Add hf CLI equivalent to generated Inference Providers task pages

davanstrien · claude · davanstrien · commit c78c7babb831 · 2026-07-14T11:55:26.000+01:00
One-line change to TIP_LIST_MODELS_LINK_TEMPLATE in generate.ts + regeneration:
each task page's 'Explore all available models' line now also shows
'hf models ls --warm --pipeline-tag &lt;task&gt; --sort trending_score'.

Regeneration picks up routine data drift (token-classification output spec,
zai-org GLM-5.2-FP8 mapping) and prettier fixes in generate.ts.

Co-Authored-By: Claude Fable 5 &lt;noreply@anthropic.com&gt;
diff --git a/docs/inference-providers/providers/zai-org.md b/docs/inference-providers/providers/zai-org.md
@@ -56,7 +56,7 @@ Find out more about Chat Completion (LLM) [here](../tasks/chat-completion).
 
 <InferenceSnippet
     pipeline=text-generation
-    providersMapping={ {"zai-org":{"modelId":"zai-org/GLM-5.2","providerModelId":"glm-5.2"} } }
+    providersMapping={ {"zai-org":{"modelId":"zai-org/GLM-5.2-FP8","providerModelId":"glm-5.2"} } }
 conversational />
 
 
diff --git a/docs/inference-providers/tasks/audio-classification.md b/docs/inference-providers/tasks/audio-classification.md
@@ -28,7 +28,11 @@ Example applications:
 ### Recommended models
 
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=audio-classification&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=audio-classification&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag audio-classification --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/automatic-speech-recognition.md b/docs/inference-providers/tasks/automatic-speech-recognition.md
@@ -29,7 +29,11 @@ Example applications:
 
 - [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3): A powerful ASR model by OpenAI.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=automatic-speech-recognition&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=automatic-speech-recognition&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag automatic-speech-recognition --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/chat-completion.md b/docs/inference-providers/tasks/chat-completion.md
@@ -33,9 +33,12 @@ This is a subtask of [`text-generation`](https://huggingface.co/docs/inference-p
 #### Conversational Vision-Language Models (VLMs)
 
 - [zai-org/GLM-4.5V](https://huggingface.co/zai-org/GLM-4.5V): Cutting-edge reasoning vision language model.
-- [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct): Small yet powerful model.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-text-to-text&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-text-to-text&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag image-text-to-text --sort trending_score
+```
 
 ### API Playground
 
@@ -65,7 +68,7 @@ The API supports:
 
 <InferenceSnippet
     pipeline=text-generation
-    providersMapping={ {"cerebras":{"modelId":"openai/gpt-oss-120b","providerModelId":"gpt-oss-120b"},"cohere":{"modelId":"CohereLabs/tiny-aya-global","providerModelId":"tiny-aya-global"},"deepinfra":{"modelId":"zai-org/GLM-5.2","providerModelId":"zai-org/GLM-5.2"},"featherless-ai":{"modelId":"zai-org/GLM-5.2","providerModelId":"zai-org/GLM-5.2"},"fireworks-ai":{"modelId":"zai-org/GLM-5.2","providerModelId":"accounts/fireworks/models/glm-5p2"},"groq":{"modelId":"openai/gpt-oss-120b","providerModelId":"openai/gpt-oss-120b"},"novita":{"modelId":"zai-org/GLM-5.2","providerModelId":"zai-org/glm-5.2"},"nscale":{"modelId":"meta-llama/Llama-3.1-8B-Instruct","providerModelId":"meta-llama/Llama-3.1-8B-Instruct"},"ovhcloud":{"modelId":"openai/gpt-oss-120b","providerModelId":"gpt-oss-120b"},"publicai":{"modelId":"swiss-ai/Apertus-8B-Instruct-2509","providerModelId":"swiss-ai/apertus-8b-instruct"},"scaleway":{"modelId":"zai-org/GLM-5.2","providerModelId":"glm-5.2"},"together":{"modelId":"zai-org/GLM-5.2","providerModelId":"zai-org/GLM-5.2"},"zai-org":{"modelId":"zai-org/GLM-5.2","providerModelId":"glm-5.2"}} }
+    providersMapping={ {"cerebras":{"modelId":"openai/gpt-oss-120b","providerModelId":"gpt-oss-120b"},"cohere":{"modelId":"CohereLabs/tiny-aya-global","providerModelId":"tiny-aya-global"},"deepinfra":{"modelId":"zai-org/GLM-5.2","providerModelId":"zai-org/GLM-5.2"},"featherless-ai":{"modelId":"zai-org/GLM-5.2","providerModelId":"zai-org/GLM-5.2"},"fireworks-ai":{"modelId":"deepseek-ai/DeepSeek-V4-Pro","providerModelId":"accounts/fireworks/models/deepseek-v4-pro"},"groq":{"modelId":"openai/gpt-oss-120b","providerModelId":"openai/gpt-oss-120b"},"novita":{"modelId":"zai-org/GLM-5.2","providerModelId":"zai-org/glm-5.2"},"nscale":{"modelId":"meta-llama/Llama-3.1-8B-Instruct","providerModelId":"meta-llama/Llama-3.1-8B-Instruct"},"ovhcloud":{"modelId":"openai/gpt-oss-120b","providerModelId":"gpt-oss-120b"},"publicai":{"modelId":"swiss-ai/Apertus-8B-Instruct-2509","providerModelId":"swiss-ai/apertus-8b-instruct"},"scaleway":{"modelId":"meta-llama/Llama-3.1-8B-Instruct","providerModelId":"llama-3.1-8b-instruct"},"together":{"modelId":"deepseek-ai/DeepSeek-V4-Pro","providerModelId":"deepseek-ai/DeepSeek-V4-Pro"},"zai-org":{"modelId":"zai-org/GLM-5.2-FP8","providerModelId":"glm-5.2"}} }
 conversational />
 
 
@@ -75,7 +78,7 @@ conversational />
 
 <InferenceSnippet
     pipeline=image-text-to-text
-    providersMapping={ {"cerebras":{"modelId":"google/gemma-4-31B-it","providerModelId":"gemma-4-31b"},"cohere":{"modelId":"CohereLabs/aya-vision-32b","providerModelId":"c4ai-aya-vision-32b"},"deepinfra":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"MiniMaxAI/MiniMax-M3"},"featherless-ai":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"MiniMaxAI/MiniMax-M3"},"fireworks-ai":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"accounts/fireworks/models/minimax-m3"},"groq":{"modelId":"meta-llama/Llama-4-Scout-17B-16E-Instruct","providerModelId":"meta-llama/llama-4-scout-17b-16e-instruct"},"novita":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"minimax/minimax-m3"},"nscale":{"modelId":"meta-llama/Llama-4-Scout-17B-16E-Instruct","providerModelId":"meta-llama/Llama-4-Scout-17B-16E-Instruct"},"ovhcloud":{"modelId":"Qwen/Qwen3.6-27B","providerModelId":"Qwen3.6-27B"},"scaleway":{"modelId":"Qwen/Qwen3.6-35B-A3B","providerModelId":"qwen3.6-35b-a3b"},"together":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"MiniMaxAI/MiniMax-M3"},"zai-org":{"modelId":"zai-org/GLM-4.6V-Flash","providerModelId":"glm-4.6v-flash"}} }
+    providersMapping={ {"cerebras":{"modelId":"google/gemma-4-31B-it","providerModelId":"gemma-4-31b"},"cohere":{"modelId":"CohereLabs/aya-vision-32b","providerModelId":"c4ai-aya-vision-32b"},"deepinfra":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"MiniMaxAI/MiniMax-M3"},"featherless-ai":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"MiniMaxAI/MiniMax-M3"},"fireworks-ai":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"accounts/fireworks/models/minimax-m3"},"groq":{"modelId":"meta-llama/Llama-4-Scout-17B-16E-Instruct","providerModelId":"meta-llama/llama-4-scout-17b-16e-instruct"},"novita":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"minimax/minimax-m3"},"nscale":{"modelId":"meta-llama/Llama-4-Scout-17B-16E-Instruct","providerModelId":"meta-llama/Llama-4-Scout-17B-16E-Instruct"},"ovhcloud":{"modelId":"Qwen/Qwen3.6-27B","providerModelId":"Qwen3.6-27B"},"scaleway":{"modelId":"Qwen/Qwen3.5-397B-A17B","providerModelId":"qwen3.5-397b-a17b"},"together":{"modelId":"MiniMaxAI/MiniMax-M3","providerModelId":"MiniMaxAI/MiniMax-M3"},"zai-org":{"modelId":"zai-org/GLM-4.5V","providerModelId":"glm-4.5v"}} }
 conversational />
 
 
diff --git a/docs/inference-providers/tasks/feature-extraction.md b/docs/inference-providers/tasks/feature-extraction.md
@@ -29,7 +29,11 @@ Example applications:
 
 - [thenlper/gte-large](https://huggingface.co/thenlper/gte-large): A powerful feature extraction model for natural language processing tasks.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=feature-extraction&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=feature-extraction&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag feature-extraction --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/fill-mask.md b/docs/inference-providers/tasks/fill-mask.md
@@ -24,7 +24,11 @@ Mask filling is the task of predicting the right word (token to be precise) in t
 
 - [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base): A multilingual model trained on 100 languages.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=fill-mask&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=fill-mask&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag fill-mask --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/image-classification.md b/docs/inference-providers/tasks/image-classification.md
@@ -26,7 +26,11 @@ Image classification is the task of assigning a label or class to an entire imag
 - [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224): A robust image classification model.
 - [facebook/convnext-large-224](https://huggingface.co/facebook/convnext-large-224): A strong image classification model.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-classification&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-classification&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag image-classification --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/image-segmentation.md b/docs/inference-providers/tasks/image-segmentation.md
@@ -24,7 +24,11 @@ Image Segmentation divides an image into segments where each pixel in the image
 
 - [facebook/mask2former-swin-large-coco-panoptic](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic): Panoptic segmentation model trained on the COCO (common objects) dataset.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-segmentation&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-segmentation&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag image-segmentation --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/image-text-to-text.md b/docs/inference-providers/tasks/image-text-to-text.md
@@ -25,7 +25,11 @@ Image-text-to-text models take in an image and text prompt and output text. Thes
 - [zai-org/GLM-4.5V](https://huggingface.co/zai-org/GLM-4.5V): Cutting-edge reasoning vision language model.
 - [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct): Small yet powerful model.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-text-to-text&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-text-to-text&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag image-text-to-text --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/image-to-image.md b/docs/inference-providers/tasks/image-to-image.md
@@ -30,7 +30,11 @@ Example applications:
 - [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev): Powerful image editing model.
 - [kontext-community/relighting-kontext-dev-lora-v3](https://huggingface.co/kontext-community/relighting-kontext-dev-lora-v3): Image re-lighting model.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-to-image&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-to-image&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag image-to-image --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/object-detection.md b/docs/inference-providers/tasks/object-detection.md
@@ -24,7 +24,11 @@ Object Detection models allow users to identify objects of certain defined class
 
 - [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50): Solid object detection model pre-trained on the COCO 2017 dataset.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=object-detection&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=object-detection&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag object-detection --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/question-answering.md b/docs/inference-providers/tasks/question-answering.md
@@ -26,7 +26,11 @@ Question Answering models can retrieve the answer to a question from a given tex
 - [distilbert/distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad): Small yet robust model that can answer questions.
 - [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A special model that can answer questions from tables.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=question-answering&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=question-answering&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag question-answering --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/summarization.md b/docs/inference-providers/tasks/summarization.md
@@ -25,7 +25,11 @@ Summarization is the task of producing a shorter version of a document while pre
 - [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn): A strong summarization model trained on English news articles. Excels at generating factual summaries.
 - [Falconsai/medical_summarization](https://huggingface.co/Falconsai/medical_summarization): A summarization model trained on medical articles.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=summarization&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=summarization&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag summarization --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/table-question-answering.md b/docs/inference-providers/tasks/table-question-answering.md
@@ -24,7 +24,11 @@ Table Question Answering (Table QA) is the answering a question about an informa
 
 - [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A robust table question answering model.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=table-question-answering&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=table-question-answering&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag table-question-answering --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/text-classification.md b/docs/inference-providers/tasks/text-classification.md
@@ -28,7 +28,11 @@ Text Classification is the task of assigning a label or class to a given text. S
 - [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection): A model that can classify languages.
 - [meta-llama/Prompt-Guard-86M](https://huggingface.co/meta-llama/Prompt-Guard-86M): A model that can classify text generation attacks.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-classification&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-classification&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag text-classification --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/text-generation.md b/docs/inference-providers/tasks/text-generation.md
@@ -33,7 +33,11 @@ If you are interested in a Chat Completion task, which generates a response base
 - [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct): Text generation model used to write code.
 - [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1): Powerful reasoning based open large language model.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-generation&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-generation&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag text-generation --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/text-to-image.md b/docs/inference-providers/tasks/text-to-image.md
@@ -26,7 +26,11 @@ Generate an image based on a given text prompt.
 - [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image): A powerful image generation model.
 - [ByteDance/Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD): A powerful text-to-image model.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-to-image&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-to-image&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag text-to-image --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/text-to-video.md b/docs/inference-providers/tasks/text-to-video.md
@@ -25,7 +25,11 @@ Generate an video based on a given text prompt.
 - [tencent/HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo): A strong model for consistent video generation.
 - [Lightricks/LTX-Video-0.9.8-13B-distilled](https://huggingface.co/Lightricks/LTX-Video-0.9.8-13B-distilled): Very fast model for video generation.
 
-Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-to-video&sort=trending).
+Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=text-to-video&sort=trending), or from the terminal with the [`hf` CLI](https://huggingface.co/docs/huggingface_hub/package_reference/cli#hf-models-list):
+
+```bash
+hf models ls --warm --pipeline-tag text-to-video --sort trending_score
+```
 
 ### Using the API
 
diff --git a/docs/inference-providers/tasks/token-classification.md b/docs/inference-providers/tasks/token-classification.md
diff --git a/docs/inference-providers/tasks/translation.md b/docs/inference-providers/tasks/translation.md
diff --git a/docs/inference-providers/tasks/zero-shot-classification.md b/docs/inference-providers/tasks/zero-shot-classification.md
diff --git a/scripts/inference-providers/scripts/generate.ts b/scripts/inference-providers/scripts/generate.ts
