Fish twin blocks

README.md

@@ -15,7 +15,7 @@ The Hugging Face CLI is a powerful tool for interacting with the Hub. It allows
 
 Humans and agents can often discover how to use it via the `--help` command and the hints the CLI outputs. However, there are some cool tricks you might not discover straight away. This is a cheat sheet for them!
 
-Notes. Every line was run against `hf` 1.29 on 2026-09-16, with public ids where possible; lines with `YOU/...` need your own ids. Start a session with `hf auth whoami`. Lines are bash and zsh; loops use `xargs`, so most also run in fish unchanged. The four recipes that set variables carry a fish line, and the substitutions are listed under Patterns. Listings take `--json`, `-`-format quiet``, `--sort` and `--limit`; the default limit is 30 on models and datasets, 100 on jobs, 10 on collections.
+Notes. Every line was run against `hf` 1.29 on 2026-09-16, with public ids where possible; lines with `YOU/...` need your own ids. Start a session with `hf auth whoami`. Lines are bash and zsh; loops use `xargs`, so most also run in fish unchanged. Blocks that set a variable have a fish twin underneath. Listings take `--json`, `-`-format quiet``, `--sort` and `--limit`; the default limit is 30 on models and datasets, 100 on jobs, 10 on collections.
 
 ## Lineage: walk the graph between models, datasets, papers, and providers
 
@@ -106,6 +106,14 @@ hf models ls --search ocr --sort created_at --limit 500 --json \
   | jq -r --arg d "$D" '[.[] | select(.created_at > $d)] | sort_by(-.likes)[:10][] | "\(.likes)\t\(.created_at[:10])\t\(.id)"'
 ```
 
+Fish:
+
+```fish
+set D (date -v-3m +%F 2>/dev/null || date -d '3 months ago' +%F)
+hf models ls --search ocr --sort created_at --limit 500 --json \
+  | jq -r --arg d "$D" '[.[] | select(.created_at > $d)] | sort_by(-.likes)[:10][] | "\(.likes)\t\(.created_at[:10])\t\(.id)"'
+```
+
 Same idea for a whole task in the last year.
 
 ```bash
@@ -114,6 +122,14 @@ hf models ls --pipeline-tag image-text-to-text --sort created_at --limit 1000 --
   | jq -r --arg d "$D" '[.[] | select(.created_at > $d)] | sort_by(-.likes)[:10][] | "\(.likes)\t\(.id)"'
 ```
 
+Fish:
+
+```fish
+set D (date -v-12m +%F 2>/dev/null || date -d '12 months ago' +%F)
+hf models ls --pipeline-tag image-text-to-text --sort created_at --limit 1000 --json \
+  | jq -r --arg d "$D" '[.[] | select(.created_at > $d)] | sort_by(-.likes)[:10][] | "\(.likes)\t\(.id)"'
+```
+
 Tag namespaces `--filter` accepts but `--help` never lists: `license:`, `dataset:`, `arxiv:`, `base_model:`, plus bare language codes and library names.
 
 ```bash
@@ -352,6 +368,14 @@ hf jobs inspect $J --json | jq -r '.[0].status.stage'
 hf jobs logs $J -f
 ```
 
+Fish:
+
+```fish
+set J (hf jobs ps -a --json --limit 0 | jq -r 'max_by(.created_at) | .id')
+hf jobs inspect $J --json | jq -r '.[0].status.stage'
+hf jobs logs $J -f
+```
+
 Label at launch, filter later. A sweep becomes a queryable set. uv run prints id=... on stdout; extract it.
 
 ```bash
@@ -359,6 +383,13 @@ J=$(hf jobs uv run --flavor cpu-basic --label run=sweep42 --label model=qwen3-8b
 hf jobs ps -a --label run=sweep42 --json | jq -r '.[] | "\(.id)\t\(.status)"'
 ```
 
+Fish:
+
+```fish
+set J (hf jobs uv run --flavor cpu-basic --label run=sweep42 --label model=qwen3-8b --detach train.py 2>/dev/null | grep -oE '^id=[a-f0-9]+' | cut -d= -f2)
+hf jobs ps -a --label run=sweep42 --json | jq -r '.[] | "\(.id)\t\(.status)"'
+```
+
 Block until every job succeeds, then chain. Exit 0 only if all completed. `hf jobs hardware` lists flavors with $/hour.
 
 ```bash
@@ -414,6 +445,17 @@ hf endpoints update $E --container-args "--enable-auto-tool-choice --tool-call-p
 hf endpoints scale-to-zero $E
 ```
 
+Fish:
+
+```fish
+set E (hf endpoints ls --format quiet | head -1)
+hf endpoints describe $E --json | jq '{compute, model: .model.image}'
+hf endpoints update $E --min-replica 0 --max-replica 2 --scale-to-zero-timeout 15
+hf endpoints update $E --tensor-parallel-size 2
+hf endpoints update $E --container-args "--enable-auto-tool-choice --tool-call-parser hermes --max-model-len 32768"
+hf endpoints scale-to-zero $E
+```
+
 or the name you deployed above
 
 State and URL of your endpoints, ready for an OpenAI-compatible client.
@@ -547,6 +589,15 @@ hf models ls --filter automatic-speech-recognition --warm --limit 10 --format qu
 hf collections info $SLUG --json | jq '[.items[].item_id]'
 ```
 
+Fish:
+
+```fish
+set SLUG (hf collections create "Warm ASR models, Sep 2026" --private --json | jq -r '.slug')
+hf models ls --filter automatic-speech-recognition --warm --limit 10 --format quiet \
+  | xargs -I{} hf collections add-item $SLUG {} model --format quiet
+hf collections info $SLUG --json | jq '[.items[].item_id]'
+```
+
 ## Recipes: a few lines chained together
 
 Each one was run on 2026-09-16. Change the ids to suit.
@@ -573,7 +624,13 @@ hf models ls --search ocr --no-gated --num-parameters max:4B --apps vllm --sort
   | jq -r --arg d "$D" '[.[] | select(.created_at > $d)] | sort_by(-.likes)[:10][] | "\(.likes)\t\(.created_at[:10])\t\(.id)"'
 ```
 
-Fish: `set D (date -v-2m +%F 2>/dev/null; or date -d '2 months ago' +%F)`
+Fish:
+
+```fish
+set D (date -v-2m +%F 2>/dev/null || date -d '2 months ago' +%F)
+hf models ls --search ocr --no-gated --num-parameters max:4B --apps vllm --sort created_at --limit 300 --json \
+  | jq -r --arg d "$D" '[.[] | select(.created_at > $d)] | sort_by(-.likes)[:10][] | "\(.likes)\t\(.created_at[:10])\t\(.id)"'
+```
 
 **3.** "From a paper to a live API." Paper -> most liked model citing it that is warm -> which provider serves it.
 
@@ -583,7 +640,13 @@ hf models info $M --expand inferenceProviderMapping --json | jq -r '.inference_p
 #   Qwen/Qwen3-8B on nscale, featherless-ai
 ```
 
-Fish: `set M (hf models ls … --format quiet)`
+Fish:
+
+```fish
+set M (hf models ls --filter arxiv:2505.09388 --warm --sort likes --limit 1 --format quiet)
+hf models info $M --expand inferenceProviderMapping --json | jq -r '.inference_provider_mapping[] | select(.status=="live") | "\(.provider)\t\(.task)"'
+#   Qwen/Qwen3-8B on nscale, featherless-ai
+```
 
 **4.** "Which GGUF fits a 16 GB card?" Sizes without downloading, largest that fits first.
 
@@ -630,7 +693,14 @@ done
 hf jobs ps -a --label run=sweep42 --format quiet | xargs hf jobs wait --timeout 2h && hf buckets sync hf://buckets/YOU/sweep42 ./sweep42
 ```
 
-Fish: `for m in …; <same line>; end`
+Fish:
+
+```fish
+for m in Qwen/Qwen3-8B Qwen/Qwen3-4B Qwen/Qwen3-1.7B
+  hf jobs uv run --flavor l4x1 -s HF_TOKEN --label run=sweep42 --label model=$m --detach eval.py $m hf://buckets/YOU/sweep42 2>/dev/null | grep -oE '^id=[a-f0-9]+'
+end
+hf jobs ps -a --label run=sweep42 --format quiet | xargs hf jobs wait --timeout 2h && hf buckets sync hf://buckets/YOU/sweep42 ./sweep42
+```
 
 **10.** "Which trending papers have a model you can call right now?" Top 100 trending papers -> any warm model citing each. 100 calls, 8 in parallel, about 10 s.
 
@@ -648,11 +718,16 @@ SLUG=$(hf collections create "Warm OCR models under 4B, Sep 2026" --private --js
 hf models ls --search ocr --warm --num-parameters max:4B --sort likes --limit 10 --format quiet | xargs -I{} hf collections add-item $SLUG {} model --format quiet
 ```
 
-Fish: `set SLUG (hf collections create … | jq -r .slug)`
+Fish:
+
+```fish
+set SLUG (hf collections create "Warm OCR models under 4B, Sep 2026" --private --json | jq -r '.slug')
+hf models ls --search ocr --warm --num-parameters max:4B --sort likes --limit 10 --format quiet | xargs -I{} hf collections add-item $SLUG {} model --format quiet
+```
 
 ## Patterns
 
-- Fish users: three substitutions cover every line here. `X=$(cmd)` becomes `set X (cmd)`; `a || b` becomes `a; or b`; `for x in …; do …; done` becomes `for x in …; …; end`. `alias`, `$(…)` inside `sh `-c` '…'`, and `jq `--arg` d "$D"` work unchanged. Or run any line as `bash `-c` '…'`.
+- Fish users: every block that sets a variable has a fish twin underneath. Everything else runs unchanged; the only bash-isms fish rejects are `X=$(cmd)` and `for … do … done`.
 
 - `-`-format quiet`` when the next step is another command. `-`-json` | jq` when you need fields. Human output for the final answer only.
 - Sort by `created_at` or `trending_score` before you recommend a model. Default order is not recency.