From 1767ed14d92bb90f743033ae0f8b6080ed7892f8 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Wed, 10 Jun 2026 15:02:23 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: North-ML1/willow-alpha Source: Original Platform --- .gitattributes | 35 + README.md | 15 + config.json | 25 + model.safetensors | 3 + model.safetensors.index.json | 225 + special_tokens_map.json | 14 + tokenizer.json | 81058 ++++++++++++++++ tokenizer_config.json | 17 + vision_adapter/README.md | 21 + vision_adapter/config.json | 8 + vision_adapter/forge_micro_vision.py | 38 + vision_adapter/vision_adapter/README.md | 21 + vision_adapter/vision_adapter/config.json | 8 + .../vision_adapter/forge_micro_vision.py | 38 + 14 files changed, 81526 insertions(+) create mode 100644 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message['content'] }}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}" +} diff --git a/vision_adapter/README.md b/vision_adapter/README.md new file mode 100644 index 0000000..202372b --- /dev/null +++ b/vision_adapter/README.md @@ -0,0 +1,21 @@ +--- +license: mit +--- + +# Forge-1V Micro Vision Adapter Scaffold + +This folder contains an experimental, untrained micro vision-adapter scaffold for future Forge-1V work. + +It is intentionally separate from the main text checkpoint: + +- The main `config.json` remains Llama-compatible. +- The GGUF export remains text-only. +- These files do not make the released model able to view images. + +Suggested target design: + +- Tiny patch encoder: 3-channel images to a small vision width. +- Projection: vision width to the 1024-dimensional Forge text hidden size. +- Prefix tokens: projected visual tokens can be prepended to the text sequence in a future custom multimodal training run. + +Approximate extra parameters for the scaffold design are well under 1M, keeping the total system under 400M parameters. diff --git a/vision_adapter/config.json b/vision_adapter/config.json new file mode 100644 index 0000000..e583d89 --- /dev/null +++ b/vision_adapter/config.json @@ -0,0 +1,8 @@ +{ + "image_size": 224, + "patch_size": 16, + "vision_width": 128, + "text_hidden_size": 1024, + "num_prefix_tokens": 16, + "status": "experimental_untrained_scaffold" +} diff --git a/vision_adapter/forge_micro_vision.py b/vision_adapter/forge_micro_vision.py new file mode 100644 index 0000000..2192150 --- /dev/null +++ b/vision_adapter/forge_micro_vision.py @@ -0,0 +1,38 @@ +from dataclasses import dataclass + +import torch +import torch.nn as nn + + +@dataclass +class ForgeMicroVisionConfig: + image_size: int = 224 + patch_size: int = 16 + vision_width: int = 128 + text_hidden_size: int = 1024 + num_prefix_tokens: int = 16 + + +class ForgeMicroVisionAdapter(nn.Module): + """Tiny untrained image-to-prefix adapter scaffold for Forge-1V experiments.""" + + def __init__(self, config: ForgeMicroVisionConfig = ForgeMicroVisionConfig()): + super().__init__() + self.config = config + self.patch_embed = nn.Conv2d( + 3, + config.vision_width, + kernel_size=config.patch_size, + stride=config.patch_size, + bias=False, + ) + self.pool = nn.AdaptiveAvgPool1d(config.num_prefix_tokens) + self.norm = nn.LayerNorm(config.vision_width) + self.proj = nn.Linear(config.vision_width, config.text_hidden_size, bias=False) + + def forward(self, images: torch.Tensor) -> torch.Tensor: + patches = self.patch_embed(images) + tokens = patches.flatten(2) + tokens = self.pool(tokens).transpose(1, 2) + tokens = self.norm(tokens) + return self.proj(tokens) diff --git a/vision_adapter/vision_adapter/README.md b/vision_adapter/vision_adapter/README.md new file mode 100644 index 0000000..202372b --- /dev/null +++ b/vision_adapter/vision_adapter/README.md @@ -0,0 +1,21 @@ +--- +license: mit +--- + +# Forge-1V Micro Vision Adapter Scaffold + +This folder contains an experimental, untrained micro vision-adapter scaffold for future Forge-1V work. + +It is intentionally separate from the main text checkpoint: + +- The main `config.json` remains Llama-compatible. +- The GGUF export remains text-only. +- These files do not make the released model able to view images. + +Suggested target design: + +- Tiny patch encoder: 3-channel images to a small vision width. +- Projection: vision width to the 1024-dimensional Forge text hidden size. +- Prefix tokens: projected visual tokens can be prepended to the text sequence in a future custom multimodal training run. + +Approximate extra parameters for the scaffold design are well under 1M, keeping the total system under 400M parameters. diff --git a/vision_adapter/vision_adapter/config.json b/vision_adapter/vision_adapter/config.json new file mode 100644 index 0000000..e583d89 --- /dev/null +++ b/vision_adapter/vision_adapter/config.json @@ -0,0 +1,8 @@ +{ + "image_size": 224, + "patch_size": 16, + "vision_width": 128, + "text_hidden_size": 1024, + "num_prefix_tokens": 16, + "status": "experimental_untrained_scaffold" +} diff --git a/vision_adapter/vision_adapter/forge_micro_vision.py b/vision_adapter/vision_adapter/forge_micro_vision.py new file mode 100644 index 0000000..2192150 --- /dev/null +++ b/vision_adapter/vision_adapter/forge_micro_vision.py @@ -0,0 +1,38 @@ +from dataclasses import dataclass + +import torch +import torch.nn as nn + + +@dataclass +class ForgeMicroVisionConfig: + image_size: int = 224 + patch_size: int = 16 + vision_width: int = 128 + text_hidden_size: int = 1024 + num_prefix_tokens: int = 16 + + +class ForgeMicroVisionAdapter(nn.Module): + """Tiny untrained image-to-prefix adapter scaffold for Forge-1V experiments.""" + + def __init__(self, config: ForgeMicroVisionConfig = ForgeMicroVisionConfig()): + super().__init__() + self.config = config + self.patch_embed = nn.Conv2d( + 3, + config.vision_width, + kernel_size=config.patch_size, + stride=config.patch_size, + bias=False, + ) + self.pool = nn.AdaptiveAvgPool1d(config.num_prefix_tokens) + self.norm = nn.LayerNorm(config.vision_width) + self.proj = nn.Linear(config.vision_width, config.text_hidden_size, bias=False) + + def forward(self, images: torch.Tensor) -> torch.Tensor: + patches = self.patch_embed(images) + tokens = patches.flatten(2) + tokens = self.pool(tokens).transpose(1, 2) + tokens = self.norm(tokens) + return self.proj(tokens)