Rewrite model card to match Laguna-XS.2-NVFP4 template
#1
by joerowell - opened
- README.md +1 -20
- config.json +2 -2
- generation_config.json +2 -7
- tokenizer_config.json +3 -2
README.md
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@@ -7,7 +7,6 @@ extra_gated_description: >-
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tags:
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- laguna-m.1
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- vllm
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- sglang
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- nvfp4
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- moe
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license: apache-2.0
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@@ -79,7 +78,7 @@ Laguna M.1-NVFP4 is a 225B total parameter Mixture-of-Experts model with 23B act
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## Usage
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Laguna M.1 has upstream support in vLLM,
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> [!NOTE]
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> For complete usage instructions, see the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1).
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@@ -102,24 +101,6 @@ vllm serve \
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--default-chat-template-kwargs '{"enable_thinking": true}'
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```
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#### SGLang
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The full SGLang recipe is on the [SGLang Cookbook](https://docs.sglang.io/cookbook/autoregressive/Poolside/Laguna-M.1). Quantization is detected automatically, so no extra flags are required.
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```shell
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git clone https://github.com/sgl-project/sglang.git
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cd sglang
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pip install -e "python[all]"
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sglang serve \
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--model-path poolside/Laguna-M.1-NVFP4 \
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--trust-remote-code \
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--reasoning-parser poolside_v1 \
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--tool-call-parser poolside_v1 \
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--tp 8 \
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--host 0.0.0.0 \
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--port 30000
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```
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#### TRT-LLM
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Laguna is supported in TensorRT-LLM thanks to the team at NVIDIA ([NVIDIA/TensorRT-LLM#13559](https://github.com/NVIDIA/TensorRT-LLM/pull/13559), with partial-RoPE fusion in [#15110](https://github.com/NVIDIA/TensorRT-LLM/pull/15110)). The full recipe is on the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1). Quantization is detected automatically from `quantization_config` in this checkpoint, so no extra flags are required.
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tags:
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- laguna-m.1
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- vllm
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- nvfp4
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- moe
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license: apache-2.0
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## Usage
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Laguna M.1 has upstream support in vLLM, and TRT-LLM thanks to the support of the team at NVIDIA.
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> [!NOTE]
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> For complete usage instructions, see the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1).
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--default-chat-template-kwargs '{"enable_thinking": true}'
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```
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#### TRT-LLM
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Laguna is supported in TensorRT-LLM thanks to the team at NVIDIA ([NVIDIA/TensorRT-LLM#13559](https://github.com/NVIDIA/TensorRT-LLM/pull/13559), with partial-RoPE fusion in [#15110](https://github.com/NVIDIA/TensorRT-LLM/pull/15110)). The full recipe is on the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1). Quantization is detected automatically from `quantization_config` in this checkpoint, so no extra flags are required.
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config.json
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@@ -41,7 +41,7 @@
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"original_max_position_embeddings": 4096,
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"beta_slow": 1.0,
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"beta_fast": 64.0,
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"attention_factor": 1.
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"partial_rotary_factor": 1.0
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}
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},
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"decoder_sparse_step": 1,
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"norm_topk_prob": true,
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"sliding_window": 0
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}
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"original_max_position_embeddings": 4096,
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"beta_slow": 1.0,
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"beta_fast": 64.0,
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"attention_factor": 1.0,
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"partial_rotary_factor": 1.0
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}
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},
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"decoder_sparse_step": 1,
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"norm_topk_prob": true,
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"sliding_window": 0
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}
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generation_config.json
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@@ -9,10 +9,5 @@
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"pad_token_id": 9,
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"temperature": 1.0,
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"top_p": 1.0,
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"min_p": 0.0
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"reasoning_parser": "poolside_v1",
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"default_chat_template_kwargs": {
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"enable_thinking": true
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}
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}
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"pad_token_id": 9,
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"temperature": 1.0,
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"top_p": 1.0,
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"min_p": 0.0
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}
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tokenizer_config.json
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@@ -571,5 +571,6 @@
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"pad_token": "〈|PAD|〉",
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"sep_token": "〈|SEP|〉",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "〈|UNK|〉"
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}
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"pad_token": "〈|PAD|〉",
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"sep_token": "〈|SEP|〉",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "〈|UNK|〉",
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"chat_template": "{% include 'chat_template.jinja' %}"
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}
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