Add dat1.co as an inference provider

packages/inference/README.md

@@ -63,6 +63,7 @@ Currently, we support the following providers:
 - [Cohere](https://cohere.com)
 - [Cerebras](https://cerebras.ai/)
 - [Groq](https://groq.com)
+- [Dat1](https://dat1.co)
 
 To send requests to a third-party provider, you have to pass the `provider` parameter to the inference function. The default value of the `provider` parameter is "auto", which will select the first of the providers available for the model, sorted by your preferred order in https://hf.co/settings/inference-providers.
 
@@ -95,6 +96,7 @@ Only a subset of models are supported when requesting third-party providers. You
 - [Together supported models](https://huggingface.co/api/partners/together/models)
 - [Cohere supported models](https://huggingface.co/api/partners/cohere/models)
 - [Cerebras supported models](https://huggingface.co/api/partners/cerebras/models)
+- [Dat1 supported models](https://huggingface.co/api/partners/dat1/models)
 - [Groq supported models](https://console.groq.com/docs/models)
 
 ❗**Important note:** To be compatible, the third-party API must adhere to the "standard" shape API we expect on HF model pages for each pipeline task type.

packages/inference/src/lib/getProviderHelper.ts

@@ -47,6 +47,7 @@ import type {
 import * as Replicate from "../providers/replicate";
 import * as Sambanova from "../providers/sambanova";
 import * as Together from "../providers/together";
+import * as Dat1 from "../providers/dat1";
 import type { InferenceProvider, InferenceProviderOrPolicy, InferenceTask } from "../types";
 
 export const PROVIDERS: Record<InferenceProvider, Partial<Record<InferenceTask, TaskProviderHelper>>> = {
@@ -59,6 +60,10 @@ export const PROVIDERS: Record<InferenceProvider, Partial<Record<InferenceTask,
 	cohere: {
 		conversational: new Cohere.CohereConversationalTask(),
 	},
+	dat1: {
+		"text-to-image": new Dat1.Dat1TextToImageTask(),
+		conversational: new Dat1.Dat1ConversationalTask(),
+	},
 	"fal-ai": {
 		"text-to-image": new FalAI.FalAITextToImageTask(),
 		"text-to-speech": new FalAI.FalAITextToSpeechTask(),

packages/inference/src/providers/consts.ts

@@ -22,6 +22,7 @@ export const HARDCODED_MODEL_INFERENCE_MAPPING: Record<
 	"black-forest-labs": {},
 	cerebras: {},
 	cohere: {},
+	dat1: {},
 	"fal-ai": {},
 	"featherless-ai": {},
 	"fireworks-ai": {},

packages/inference/src/providers/dat1.ts

@@ -0,0 +1,81 @@
+/**
+ * See the registered mapping of HF model ID => Together model ID here:
+ *
+ * https://huggingface.co/api/partners/together/models
+ *
+ * This is a publicly available mapping.
+ *
+ * If you want to try to run inference for a new model locally before it's registered on huggingface.co,
+ * you can add it to the dictionary "HARDCODED_MODEL_ID_MAPPING" in consts.ts, for dev purposes.
+ *
+ * - If you work at Together and want to update this mapping, please use the model mapping API we provide on huggingface.co
+ * - If you're a community member and want to add a new supported HF model to Together, please open an issue on the present repo
+ * and we will tag Together team members.
+ *
+ * Thanks!
+ */
+import { InferenceOutputError } from "../lib/InferenceOutputError";
+import type { BodyParams } from "../types";
+import { omit } from "../utils/omit";
+import {
+	BaseConversationalTask,
+	TaskProviderHelper,
+	type TextToImageTaskHelper,
+} from "./providerHelper";
+
+const DAT1_API_BASE_URL = "https://api.dat1.co/api/v1/hf";
+
+interface Dat1Base64ImageGeneration {
+	data: Array<{
+		b64_json: string;
+	}>;
+}
+
+export class Dat1ConversationalTask extends BaseConversationalTask {
+	constructor() {
+		super("dat1", DAT1_API_BASE_URL);
+	}
+
+	override makeRoute(): string {
+		return "/chat/completions";
+	}
+}
+
+export class Dat1TextToImageTask extends TaskProviderHelper implements TextToImageTaskHelper {
+	constructor() {
+		super("dat1", DAT1_API_BASE_URL);
+	}
+
+	override makeRoute(): string {
+		return "/images/generations";
+	}
+
+	preparePayload(params: BodyParams): Record<string, unknown> {
+		return {
+			...omit(params.args, ["inputs", "parameters"]),
+			...(params.args.parameters as Record<string, unknown>),
+			prompt: params.args.inputs,
+			response_format: "base64",
+			model: params.model,
+		};
+	}
+
+	async getResponse(response: Dat1Base64ImageGeneration, outputType?: "url" | "blob"): Promise<string | Blob> {
+		if (
+			typeof response === "object" &&
+			"data" in response &&
+			Array.isArray(response.data) &&
+			response.data.length > 0 &&
+			"b64_json" in response.data[0] &&
+			typeof response.data[0].b64_json === "string"
+		) {
+			const base64Data = response.data[0].b64_json;
+			if (outputType === "url") {
+				return `data:image/jpeg;base64,${base64Data}`;
+			}
+			return fetch(`data:image/jpeg;base64,${base64Data}`).then((res) => res.blob());
+		}
+
+		throw new InferenceOutputError("Expected Dat1 text-to-image response format");
+	}
+}

packages/inference/src/types.ts

@@ -41,6 +41,7 @@ export const INFERENCE_PROVIDERS = [
 	"black-forest-labs",
 	"cerebras",
 	"cohere",
+	"dat1",
 	"fal-ai",
 	"featherless-ai",
 	"fireworks-ai",

packages/inference/test/InferenceClient.spec.ts

@@ -989,6 +989,65 @@ describe.skip("InferenceClient", () => {
 		TIMEOUT
 	);
 
+	describe.concurrent(
+		"dat1",
+		() => {
+			const client = new InferenceClient(env.DAT1_KEY ?? "dummy");
+
+			HARDCODED_MODEL_INFERENCE_MAPPING["dat1"] = {
+				"unsloth/Llama-3.2-3B-Instruct-GGUF": {
+					hfModelId: "unsloth/Llama-3.2-3B-Instruct-GGUF",
+					providerId: "unsloth-Llama-32-3B-Instruct-GGUF",
+					status: "live",
+					task: "conversational",
+				},
+				"Kwai-Kolors/Kolors": {
+					hfModelId: "Kwai-Kolors/Kolors",
+					providerId: "Kwai-Kolors-Kolors",
+					status: "live",
+					task: "text-to-image",
+				},
+			};
+
+			it("chatCompletion", async () => {
+				const res = await client.chatCompletion({
+					model: "unsloth/Llama-3.2-3B-Instruct-GGUF",
+					provider: "dat1",
+					messages: [{ role: "user", content: "Complete this sentence with words, one plus one is equal " }],
+				});
+				if (res.choices && res.choices.length > 0) {
+					const completion = res.choices[0].message?.content;
+					expect(completion).toContain("two");
+				}
+			});
+
+			it("chatCompletion stream", async () => {
+				const stream = client.chatCompletionStream({
+					model: "unsloth/Llama-3.2-3B-Instruct-GGUF",
+					provider: "dat1",
+					messages: [{ role: "user", content: "Complete the equation 1 + 1 = , just the answer" }],
+				}) as AsyncGenerator<ChatCompletionStreamOutput>;
+				let out = "";
+				for await (const chunk of stream) {
+					if (chunk.choices && chunk.choices.length > 0) {
+						out += chunk.choices[0].delta.content;
+					}
+				}
+				expect(out).toContain("2");
+			});
+
+			it("textToImage", async () => {
+				const res = await client.textToImage({
+					model: "Kwai-Kolors/Kolors",
+					provider: "dat1",
+					inputs: "award winning high resolution photo of a giant tortoise",
+				});
+				expect(res).toBeInstanceOf(Blob);
+			});
+		},
+		TIMEOUT
+	)
+
 	/**
 	 * Compatibility with third-party Inference Providers
 	 */