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Does a new docs run and adds pricing info. Renames Features to Info
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67 files changed

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‎src/components/models/ModelFeatures.tsx‎

Lines changed: 17 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -2,6 +2,11 @@ import type { WorkersAIModelsSchema } from "~/schemas";
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const ModelFeatures = ({ model }: { model: WorkersAIModelsSchema }) => {
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const nf = new Intl.NumberFormat("en-US");
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const currencyFormatter = new Intl.NumberFormat("en-US", {
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style: "currency",
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currency: "USD",
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maximumFractionDigits: 10,
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});
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const properties: any = {};
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model.properties.forEach((property: any) => {
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properties[property.property_id] = property.value;
@@ -15,7 +20,7 @@ const ModelFeatures = ({ model }: { model: WorkersAIModelsSchema }) => {
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<thead>
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<tr>
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<>
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<th>Features</th>
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<th>Model Info</th>
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<th />
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</>
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</tr>
@@ -105,6 +110,17 @@ const ModelFeatures = ({ model }: { model: WorkersAIModelsSchema }) => {
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<td>Yes</td>
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</tr>
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)}
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{properties.price &&
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properties.price.map(
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(price: { price: number; unit: string }) => (
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<tr key={price.price}>
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<td>Pricing</td>
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<td>
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{currencyFormatter.format(price.price)} {price.unit}
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</td>
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</tr>
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),
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)}
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</tbody>
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</table>
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</>

‎src/content/workers-ai-models/bart-large-cnn.json‎

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@@ -8,6 +8,7 @@
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"name": "Summarization",
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"description": "Summarization is the task of producing a shorter version of a document while preserving its important information. Some models can extract text from the original input, while other models can generate entirely new text."
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},
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"created_at": "2024-02-27 18:28:11.833",
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"tags": [],
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"properties": [
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{

‎src/content/workers-ai-models/bge-base-en-v1.5.json‎

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@@ -8,6 +8,7 @@
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"name": "Text Embeddings",
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"description": "Feature extraction models transform raw data into numerical features that can be processed while preserving the information in the original dataset. These models are ideal as part of building vector search applications or Retrieval Augmented Generation workflows with Large Language Models (LLM)."
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},
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"created_at": "2023-09-25 19:21:11.898",
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"tags": [],
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"properties": [
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{

‎src/content/workers-ai-models/bge-large-en-v1.5.json‎

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@@ -8,6 +8,7 @@
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"name": "Text Embeddings",
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"description": "Feature extraction models transform raw data into numerical features that can be processed while preserving the information in the original dataset. These models are ideal as part of building vector search applications or Retrieval Augmented Generation workflows with Large Language Models (LLM)."
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},
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"created_at": "2023-11-07 15:43:58.042",
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"tags": [],
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"properties": [
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{

‎src/content/workers-ai-models/bge-m3.json‎

Lines changed: 120 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -8,42 +8,96 @@
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"name": "Text Embeddings",
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"description": "Feature extraction models transform raw data into numerical features that can be processed while preserving the information in the original dataset. These models are ideal as part of building vector search applications or Retrieval Augmented Generation workflows with Large Language Models (LLM)."
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},
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"created_at": "2024-05-22 19:27:09.781",
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"tags": [],
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"properties": [],
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"properties": [
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{
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"property_id": "price",
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"value": [
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{
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"unit": "per M input tokens",
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"price": 0.012,
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"currency": "USD"
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}
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]
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}
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],
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"schema": {
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"input": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"minLength": 1,
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"description": "A query you wish to perform against the provided contexts. If no query is provided the model with respond with embeddings for contexts"
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"oneOf": [
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{
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"title": "BGE M3 Input Query and Contexts",
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"properties": {
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"query": {
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"type": "string",
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"minLength": 1,
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"description": "A query you wish to perform against the provided contexts. If no query is provided the model with respond with embeddings for contexts"
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},
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"contexts": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"text": {
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"type": "string",
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"minLength": 1,
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"description": "One of the provided context content"
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}
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}
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},
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"description": "List of provided contexts. Note that the index in this array is important, as the response will refer to it."
50+
},
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"truncate_inputs": {
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"type": "boolean",
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"default": false,
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"description": "When provided with too long context should the model error out or truncate the context to fit?"
55+
}
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},
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"required": [
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"contexts"
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]
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},
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"contexts": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"text": {
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"type": "string",
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"minLength": 1,
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"description": "One of the provided context content"
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}
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{
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"title": "BGE M3 Input Embedding",
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"properties": {
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"text": {
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"oneOf": [
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{
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"type": "string",
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"description": "The text to embed",
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"minLength": 1
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},
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{
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"type": "array",
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"description": "Batch of text values to embed",
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"items": {
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"type": "string",
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"description": "The text to embed",
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"minLength": 1
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},
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"maxItems": 100
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}
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]
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},
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"truncate_inputs": {
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"type": "boolean",
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"default": false,
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"description": "When provided with too long context should the model error out or truncate the context to fit?"
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}
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},
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"description": "List of provided contexts. Note that the index in this array is important, as the response will refer to it."
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"required": [
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"text"
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]
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}
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},
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"required": [
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"contexts"
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]
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},
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"output": {
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"type": "object",
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"contentType": "application/json",
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"oneOf": [
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{
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"title": "Query",
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"title": "BGE M3 Ouput Query",
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"properties": {
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"response": {
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"type": "array",
@@ -64,7 +118,7 @@
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}
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},
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{
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"title": "Embedding",
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"title": "BGE M3 Output Embedding for Contexts",
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"properties": {
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"response": {
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"type": "array",
@@ -74,6 +128,50 @@
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"type": "number"
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}
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}
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},
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"shape": {
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"type": "array",
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"items": {
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"type": "number"
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}
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},
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"pooling": {
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"type": "string",
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"enum": [
141+
"mean",
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"cls"
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],
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"description": "The pooling method used in the embedding process."
145+
}
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}
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},
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{
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"title": "BGE M3 Ouput Embedding",
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"properties": {
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"shape": {
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"type": "array",
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"items": {
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"type": "number"
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}
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},
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"data": {
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"type": "array",
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"description": "Embeddings of the requested text values",
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"items": {
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"type": "array",
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"description": "Floating point embedding representation shaped by the embedding model",
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"items": {
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"type": "number"
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}
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}
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},
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"pooling": {
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"type": "string",
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"enum": [
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"mean",
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"cls"
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],
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"description": "The pooling method used in the embedding process."
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}
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}
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}

‎src/content/workers-ai-models/bge-reranker-base.json‎

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@@ -8,6 +8,7 @@
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"name": "Text Classification",
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"description": "Sentiment analysis or text classification is a common NLP task that classifies a text input into labels or classes."
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},
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"created_at": "2025-02-14 12:28:19.009",
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"tags": [],
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"properties": [],
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"schema": {
@@ -21,7 +22,6 @@
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},
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"top_k": {
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"type": "integer",
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"default": null,
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"minimum": 1,
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"description": "Number of returned results starting with the best score."
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},

‎src/content/workers-ai-models/bge-small-en-v1.5.json‎

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@@ -8,6 +8,7 @@
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"name": "Text Embeddings",
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"description": "Feature extraction models transform raw data into numerical features that can be processed while preserving the information in the original dataset. These models are ideal as part of building vector search applications or Retrieval Augmented Generation workflows with Large Language Models (LLM)."
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},
11+
"created_at": "2023-11-07 15:43:58.042",
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"tags": [],
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"properties": [
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{

‎src/content/workers-ai-models/deepseek-coder-6.7b-base-awq.json‎

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"name": "Text Generation",
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"description": "Family of generative text models, such as large language models (LLM), that can be adapted for a variety of natural language tasks."
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},
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"created_at": "2024-02-06 18:16:27.183",
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"tags": [],
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"properties": [
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{
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"oneOf": [
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{
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"type": "object",
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"contentType": "application/json",
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"properties": {
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"response": {
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"type": "string",
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},
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{
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"type": "string",
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"contentType": "text/event-stream",
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"format": "binary"
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}
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]

‎src/content/workers-ai-models/deepseek-coder-6.7b-instruct-awq.json‎

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"name": "Text Generation",
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"description": "Family of generative text models, such as large language models (LLM), that can be adapted for a variety of natural language tasks."
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},
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"created_at": "2024-02-06 18:18:27.462",
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"tags": [],
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"properties": [
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{
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"oneOf": [
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{
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"type": "object",
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"contentType": "application/json",
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"properties": {
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"response": {
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"type": "string",
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},
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{
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"type": "string",
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"contentType": "text/event-stream",
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"format": "binary"
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}
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]

‎src/content/workers-ai-models/deepseek-math-7b-instruct.json‎

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"name": "Text Generation",
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"description": "Family of generative text models, such as large language models (LLM), that can be adapted for a variety of natural language tasks."
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},
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"created_at": "2024-02-27 17:54:17.459",
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"tags": [],
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"properties": [
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{
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"oneOf": [
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{
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"type": "object",
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"contentType": "application/json",
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"properties": {
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"response": {
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"type": "string",
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},
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{
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"type": "string",
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"contentType": "text/event-stream",
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"format": "binary"
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}
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]

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