# Archivo de prueba de LLM { #llm-test-file }

Este documento prueba si el <abbr title="Large Language Model – Modelo de lenguaje grande">LLM</abbr>, que traduce la documentación, entiende el `general_prompt` en `scripts/translate.py` y el prompt específico del idioma en `docs/{language code}/llm-prompt.md`. El prompt específico del idioma se agrega al final de `general_prompt`.

	ANDW	$0x6006000060060, R27, R5                        // ERROR "cannot use REGTMP as source"
	STP	(R3, R4), 0x1234567(R27)                         // ERROR "REGTMP used in large offset store"
	LDP	0x1234567(R27), (R3, R4)                         // ERROR "REGTMP used in large offset load"
	STP	(R26, R27), 700(R2)                              // ERROR "cannot use REGTMP as source"

Las claves del `dict` identifican cada ejemplo, y cada valor es otro `dict`.

Cada `dict` específico del ejemplo en los `examples` puede contener:

* `summary`: Descripción corta del ejemplo.
* `description`: Una descripción larga que puede contener texto Markdown.
* `value`: Este es el ejemplo real mostrado, e.g. un `dict`.

		}
		var terr zipindex.ErrNeedMoreData
		if errors.As(err, &terr) {
			size = int(terr.FromEnd)
			if size <= 0 || size > 100<<20 {
				return nil, ObjectInfo{}, errors.New("zip directory too large")
			}
		} else {
			return nil, ObjectInfo{}, err
		}
	}
}

// headObjectInArchiveFileHandler - HEAD Object in an archive file

    # Remove \r in case we run on Windows within Git Bash
    # and check out the repository with auto CRLF management
    # enabled. Otherwise, we may read lines that are delimited with
    # \r\n and produce $'-Xarg\r' rather than -Xarg due to word
    # splitting rules.
    tr -s '\r\n' ' ' <"$1"
  fi
}

log() {
  if [ "$MVNW_VERBOSE" = true ]; then
    printf '%s\n' "$1"
  fi
}

# LLM test file { #llm-test-file }

This document tests if the <abbr title="Large Language Model">LLM</abbr>, which translates the documentation, understands the `general_prompt` in `scripts/translate.py` and the language specific prompt in `docs/{language code}/llm-prompt.md`. The language specific prompt is appended to `general_prompt`.

Tests added here will be seen by all designers of language specific prompts.

Use as follows:

		// Part with size larger than 5 MiB.
		{bucketName, objectName, uploadIDs[0], 5, string(validPart), validPartMD5, int64(len(validPart))},
		{bucketName, objectName, uploadIDs[0], 6, string(validPart), validPartMD5, int64(len(validPart))},

		// Part with size larger than 5 MiB.
		// Parts uploaded for anonymous/unsigned API handler test.

///

## Zusätzliche Validierung { #additional-validation }

Wir werden sicherstellen, dass, obwohl `q` optional ist, wann immer es bereitgestellt wird, **seine Länge 50 Zeichen nicht überschreitet**.

### `Query` und `Annotated` importieren { #import-query-and-annotated }

Um dies zu erreichen, importieren Sie zuerst:

* `Query` von `fastapi`
* `Annotated` von `typing`

</p>

<p>
Reads of memory locations larger than a single machine word
are encouraged but not required to meet the same semantics
as word-sized memory locations,
observing a single allowed write <i>w</i>.
For performance reasons,
implementations may instead treat larger operations
as a set of individual machine-word-sized operations
in an unspecified order.

                assertEquals(expectedSize, actualEncoded, "Size and encode methods return different values for " + numChunks + " chunks");
            }
        }

        @Test
        @DisplayName("Should handle large number of chunks")
        void testLargeNumberOfChunks() {
            // Given
            byte[] sourceKey = new byte[SOURCE_KEY_SIZE];
            int chunkCount = 50;