♻️ Forgot the common-definitions.json :)

Author: SBrandeis

packages/tasks/src/tasks/common-definitions.json

@@ -32,7 +32,10 @@
 					"description": "The corresponding probability."
 				}
 			},
-			"required": ["label", "score"]
+			"required": [
+				"label",
+				"score"
+			]
 		},
 		"GenerationParameters": {
 			"title": "GenerationParameters",
@@ -43,67 +46,75 @@
 					"type": "number",
 					"description": "The value used to modulate the next token probabilities."
 				},
-				"topK": {
+				"top_k": {
 					"type": "integer",
 					"description": "The number of highest probability vocabulary tokens to keep for top-k-filtering."
 				},
-				"topP": {
+				"top_p": {
 					"type": "number",
 					"description": "If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation."
 				},
-				"typicalP": {
+				"typical_p": {
 					"type": "number",
 					"description": " Local typicality measures how similar the conditional probability of predicting a target token next is to the expected conditional probability of predicting a random token next, given the partial text already generated. If set to float < 1, the smallest set of the most locally typical tokens with probabilities that add up to typical_p or higher are kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details."
 				},
-				"epsilonCutoff": {
+				"epsilon_cutoff": {
 					"type": "number",
 					"description": "If set to float strictly between 0 and 1, only tokens with a conditional probability greater than epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191) for more details."
 				},
-				"etaCutoff": {
+				"eta_cutoff": {
 					"type": "number",
 					"description": "Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model. See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191) for more details."
 				},
-				"maxLength": {
+				"max_length": {
 					"type": "integer",
 					"description": "The maximum length (in tokens) of the generated text, including the input."
 				},
-				"maxNewTokens": {
+				"max_new_tokens": {
 					"type": "integer",
 					"description": "The maximum number of tokens to generate. Takes precedence over maxLength."
 				},
-				"minLength": {
+				"min_length": {
 					"type": "integer",
 					"description": "The minimum length (in tokens) of the generated text, including the input."
 				},
-				"minNewTokens": {
+				"min_new_tokens": {
 					"type": "integer",
 					"description": "The minimum number of tokens to generate. Takes precedence over maxLength."
 				},
-				"doSample": {
+				"do_sample": {
 					"type": "boolean",
 					"description": "Whether to use sampling instead of greedy decoding when generating new tokens."
 				},
-				"earlyStopping": {
+				"early_stopping": {
 					"description": "Controls the stopping condition for beam-based methods.",
-					"oneOf": [{ "type": "boolean" }, { "const": "never", "type": "string" }]
-				},
-				"numBeams": {
+					"oneOf": [
+						{
+							"type": "boolean"
+						},
+						{
+							"const": "never",
+							"type": "string"
+						}
+					]
+				},
+				"num_beams": {
 					"type": "integer",
 					"description": "Number of beams to use for beam search."
 				},
-				"numBeamGroups": {
+				"num_beam_groups": {
 					"type": "integer",
 					"description": "Number of groups to divide num_beams into in order to ensure diversity among different groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details."
 				},
-				"penaltyAlpha": {
+				"penalty_alpha": {
 					"type": "number",
 					"description": "The value balances the model confidence and the degeneration penalty in contrastive search decoding."
 				},
-				"useCache": {
+				"use_cache": {
 					"type": "boolean",
 					"description": "Whether the model should use the past last key/values attentions to speed up decoding"
 				}
 			}
 		}
 	}
-}
+}