Re-export flan-t5-small (loom-exporter f85a071-dirty)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+language:
+- en
+- fr
+- ro
+- de
+- multilingual
+base_model:
+- google/flan-t5-small
+pipeline_tag: text-generation
+tags:
+- loom
+- text2text-generation
+library_name: loom-py-rt
+---
+# FLAN-T5 Small
+
+Google's instruction-tuned T5-small, exported for loom.cpp. Family 6: text in, text out, through an encoder read once and a KV-cached decoder that cross-attends to it -- the first encoder-decoder TEXT model and the first SentencePiece Unigram vocabulary in this collection.
+
+This is a [loom.cpp](https://github.com/loom-ai-org/loom.cpp) export: a single self-describing GGUF
+that carries its own graph topologies, tokenizer (if any) and driver script, produced by
+[loom-exporter](https://github.com/loom-ai-org/loom-exporter).
+
+## Original model
+
+Exported from [`google/flan-t5-small`](https://huggingface.co/google/flan-t5-small). Weights are unmodified; this repo packages the same parameters into
+loom.cpp's GGUF format.
+
+## License
+
+`apache-2.0`, inherited from the base model above.
+
+## Language(s)
+
+`en`, `fr`, `ro`, `de`, `multilingual`
+
+## Usage
+
+Run it with [loom-py](https://github.com/loom-ai-org/loom-py) -- `loom-py-rt` on PyPI:
+
+```sh
+pip install -U "loom-py-rt[hub]"
+```
+
+```python
+import loom
+
+model = loom.Model.from_pretrained("loom-ai-org/flan-t5-small-loom")
+
+# The instruction is part of the text -- this is an instruction-TUNED encoder-decoder, not a chat
+# model, and it has no template to apply.
+print(model.text2text.infer(
+    "translate English to German: The weather today is cold and rainy in the north.",
+    max_new_tokens=32))
+
+# The same door, on the other tasks this checkpoint was tuned for:
+print(model.text2text.infer("Answer the following question. What is the capital of France?"))
+print(model.text2text.infer(
+    "summarize: The committee met on Tuesday to discuss the annual budget. After three hours of "
+    "debate, the members agreed to postpone the vote until the following month, citing incomplete "
+    "figures from the finance office.",
+    max_new_tokens=64))
+```
+
+### The layer underneath
+
+The call above is the high-level door: one per task, named for the modality pair it maps between, with
+the windowing, sampling and assembly this model needs already applied. Under it, `model.infer(...)`
+passes your arguments straight to the driver this GGUF embeds -- which is where you go for a knob the
+door does not name.
+
+`model.driver_source` prints that driver, including a header comment documenting every argument it
+accepts for this model, and is the authority on it. See [loom-py](https://github.com/loom-ai-org/loom-py) for the API and
+[loom.cpp](https://github.com/loom-ai-org/loom.cpp) for what the engine does between the two.
+
+## Known limitations
+
+**It is 80M parameters, and it answers like it.** flan-t5-small is the smallest member of the flan collection: it follows an instruction's SHAPE reliably and is often wrong about the content. Asked for the capital of France it says `london`, and `transformers` says the same thing on the same checkpoint -- that is the model, not the export. Use it to try the interface, and step up to `flan-t5-base` or larger for answers you intend to keep.
+
+**The instruction is part of the text.** There is no chat template and no system turn; prompts look like `translate English to German: ...` or `Answer the following question. ...`, which is how the checkpoint was tuned. A bare sentence with no instruction is out of distribution and usually comes back as a fragment of itself.
+
+**Greedy, not beam search.** The upstream `config.json` names 4 beams for its translation and summarization presets. The export decodes one token at a time from the argmax (or from the sampler, if you pass `temperature=`), so translations here are the greedy path through the same model rather than what the published presets would give.
+
+Sequences are capped at 512 tokens on each side. T5 has no learned position table, so nothing in the file stops a longer source -- but its relative-position buckets saturate at 128 tokens of distance and the KV cache is built at 512, which is the length this export is honest about.
+
+## Files
+
+- `flan-t5-small.gguf` -- the model, exported with loom-exporter.

flan-t5-small.gguf

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