README.md

---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
base_model:
- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
new_version: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
pipeline_tag: summarization
library_name: flair
tags:
- legal
---
	/**
	 * Key to config.json file.
	 */
	key:          string;
	etag:         string;
	lastModified: Date;
	size:         number;
	modelId:      ModelId;
	author?:      AuthorId;
	siblings:     IS3ObjectWRelativeFilename[];
	config:       Obj;
	configTxt?:   string;  /// if flag is set when fetching.
	downloads?:   number;  /// if flag is set when fetching.
	naturalIdx:   number;
	cardSource?:  Source;
	cardData?:    Obj;

	constructor(o: Partial<ModelInfo>) {
		return Object.assign(this, o);
	}
	
	get jsonUrl(): string {
		return Bucket.R.models.urlForKey(this.key);
	}
	
	get cdnJsonUrl(): string {
		return Bucket.R.models.cdnUrlForKey(this.key);
	}
	
	async validate(): Promise<Ajv.ErrorObject[] | undefined> {
		const jsonSchema = JSON.parse(
			await fs.promises.readFile(CONFIG_JSON_SCHEMA, 'utf8')
		);
		const ajv = new Ajv();
		ajv.validate(jsonSchema, this.config);
		return ajv.errors ?? undefined;
	}
	
	/**
	 * Readme key, w. and w/o S3 prefix.
	 */
	get readmeKey(): string {
		return this.key.replace("config.json", "README.md");
	}
	get readmeTrimmedKey(): string {
		return Utils.trimPrefix(this.readmeKey, S3_MODELS_PREFIX);
	}
	
	/**
	 * ["pytorch", "tf", ...]
	 */
	get mlFrameworks(): string[] {
		return Object.keys(FileType).filter(k => {
			const filename = FileType[k];
			const isExtension = filename.startsWith(".");
			return isExtension 
				? this.siblings.some(sibling => sibling.rfilename.endsWith(filename))
				: this.siblings.some(sibling => sibling.rfilename === filename);
		});
	}
	/**
	 * What to display in the code sample.
	 */
	get autoArchitecture(): string {
		const useTF = this.mlFrameworks.includes("tf") && ! this.mlFrameworks.includes("pytorch");
		const arch = this.autoArchType[0];
		return useTF ? `TF${arch}` : arch;
	}
	get autoArchType(): [string, string | undefined] {
		const architectures = this.config.architectures;
		if (!architectures || architectures.length === 0) {
			return ["AutoModel", undefined];
		}
		const architecture = architectures[0].toString() as string;
		if (architecture.endsWith("ForQuestionAnswering")) {
			return ["AutoModelForQuestionAnswering", "question-answering"];
		}
		else if (architecture.endsWith("ForTokenClassification")) {
			return ["AutoModelForTokenClassification", "token-classification"];
		}
		else if (architecture.endsWith("ForSequenceClassification")) {
			return ["AutoModelForSequenceClassification", "text-classification"];
		}
		else if (architecture.endsWith("ForMultipleChoice")) {
			return ["AutoModelForMultipleChoice", "multiple-choice"];
		}
		else if (architecture.endsWith("ForPreTraining")) {
			return ["AutoModelForPreTraining", "pretraining"];
		}
		else if (architecture.endsWith("ForMaskedLM")) {
			return ["AutoModelForMaskedLM", "masked-lm"];
		}
		else if (architecture.endsWith("ForCausalLM")) {
			return ["AutoModelForCausalLM", "causal-lm"];
		}
		else if (
			   architecture.endsWith("ForConditionalGeneration")
			|| architecture.endsWith("MTModel")
			|| architecture == "EncoderDecoderModel"
		) {
			return ["AutoModelForSeq2SeqLM", "seq2seq"];
		}
		else if (architecture.includes("LMHead")) {
			return ["AutoModelWithLMHead", "lm-head"];
		}
		else if (architecture.endsWith("Model")) {
			return ["AutoModel", undefined];
		}
		else {
			return [architecture, undefined];
		}
	}
	/**
	 * All tags
	 */
	get tags(): string[] {
		const x = [
			...this.mlFrameworks,
		];
		if (this.config.model_type) {
			x.push(this.config.model_type);
		}
		const arch = this.autoArchType[1];
		if (arch) {
			x.push(arch);
		}
		if (arch === "lm-head" && this.config.model_type) {
			if ([
				"t5",
				"bart",
				"marian",
			].includes(this.config.model_type)) {
				x.push("seq2seq");
			}
			else if ([
				"gpt2",
				"ctrl",
				"openai-gpt",
				"xlnet",
				"transfo-xl",
				"reformer",
			].includes(this.config.model_type)) {
				x.push("causal-lm");
			}
			else {
				x.push("masked-lm");
			}
		}
		x.push(
			...this.languages() ?? []
		);
		x.push(
			...this.datasets().map(k => `dataset:${k}`)
		);
		for (let [k, v] of Object.entries(this.cardData ?? {})) {
			if (!['tags', 'license'].includes(k)) {
				/// ^^ whitelist of other accepted keys
				continue;
			}
			if (typeof v === 'string') {
				v = [ v ];
			} else if (Utils.isStrArray(v)) {
				/// ok
			} else {
				c.error(`Invalid ${k} tag type`, v);
				c.debug(this.modelId);
				continue;
			}
			if (k === 'license') {
				x.push(...v.map(x => `license:${x.toLowerCase()}`));
			} else {
				x.push(...v);
			}
		}
		if (this.config.task_specific_params) {
			const keys = Object.keys(this.config.task_specific_params);
			for (const key of keys) {
				x.push(`pipeline:${key}`);
			}
		}
		const explicit_ptag = this.cardData?.pipeline_tag;
		if (explicit_ptag) {
			if (typeof explicit_ptag === 'string') {
				x.push(`pipeline_tag:${explicit_ptag}`);
			} else {
				x.push(`pipeline_tag:invalid`);
			}
		}
		return [...new Set(x)];
	}
	
	get pipeline_tag(): (keyof typeof PipelineType) | undefined {
		if (isBlacklisted(this.modelId) || this.cardData?.inference === false) {
			return undefined;
		}
		
		const explicit_ptag = this.cardData?.pipeline_tag;
		if (explicit_ptag) {
			if (typeof explicit_ptag == 'string') {
				return explicit_ptag as keyof typeof PipelineType;
			} else {
				c.error(`Invalid explicit pipeline_tag`, explicit_ptag);
				return undefined;
			}
		}
		
		const tags = this.tags;
		/// Special case for translation
		/// Get the first of the explicit tags that matches.
		const EXPLICIT_PREFIX = "pipeline:";
		const explicit_tag = tags.find(x => x.startsWith(EXPLICIT_PREFIX + `translation`));
		if (!!explicit_tag) {
			return "translation";
		}
		/// Otherwise, get the first (most specific) match **from the mapping**.
		for (const ptag of ALL_PIPELINE_TYPES) {
			if (tags.includes(ptag)) {
				return ptag;
			}
		}
		/// Extra mapping
		const mapping = new Map<string, keyof typeof PipelineType>([
			["seq2seq", "text-generation"],
			["causal-lm", "text-generation"],
			["masked-lm", "fill-mask"],
		]);
		for (const [tag, ptag] of mapping) {
			if (tags.includes(tag)) {
				return ptag;
			}
		}
	}
}