Rewrite model card to match Laguna-XS.2-NVFP4 template

#1
by joerowell - opened
Files changed (4) hide show
  1. README.md +1 -20
  2. config.json +2 -2
  3. generation_config.json +2 -7
  4. tokenizer_config.json +3 -2
README.md CHANGED
@@ -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
@@ -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, SGLang, 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).
@@ -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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-
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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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-
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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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-
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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.
config.json CHANGED
@@ -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": 1.4158883083359672,
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  "partial_rotary_factor": 1.0
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  }
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  },
@@ -202,4 +202,4 @@
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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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+ }
generation_config.json CHANGED
@@ -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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- "tool_call_parser": "poolside_v1",
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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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+ }
 
 
 
 
 
tokenizer_config.json CHANGED
@@ -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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+ }