[Components] lamini - new components

components/lamini/actions/create-fine-tune-job/create-fine-tune-job.mjs

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+import app from "../../lamini.app.mjs";
+import constants from "../../common/constants.mjs";
+import utils from "../../common/utils.mjs";
+
+export default {
+  key: "lamini-create-fine-tune-job",
+  name: "Create Fine-Tune Job",
+  description: "Create a fine-tuning job with a dataset. [See the documentation](https://docs.lamini.ai/api/).",
+  version: "0.0.1",
+  type: "action",
+  props: {
+    app,
+    modelName: {
+      description: "Base model to be fine-tuned.",
+      propDefinition: [
+        app,
+        "modelName",
+        () => ({
+          includeFineTunedModels: false,
+        }),
+      ],
+    },
+    datasetId: {
+      type: "string",
+      label: "Dataset ID",
+      description: "Previously uploaded dataset to use for training. Please use the **Upload Dataset** action to upload a dataset.",
+    },
+    fineTuneArgs: {
+      type: "object",
+      label: "Finetune Arguments",
+      description: "Optional hyperparameters for fine-tuning. Each property is optional:\n- `index_pq_m`: Number of subquantizers for PQ (eg. 8)\n- `index_max_size`: Maximum index size (eg. 65536)\n- `max_steps`: Maximum number of training steps (eg. 60)\n- `batch_size`: Training batch size (eg. 1)\n- `learning_rate`: Learning rate (eg. 0.0003)\n- `index_pq_nbits`: Number of bits per subquantizer (eg. 8)\n- `max_length`: Maximum sequence length (eg. 2048)\n- `index_ivf_nlist`: Number of IVF lists (eg. 2048)\n- `save_steps`: Steps between checkpoints (eg. 60)\n- `args_name`: Name for the argument set (eg. \"demo\")\n- `r_value`: R value for LoRA (eg. 32)\n- `index_hnsw_m`: Number of neighbors in HNSW (eg. 32)\n- `index_method`: Indexing method (eg. \"IndexIVFPQ\")\n- `optim`: Optimizer to use (eg. \"adafactor\")\n- `index_hnsw_efConstruction`: HNSW construction parameter (eg. 16)\n- `index_hnsw_efSearch`: HNSW search parameter (eg. 8)\n- `index_k`: Number of nearest neighbors (eg. 2)\n- `index_ivf_nprobe`: Number of IVF probes (eg. 48)\n- `eval_steps`: Steps between evaluations (eg. 30)\n[See the documentation](https://docs.lamini.ai/tuning/hyperparameters/#finetune_args).",
+      optional: true,
+    },
+    gpuConfig: {
+      type: "object",
+      label: "GPU Config",
+      description: "Optional GPU configuration for fine-tuning. [See the documentation](https://docs.lamini.ai/tuning/hyperparameters/#gpu_config).",
+      optional: true,
+    },
+    isPublic: {
+      type: "boolean",
+      label: "Is Public",
+      description: "Whether this fine-tuning job and dataset should be publicly accessible.",
+      optional: true,
+    },
+    customModelName: {
+      type: "string",
+      label: "Custom Model Name",
+      description: "A human-readable name for the fine-tuned model.",
+      optional: true,
+    },
+  },
+  methods: {
+    createFineTuneJob(args = {}) {
+      return this.app.post({
+        versionPath: constants.VERSION_PATH.V1,
+        path: "/train",
+        ...args,
+      });
+    },
+  },
+  async run({ $ }) {
+    const {
+      app,
+      createFineTuneJob,
+      modelName,
+      datasetId,
+      fineTuneArgs,
+      gpuConfig,
+      isPublic,
+      customModelName,
+    } = this;
+
+    const { upload_base_path: uploadBasePath } =
+      await app.getUploadBasePath({
+        $,
+      });
+
+    await app.getExistingDataset({
+      $,
+      data: {
+        dataset_id: datasetId,
+        upload_base_path: uploadBasePath,
+      },
+    });
+
+    const response = await createFineTuneJob({
+      $,
+      data: {
+        model_name: modelName,
+        dataset_id: datasetId,
+        upload_file_path: `${uploadBasePath}/${datasetId}.jsonlines`,
+        finetune_args: utils.parseJson(fineTuneArgs),
+        gpu_config: utils.parseJson(gpuConfig),
+        is_public: isPublic,
+        custom_model_name: customModelName,
+      },
+    });
+
+    $.export("$summary", `Successfully created a fine-tune job with ID \`${response.job_id}\`.`);
+    return response;
+  },
+};

components/lamini/actions/evaluate-job/evaluate-job.mjs

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+import app from "../../lamini.app.mjs";
+
+export default {
+  key: "lamini-evaluate-job",
+  name: "Evaluate Job",
+  description: "Evaluate a fine-tuning job by job ID. [See the documentation](https://docs.lamini.ai/api/).",
+  version: "0.0.1",
+  type: "action",
+  props: {
+    app,
+    jobId: {
+      propDefinition: [
+        app,
+        "jobId",
+      ],
+      description: "The ID of the fine-tuning job to evaluate.",
+    },
+  },
+  methods: {
+    evaluateJob({
+      jobId, ...args
+    } = {}) {
+      return this.app.makeRequest({
+        path: `/finetune_eval/jobs/${jobId}`,
+        ...args,
+      });
+    },
+  },
+  async run({ $ }) {
+    const {
+      evaluateJob,
+      jobId,
+    } = this;
+
+    const response = await evaluateJob({
+      $,
+      jobId,
+    });
+
+    $.export("$summary", `Successfully evaluated job with ID \`${jobId}\`.`);
+    return response;
+  },
+};

components/lamini/actions/generate-completion/generate-completion.mjs

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+import app from "../../lamini.app.mjs";
+import utils from "../../common/utils.mjs";
+
+export default {
+  key: "lamini-generate-completion",
+  name: "Generate Completion",
+  description: "Generate completions using a Lamini model. [See the documentation](https://docs.lamini.ai/api/).",
+  version: "0.0.1",
+  type: "action",
+  props: {
+    app,
+    modelName: {
+      propDefinition: [
+        app,
+        "modelName",
+      ],
+      description: "The model to use for completion.",
+    },
+    prompt: {
+      type: "string",
+      label: "Prompt",
+      description: "The prompt to send to the model.",
+    },
+    outputType: {
+      propDefinition: [
+        app,
+        "outputType",
+      ],
+    },
+    maxTokens: {
+      propDefinition: [
+        app,
+        "maxTokens",
+      ],
+    },
+    maxNewTokens: {
+      propDefinition: [
+        app,
+        "maxNewTokens",
+      ],
+    },
+  },
+  methods: {
+    generateCompletion(args = {}) {
+      return this.app.post({
+        path: "/completions",
+        ...args,
+      });
+    },
+  },
+  async run({ $ }) {
+    const {
+      generateCompletion,
+      modelName,
+      prompt,
+      outputType,
+      maxTokens,
+      maxNewTokens,
+    } = this;
+
+    const response = await generateCompletion({
+      $,
+      data: {
+        model_name: modelName,
+        prompt,
+        output_type: utils.parseJson(outputType),
+        max_tokens: maxTokens,
+        max_new_tokens: maxNewTokens,
+      },
+    });
+
+    $.export("$summary", `Successfully generated completion for prompt with model ${modelName}.`);
+    return response;
+  },
+};

components/lamini/actions/get-batch-completions/get-batch-completions.mjs

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+import app from "../../lamini.app.mjs";
+import utils from "../../common/utils.mjs";
+
+export default {
+  key: "lamini-get-batch-completions",
+  name: "Get Batch Completions",
+  description: "Retrieve the results of a batch completion request from Lamini. [See the documentation](https://docs.lamini.ai/api/).",
+  version: "0.0.1",
+  type: "action",
+  props: {
+    app,
+    modelName: {
+      propDefinition: [
+        app,
+        "modelName",
+      ],
+    },
+    prompt: {
+      type: "string[]",
+      label: "Prompts",
+      description: "The prompts to use for completion.",
+    },
+    outputType: {
+      propDefinition: [
+        app,
+        "outputType",
+      ],
+    },
+    maxTokens: {
+      propDefinition: [
+        app,
+        "maxTokens",
+      ],
+    },
+    maxNewTokens: {
+      propDefinition: [
+        app,
+        "maxNewTokens",
+      ],
+    },
+  },
+  methods: {
+    submitBatchCompletions(args = {}) {
+      return this.app.post({
+        path: "/batch_completions",
+        ...args,
+      });
+    },
+    getBatchCompletions({
+      id, ...args
+    } = {}) {
+      return this.app.makeRequest({
+        path: `/batch_completions/${id}/result`,
+        ...args,
+      });
+    },
+  },
+  async run({ $ }) {
+    const {
+      submitBatchCompletions,
+      getBatchCompletions,
+      modelName,
+      prompt,
+      outputType,
+      maxTokens,
+      maxNewTokens,
+    } = this;
+
+    const { id } = await submitBatchCompletions({
+      $,
+      data: {
+        model_name: modelName,
+        prompt,
+        output_type: utils.parseJson(outputType),
+        max_tokens: maxTokens,
+        max_new_tokens: maxNewTokens,
+      },
+    });
+
+    const response = await getBatchCompletions({
+      $,
+      id,
+    });
+
+    $.export("$summary", `Successfully submitted batch completion with ID \`${id}\`.`);
+    return response;
+  },
+};