Fix gating: per-element to match g_proj weights (was True/per-head)

.eval_results/swe-bench_pro.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: ScaleAI/SWE-bench_Pro
-    task_id: SWE_Bench_Pro
-  value: 49.2
-  source:
-    url: https://huggingface.co/poolside/Laguna-M.1
-    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: 74.6
-  source:
-    url: https://huggingface.co/poolside/Laguna-M.1
-    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: 45.8
-  source:
-    url: https://huggingface.co/poolside/Laguna-M.1
-    name: Model Card

LICENSE.md

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

@@ -7,10 +7,8 @@ extra_gated_description: >-
 tags:
 - laguna-m.1
 - vllm
-- sglang
 - bf16
 - moe
-license: apache-2.0
 pipeline_tag: text-generation
 ---
 
@@ -28,7 +26,7 @@ pipeline_tag: text-generation
 
 # Laguna M.1
 
-Laguna M.1 is a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token designed for agentic coding and long-horizon work.
+Laguna M.1 is a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token designed for agentic coding and long-horizon work. This release has upstream support in vLLM and is part of stable releases from version v0.21.0.
 
 > [!NOTE]
 > For more details on how we trained this model, including our Model Factory approach, post-training recipe, async off-policy agent RL, and evaluations, check out our [release blog post](https://poolside.ai/blog/laguna-a-deeper-dive) and [technical report](https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-report.pdf).
@@ -40,7 +38,6 @@ Laguna M.1 is a 225B total parameter Mixture-of-Experts model with 23B activated
 * **Global attention architecture**: Laguna M.1 uses global attention across all layers with 64 Q-heads, 8 KV-heads and softplus attention output gating
 * **Native reasoning support**: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request
 * **Strong agentic benchmark performance**: Laguna M.1 is competitive with state-of-the-art open-weight and frontier models on SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro and Terminal-Bench 2.0
-* **Apache 2.0 license**: Use and modify freely for commercial and non-commercial purposes
 
 ---
 
@@ -87,7 +84,7 @@ Laguna M.1 is a 225B total parameter Mixture-of-Experts model with 23B activated
 
 ## Usage
 
-Laguna M.1 has upstream support in vLLM, SGLang, and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA.
+Laguna M.1 is available through the Poolside API.
 
 ### pool
 
@@ -122,10 +119,10 @@ Submit feedback with `/feedback` and read the [full documentation on GitHub](htt
 Serve Laguna M.1 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 support landed in vLLM via [vllm-project/vllm#41129](https://github.com/vllm-project/vllm/pull/41129) (shared with [Laguna XS.2](https://huggingface.co/poolside/Laguna-XS.2)) and is available in vLLM 0.21.0 and later.
+> Laguna M.1 support is available in upstream vLLM.
 
 ```shell
-pip install 'vllm>=0.21.0'
+pip install vllm
 
 vllm serve \
     --model poolside/Laguna-M.1 \
@@ -136,85 +133,7 @@ vllm serve \
     --default-chat-template-kwargs '{"enable_thinking": true}'
 ```
 
-See the [vLLM recipes page](https://recipes.vllm.ai/poolside/Laguna-XS.2) for our Laguna XS.2 model with which the implementation is shared for additional deployment guidance. FP8 and NVFP4 quantized checkpoints are available at [Laguna-M.1-FP8](https://huggingface.co/poolside/Laguna-M.1-FP8) and [Laguna-M.1-NVFP4](https://huggingface.co/poolside/Laguna-M.1-NVFP4); quantization is detected automatically from `quantization_config`, so the same command works with the model ID substituted.
-
-#### SGLang
-
-Laguna M.1 can be served with SGLang using its OpenAI-compatible server, including support for tool calling, streaming responses, and reasoning parsing:
-
-> [!NOTE]
-> Laguna support was added to SGLang in [sgl-project/sglang#24204](https://github.com/sgl-project/sglang/pull/24204). The integration is shared with [Laguna XS.2](https://huggingface.co/poolside/Laguna-XS.2) and is currently available on SGLang main.
-
-```shell
-# Laguna M.1 support is currently on SGLang main, so install from source
-git clone https://github.com/sgl-project/sglang.git
-cd sglang
-pip install -e "python[all]"
-
-sglang serve \
-    --trust-remote-code \
-    --model-path poolside/Laguna-M.1 \
-    --tool-call-parser poolside_v1 \
-    --reasoning-parser poolside_v1 \
-    --tp 8 \
-    --host 0.0.0.0
-```
-
-Quantized Laguna M.1 checkpoints are also available as [Laguna-M.1-FP8](https://huggingface.co/poolside/Laguna-M.1-FP8) and [Laguna-M.1-NVFP4](https://huggingface.co/poolside/Laguna-M.1-NVFP4). SGLang reads the checkpoint `quantization_config`, so you can use the same launch command after replacing the model ID. For more SGLang-specific deployment details, see the [SGLang Cookbook](https://docs.sglang.io/cookbook/autoregressive/Poolside/Laguna-M.1).
-
-#### Transformers
-
-Laguna is supported in Transformers `v5.7.0` and later ([huggingface/transformers#45673](https://github.com/huggingface/transformers/pull/45673)).
-
-> [!NOTE]
-> Laguna M.1 is a 225B-parameter model; loading the BF16 checkpoint in Transformers requires substantial multi-GPU memory (`device_map="auto"` shards across available devices). For single-node serving, vLLM or SGLang is recommended.
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "poolside/Laguna-M.1"
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    dtype=torch.bfloat16,
-    device_map="auto",
-)
-
-messages = [
-    {"role": "user", "content": "Write a Python retry wrapper with exponential backoff."},
-]
-
-# Reasoning is on by default; pass enable_thinking=False to skip the <think> block.
-inputs = tokenizer.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    return_tensors="pt",
-    enable_thinking=True,
-).to(model.device)
-
-outputs = model.generate(inputs, max_new_tokens=1024, do_sample=True, temperature=1.0, top_k=20)
-print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
-```
-
-#### TRT-LLM
-
-Laguna is supported in TensorRT-LLM thanks to the team at NVIDIA — model support landed in [NVIDIA/TensorRT-LLM#13559](https://github.com/NVIDIA/TensorRT-LLM/pull/13559), with partial-RoPE fusion added in [#15110](https://github.com/NVIDIA/TensorRT-LLM/pull/15110). Build TensorRT-LLM from a `main` that includes these PRs (or a release once they ship).
-
-```python
-from tensorrt_llm import LLM, SamplingParams
-
-llm = LLM(model="poolside/Laguna-M.1", trust_remote_code=True)
-sampling = SamplingParams(max_tokens=1024, temperature=1.0, top_k=20)
-out = llm.generate(["Write a Python retry wrapper with exponential backoff."], sampling)
-print(out[0].outputs[0].text)
-```
-
-> [!NOTE]
-> If your TensorRT-LLM build pins `transformers < 4.58`, `configuration_laguna.py` needs a small compat shim; use the `laguna_minimal_overlay.sh` helper from the support PR and load TRT-LLM against the overlay directory.
-
-Quantization is detected automatically from `quantization_config`, so the same recipe works for the [FP8](https://huggingface.co/poolside/Laguna-M.1-FP8) and [NVFP4](https://huggingface.co/poolside/Laguna-M.1-NVFP4) variants with no extra flags.
+See the [vLLM recipes page](https://recipes.vllm.ai/poolside/Laguna-XS.2) for our Laguna XS.2 model with which the implementation is shared for additional deployment guidance.
 
 ## Controlling reasoning
 
@@ -326,12 +245,8 @@ for chunk in completion:
 
 For agentic coding use cases, we recommend enabling thinking and preserving reasoning in message history as outlined in the [Controlling reasoning](#controlling-reasoning) section.
 
-## License
-
-This model is licensed under the [Apache 2.0 License](https://huggingface.co/poolside/Laguna-M.1/blob/main/LICENSE.md).
-
 ## Intended and Responsible Use 
 
-Laguna M.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna M.1 is subject to the [Apache 2.0 License](https://huggingface.co/poolside/Laguna-M.1/blob/main/LICENSE.md), and should be used consistently with Poolside's [Acceptable Use Policy](https://poolside.ai/legal/acceptable-use-policy). We advise against circumventing Laguna M.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.
+Laguna M.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. We advise against circumventing Laguna M.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.
 
 Please report security vulnerabilities or safety concerns to [security@poolside.ai](mailto:security@poolside.ai).

config.json

@@ -14,7 +14,7 @@
   "num_attention_heads": 64,
   "num_key_value_heads": 8,
   "head_dim": 128,
-  "max_position_embeddings": 262144,
+  "max_position_embeddings": 131072,
   "attention_bias": false,
   "attention_dropout": 0.0,
   "rms_norm_eps": 1e-06,
@@ -45,7 +45,7 @@
     "full_attention": {
       "rope_theta": 500000.0,
       "rope_type": "yarn",
-      "factor": 64.0,
+      "factor": 32.0,
       "original_max_position_embeddings": 4096,
       "beta_slow": 1.0,
       "beta_fast": 64.0,

generation_config.json

@@ -9,10 +9,5 @@
   "pad_token_id": 9,
   "temperature": 1.0,
   "top_p": 1.0,
-  "min_p": 0.0,
-  "tool_call_parser": "poolside_v1",
-  "reasoning_parser": "poolside_v1",
-  "default_chat_template_kwargs": {
-    "enable_thinking": true
-  }
-}
+  "min_p": 0.0
+}

tokenizer_config.json

@@ -571,5 +571,6 @@
   "pad_token": "〈|PAD|〉",
   "sep_token": "〈|SEP|〉",
   "tokenizer_class": "PreTrainedTokenizerFast",
-  "unk_token": "〈|UNK|〉"
-}
+  "unk_token": "〈|UNK|〉",
+  "chat_template": "{% include 'chat_template.jinja' %}"
+}