Update README.md

.eval_results/swe-bench_pro.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: ScaleAI/SWE-bench_Pro
-    task_id: SWE_Bench_Pro
-  value: 44.5
-  source:
-    url: https://huggingface.co/poolside/Laguna-XS.2
-    name: Model Card

.eval_results/swe-bench_verified.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: SWE-bench/SWE-bench_Verified
-    task_id: swe_bench_%_resolved
-  value: 68.2
-  source:
-    url: https://huggingface.co/poolside/Laguna-XS.2
-    name: Model Card

.eval_results/terminal-bench-2.0.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: harborframework/terminal-bench-2.0
-    task_id: terminalbench_2
-  value: 30.1
-  source:
-    url: https://huggingface.co/poolside/Laguna-XS.2
-    name: Model Card

LICENSE.md

@@ -1,202 +0,0 @@
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README.md

@@ -1,12 +1,11 @@
 ---
-library_name: transformers
+library_name: vllm
 inference: false
 extra_gated_description: >-
   To learn more about how we process your personal data, please read our <a
   href="https://poolside.ai/legal/privacy">Privacy Policy</a>.
 tags:
 - laguna-xs.2
-- vllm
 license: apache-2.0
 pipeline_tag: text-generation
 ---
@@ -52,9 +51,9 @@ Laguna XS.2 is a 33B total parameter Mixture-of-Experts model with 3B activated
 
 ## Benchmark results
 
-<p align="center">
-  <img alt="benchmarks" src="https://poolside.ai/assets/laguna/laguna-xs2-chart.svg" width="800px">
-</p>
+[Placeholder for chart SVG]
+
+We evaluate Laguna XS.2 with thinking enabled in our agent harness, pool (see the Usage section below to download and run locally), across all benchmarks. For other models, we use the best available publicly-reported score; if not available, we calculate baselines using OpenHands (SWE-bench family) or Terminus 2 (Terminal-Bench 2.0) using the settings below.
 
 | Model                     | Size (total params.) | SWE-bench Verified | SWE-bench Multilingual | SWE-bench Pro (Public Dataset) | Terminal-Bench 2.0 |
 |---------------------------|----------------------|--------------------|------------------------|--------------------------------|--------------------|
@@ -62,11 +61,10 @@ Laguna XS.2 is a 33B total parameter Mixture-of-Experts model with 3B activated
 | Devstral Small 2          | 24B dense            | 68.0%              | 55.7%                  | -                              | 22.5%              |
 | Gemma 4 31B IT            | 31B dense            | 52.0%              | 51.7%                  | 35.7%                          | 42.9%              |
 | Qwen3.5-35B-A3B           | 35B                  | 69.2%              | 60.3%                  | 44.6%                          | 40.5%              |
-| Qwen3.6-35B-A3B           | 35B                  | 73.4%              | 67.2%                  | 49.5%                          | 51.5%              |
-| Claude Haiku 4.5          | -                    | 73.3%              | -                      | 39.5%                          | 29.8%              |
 | GPT-5.4 Nano              | -                    | -                  | -                      | 52.4%                          | 46.3%              |
+| Qwen3.6-27B               | 27B dense            | 77.2%              | 71.3%                  | 53.2%                          | 59.3%              |
 
-*We used the highest publicly-referenced scores for all comparison models across each benchmark. In almost all cases these were official scores published in release blog posts or equivalent, with the exception of Gemma 4 31B IT where the highest published scores were [reported by the Qwen team](https://qwen.ai/blog?id=qwen3.6-35b-a3b) and Claude Haiku 4.5 where the highest published (verified) scores for SWE-bench Pro and Terminal-Bench 2.0 are from their respective official leaderboards.*
+*We used the highest publicly-referenced scores for all comparison models across each benchmark. In all cases these were official scores published in release blog posts or equivalent, with the exception of Gemma 4 31B IT where the highest published scores were [reported by the Qwen team](https://qwen.ai/blog?id=qwen3.6-35b-a3b).*
 
 <details>
 <summary>Expand for benchmarking methodology</summary>
@@ -82,7 +80,7 @@ All benchmarking for Laguna XS.2 was completed using the Laude Institute’s Har
 
 ## Usage
 
-Laguna XS.2 has launch-day support in vLLM and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA.
+Laguna XS.2 has launch-day support in vLLM and Transformers, and TRT-LLM and SGLang thanks to the support of the team at NVIDIA.
 
 The fastest way to get started is with our API, directly or using OpenRouter.
 
@@ -114,42 +112,39 @@ pool acp setup --editor zed|jetbrains
 Use pool with Ollama with one-command setup:
 
 ```shell
-ollama pull laguna-xs.2
-ollama launch pool --model laguna-xs.2
+ollama pull laguna.xs-2
+ollama launch pool --model laguna.xs-2
 ```
 
+(requires Ollama 0.20.8 or later)
+
 #### Feedback and issues
 
 Submit feedback with `/feedback` and read the [full documentation on GitHub](https://github.com/poolsideai/pool).
 
 ### Local deployment
 
-Laguna XS.2 is supported in vLLM and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA. Use Laguna-XS.2 with Ollama (with MLX support) and the mlx-lm framework for the best experience on your local machine.
+[vLLM, Transformers v5, TRT-LLM, SGLang, ...]
+
+Thanks to support from Ollama and the mlx-lm team...
+
+[Device frameworks: Ollama, mlx-lm, ...]
 
 #### vLLM
 
 Serve Laguna XS.2 locally with vLLM and query it from any OpenAI-compatible client (see [Controlling reasoning](#controlling-reasoning) for tool calls, streaming, and reasoning extraction):
 
-> [!NOTE]
-> Laguna XS.2 support has been merged into vLLM ([vllm-project/vllm#41129](https://github.com/vllm-project/vllm/pull/41129)) and will ship in the next release. Until then, install a nightly wheel:
-
 ```shell
-pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
-
-VLLM_USE_DEEP_GEMM=0 vllm serve \
-    --model poolside/Laguna-XS.2 \
-    --tool-call-parser poolside_v1 \
-    --reasoning-parser poolside_v1 \
-    --enable-auto-tool-choice \
-    --served-model-name laguna \
+pip install "vllm>=<PENDING_VERSION>"
+
+vllm serve poolside/Laguna-XS.2 \
+    --max-model-len 131072 \
     --default-chat-template-kwargs '{"enable_thinking": true}'
 ```
 
-See the [vLLM recipes page](https://recipes.vllm.ai/poolside/Laguna-XS.2) for additional deployment guidance.
-
 #### Transformers
 
-Laguna XS.2 is supported in Transformers `v5.7.0` and later ([huggingface/transformers#45673](https://github.com/huggingface/transformers/pull/45673)).
+> Requires `transformers >= <PENDING_VERSION>` (Laguna support is on the upcoming release; until then install from source).
 
 ```python
 import torch
@@ -188,53 +183,7 @@ response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=T
 print(response)
 ```
 
-#### TRT-LLM
-
-> [!NOTE]
-> Requires building TensorRT-LLM from the upstream PR that adds Laguna XS.2 support
-> ([NVIDIA/TensorRT-LLM#13559](https://github.com/NVIDIA/TensorRT-LLM/pull/13559)).
-> Once that PR merges, the same code will work on a released `tensorrt-llm` wheel.
-
-Laguna XS.2's `configuration_laguna.py` imports a few `transformers >= 4.58` symbols.
-TRT-LLM currently pins `transformers 4.57`, so the PR ships a `laguna_minimal_overlay.sh` script that symlinks the checkpoint and patches only the config file with a compat shim. Load TRT-LLM against the **overlay directory**, not the original checkpoint.
-
-```shell
-# 1. Check out the PR branch and build TRT-LLM from source (see the TensorRT-LLM build docs).
-git clone https://github.com/NVIDIA/TensorRT-LLM.git && cd TensorRT-LLM
-git fetch origin pull/13559/head:laguna && git checkout laguna
-
-# 2. Download the checkpoint.
-huggingface-cli download poolside/Laguna-XS.2 --local-dir ~/models/Laguna-XS.2
-
-# 3. Build the transformers-4.57 compat overlay (echoes the overlay path).
-OVERLAY=$(bash laguna_minimal_overlay.sh ~/models/Laguna-XS.2)
-```
-
-```python
-from tensorrt_llm import LLM, SamplingParams
-
-llm = LLM(
-    model=OVERLAY,             # overlay path, not the original checkpoint
-    trust_remote_code=True,
-    tensor_parallel_size=1,
-)
-
-sampling = SamplingParams(max_tokens=1024, temperature=0.7, top_k=20)
-out = llm.generate(["Write a Python retry wrapper with exponential backoff."], sampling)
-print(out[0].outputs[0].text)
-```
-
-Or serve with an OpenAI-compatible endpoint:
-
-```shell
-trtllm-serve "$OVERLAY" --port 8000 --trust-remote-code
-```
-
-The same recipe works for the [FP8](https://huggingface.co/poolside/Laguna-XS.2-FP8) and [NVFP4](https://huggingface.co/poolside/Laguna-XS.2-NVFP4) variants: quantization is detected automatically from `quantization_config`, no extra flags required.
-
-#### Ollama
-
-Visit [Ollama's model library](https://ollama.com/library/laguna-xs.2) to pull to your local machine.
+#### [Other frameworks]
 
 ## Controlling reasoning
 

config.json

@@ -180,4 +180,14 @@
     64,
     64,
     64
-  ]}
+  ],
+  "compression_config": {
+    "mode": null,
+    "group_size": 32,
+    "eps": 1e-05,
+    "filter_fqns": [
+      "output"
+    ],
+    "recompute_fake_quantize": false
+  }
+}