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Fix broken modal.com URLs: blog typos and wrong doc paths #1503

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charlesfrye merged 1 commit into from
Feb 20, 2026
Merged

Fix broken modal.com URLs: blog typos and wrong doc paths #1503

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2 changes: 1 addition & 1 deletion 06_gpu_and_ml/llm-serving/ministral3_inference.py
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Expand Up @@ -70,7 +70,7 @@

# Native hardware support for FP8 formats in [Tensor Cores](https://modal.com/gpu-glossary/device-hardware/tensor-core)
# is limited to the latest [Streaming Multiprocessor architectures](https://modal.com/gpu-glossary/device-hardware/streaming-multiprocessor-architecture),
# like those of Modal's [Hopper H100/H200 and Blackwell B200 GPUs](https://modal.com/blog/announcing-h200-b200).
# like those of Modal's [Hopper H100/H200 and Blackwell B200 GPUs](https://modal.com/blog/introducing-b200-h200).

# At 80 GB VRAM, a single H100 GPU has enough space to store the 8B FP8 model weights (~8 GB)
# and a very large KV cache. A single H100 is also enough to serve the 14B model in full precision,
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2 changes: 1 addition & 1 deletion 06_gpu_and_ml/llm-serving/very_large_models.py
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Expand Up @@ -277,7 +277,7 @@ def _start_server() -> subprocess.Popen:
app = modal.App("example-serve-very-large-models", image=image)

# Most importantly, we need to decide what hardware to run on.
# [H200 and B200 GPUs](https://modal.com/blog/introducting-b200-h200)
# [H200 and B200 GPUs](https://modal.com/blog/introducing-b200-h200)
# have over 100 GB of [GPU RAM](https://modal.com/gpu-glossary/device-hardware/gpu-ram) --
# 141 GB and 180 GB, respectively.
# The model's weights will be stored in this memory,
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2 changes: 1 addition & 1 deletion 06_gpu_and_ml/llm-serving/vllm_inference.py
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Expand Up @@ -52,7 +52,7 @@
# We'll use an FP8 (eight-bit floating-point) post-training-quantized variant: `Qwen/Qwen3-4B-Thinking-2507-FP8`.
# Native hardware support for FP8 formats in [Tensor Cores](https://modal.com/gpu-glossary/device-hardware/tensor-core)
# is limited to the latest [Streaming Multiprocessor architectures](https://modal.com/gpu-glossary/device-hardware/streaming-multiprocessor-architecture),
# like those of Modal's [Hopper H100/H200 and Blackwell B200 GPUs](https://modal.com/blog/announcing-h200-b200).
# like those of Modal's [Hopper H100/H200 and Blackwell B200 GPUs](https://modal.com/blog/introducing-b200-h200).

# You can swap this model out for another by changing the strings below.
# A single H100 GPU has enough VRAM to store a 4,000,000,000 parameter model,
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2 changes: 1 addition & 1 deletion 06_gpu_and_ml/speech-to-text/sortformer2_1_speaker_diarization.py
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Expand Up @@ -74,7 +74,7 @@

# ## Using WebSockets to stream audio and diarization results

# We use a Modal [ASGI](https://modal.com/docs/guide/asgi) app to serve the diarization results
# We use a Modal [ASGI](https://modal.com/docs/guide/webhooks) app to serve the diarization results
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🚩 ASGI link text doesn't match new webhooks destination

The link text still says [ASGI] but the URL was changed from /docs/guide/asgi to /docs/guide/webhooks. If the webhooks page doesn't cover ASGI specifics, this could be misleading to readers. The PR checklist notes this was reviewed and the old /docs/guide/asgi page no longer exists, so /docs/guide/webhooks is the closest valid destination. However, it may be worth updating the link text from ASGI to something like ASGI/webhooks to better match the destination page content.

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# over WebSockets. This allows us to stream the diarization results to the client in real-time.

# We use a simple queue-based architecture to handle the audio and diarization results.
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2 changes: 1 addition & 1 deletion 13_sandboxes/sandbox_agent.py
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Expand Up @@ -15,7 +15,7 @@

app = modal.App.lookup("example-sandbox-agent", create_if_missing=True)

# First, we create a custom [Image](https://modal.com/docs/images) that has Claude Code
# First, we create a custom [Image](https://modal.com/docs/guide/images) that has Claude Code
# and git installed.

image = (
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