jul 7, 2026
@DiegoCao @claude
[Spec Decode][DSpark] Add Gemma4-12B DSpark draft model
1c84fba 
Adds DSpark (semi-autoregressive block speculative decoding) support for
Gemma4-12B, for the deepseek-ai/dspark_gemma4_12b_block7 checkpoint.

Following the Laguna DFlash review (vllm-project#46853), this is a thin reuse of the
existing stacks rather than a standalone model:

- Gemma4DSparkModel subclasses DFlashQwen3Model and INHERITS the fused
  context-KV precompute (precompute_and_store_context_kv + _normalize_context_k
  + RoPE + cache-write). Gemma4's attention_k_eq_v is isolated behind the
  _build_context_kv_buffers / _project_context_kv hooks: the context is
  projected once through k_proj for all layers, K is returned raw (the inherited
  path applies k_norm + RoPE) and V = v_norm(that projection) with no RoPE.
  Only forward (sandwich norms + layer_scalar) and embed_input_ids
  (sqrt(hidden) scaling) are overridden.
- Gemma4DSparkForCausalLM subclasses Qwen3DSparkForCausalLM: markov_embed/
  markov_bias, embed_input_ids, forward, compute_logits and precompute
  delegation are inherited; only the self-contained checkpoint bits (own
  embed_tokens/lm_head, final-logit softcap, no draft->target id remap) and the
  flat weight loader are overridden.
- The Gemma4 attention/decoder-layer/MLP reuse the native Gemma4 MTP draft
  (Gemma4MTPAttention/Gemma4MTPDecoderLayer/Gemma4MLP); the DSpark attention
  only adds the K/V projections + norms.

registry.py registers Gemma4DSparkModel; config/speculative.py detects it as
method=dspark and normalizes target_layer_ids -> dspark_target_layer_ids and
block_size -> n_predict.

Validated on 1x H100 (TP=1): draft loads (67 params), server healthy, greedy
generation correct, and DSpark drafting/acceptance metrics recorded through the
inherited fused precompute (block-7, ~3 mean acceptance length).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: DiegoCao <DiegoCao@users.noreply.github.com>
DiegoCao added a commit to DiegoCao/vllm that referenced this pull request Jul 7, 2026
@DiegoCao @claude
[Spec Decode][DSpark] Add Gemma4-12B DSpark draft model
42523cc 
Adds DSpark (semi-autoregressive block speculative decoding) support for
Gemma4-12B, for the deepseek-ai/dspark_gemma4_12b_block7 checkpoint.

Following the Laguna DFlash review (vllm-project#46853), this is a thin reuse of the
existing stacks rather than a standalone model:

- Gemma4DSparkModel subclasses DFlashQwen3Model and INHERITS the fused
  context-KV precompute (precompute_and_store_context_kv + _normalize_context_k
  + RoPE + cache-write). Gemma4's attention_k_eq_v is isolated behind the
  _build_context_kv_buffers / _project_context_kv hooks: the context is
  projected once through k_proj for all layers, K is returned raw (the inherited
  path applies k_norm + RoPE) and V = v_norm(that projection) with no RoPE.
  Only forward (sandwich norms + layer_scalar) and embed_input_ids
  (sqrt(hidden) scaling) are overridden.
- Gemma4DSparkForCausalLM subclasses Qwen3DSparkForCausalLM: markov_embed/
  markov_bias, embed_input_ids, forward, compute_logits and precompute
  delegation are inherited; only the self-contained checkpoint bits (own
  embed_tokens/lm_head, final-logit softcap, no draft->target id remap) and the
  flat weight loader are overridden.
- The Gemma4 attention/decoder-layer/MLP reuse the native Gemma4 MTP draft
  (Gemma4MTPAttention/Gemma4MTPDecoderLayer/Gemma4MLP); the DSpark attention
  only adds the K/V projections + norms.

registry.py registers Gemma4DSparkModel; config/speculative.py detects it as
method=dspark and normalizes target_layer_ids -> dspark_target_layer_ids and
block_size -> n_predict.

Validated on 1x H100 (TP=1): draft loads (67 params), server healthy, greedy
generation correct, and DSpark drafting/acceptance metrics recorded through the
inherited fused precompute (block-7, ~3 mean acceptance length).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: DiegoCao <DiegoCao@users.noreply.github.com>
lkk12014402 pushed a commit to lkk12014402/vllm that referenced this pull request Jul 8, 2026
@adamkbaranowski @lkk12014402
Add Laguna XS.2.1 DFlash drafter support (vllm-project#46853)
ca1fbdf 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
mayuyuace pushed a commit to mayuyuace/vllm that referenced this pull request Jul 9, 2026
@adamkbaranowski @mayuyuace
Add Laguna XS.2.1 DFlash drafter support (vllm-project#46853)
938fdd4 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
Signed-off-by: mayuyuace <qiming1.zhang@intel.com>
@SamuelOliveirads SamuelOliveirads mentioned this pull request Jul 14, 2026
Merged
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NickLucche pushed a commit to NickLucche/vllm that referenced this pull request Jul 15, 2026
@adamkbaranowski @NickLucche
Add Laguna XS.2.1 DFlash drafter support (vllm-project#46853)
5eeb558 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
philippesic pushed a commit to philippesic/vllm-semantic-cache that referenced this pull request Jul 19, 2026
@adamkbaranowski
Add Laguna XS.2.1 DFlash drafter support (vllm-project#46853)
cacac5b 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
plasticchris pushed a commit to plasticchris/vllm that referenced this pull request Jul 20, 2026
@adamkbaranowski
Add Laguna XS.2.1 DFlash drafter support (vllm-project#46853)
ea2f1c5 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
@ttupper92618 ttupper92618 mentioned this pull request Jul 22, 2026
Merged
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aditi-amd pushed a commit to aditi-amd/vllm that referenced this pull request Aug 4, 2026
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Add Laguna XS.2.1 DFlash drafter support (vllm-project#46853)
4132ebb 
Signed-off-by: Adam Baranowski <adam.baranowski@poolside.ai>
Signed-off-by: root <root@smci355-ccs-aus-m02-09.cs-aus.dcgpu>
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new-model Requests to new models qwen Related to Qwen models ready ONLY add when PR is ready to merge/full CI is needed speculative-decoding v1

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