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# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

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{
  "New Developments": {
 "keywords": {
   "indicted party boss": {
  "description": "Current trending topic",
  "weight": 3,
  "timestamp": "2025-01-29T00:05:14.7211272+00:00"
   },
   "urgent health care": {
  "description": "Current trending topic",

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############# Install PSOpenAI and set your API key #############
Install-Module PSOpenAI
Import-Module PSOpenAI # only when it's first installed
$env:OPENAI_API_KEY = 'sk-xxxxxxxxxxxxxxxxxxxxxx'

############# Here's some markdown that'd be extracted from a PDF, however you want #############
$markdown = "# Dog Shot Record
2309 Kelley Road, Gulfport, MS, 39501
**CLINIC NAME**
Phone: 123-1234567 - Email: info@petshopname.com

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{
    "name": "pet",
    "strict": true,
    "schema": {
        "type": "object",
        "properties": {
            "pet_name": {
                "type": "string",
                "description": "What is the name of the pet?"
            },

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/*! Asciidoctor default stylesheet | MIT License | https://asciidoctor.org */
/* Uncomment the following line when using as a custom stylesheet */

@import "https://fonts.googleapis.com/css?family=Open+Sans:300,300italic,400,400italic,600,600italic%7CNoto+Serif:400,400italic,700,700italic%7CDroid+Sans+Mono:400,700";

html {
    font-family: sans-serif;
    -webkit-text-size-adjust: 100%
}

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To use the DCLM-7B model on Jan.ai, follow these steps:

1. Install the Required Tools: Ensure you have the necessary tools installed. You might need packages like transformers and open_lm. You can install them using:

   pip install git+https://github.com/mlfoundations/open_lm.git transformers

2. Download the Model: Integrate the DCLM-7B model from Hugging Face with Jan.ai:

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Loading and unloading models is a common practice in both machine learning (ML) and artificial intelligence (AI) applications, especially when dealing with large-scale or resource-intensive models.

In the context of this VS Code extension, the term "AI models" is used, but it encompasses both machine learning and artificial intelligence models. Machine learning is a subset of AI that focuses on enabling computers to learn and improve from experience without being explicitly programmed. AI, on the other hand, is a broader field that includes machine learning and other techniques to create intelligent systems.

The need to load and unload models arises due to several reasons:

1. Resource Management: AI and ML models can be computationally expensive and consume significant amounts of memory. When multiple models are available, loading all of them simultaneously may exceed the available system resources. By selectively loading and unloading models, the extension can manage resource usage efficiently and ensure

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[
    {
        "type": "function",
        "function": {
            "name": "Invoke-DbaQuery",
            "description": "Use this function to answer user questions about Northwind. Input should be a fully formed SQL query.",
            "parameters": {
                "properties": {
                    "Query": {
                        "type": "string",

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import {
  AppBskyEmbedImages,
  AppBskyFeedPost,
  BskyAgent,
} from '@atproto/api'
import * as dotenv from 'dotenv'
import {ComAtprotoSyncSubscribeRepos, subscribeRepos, SubscribeReposMessage} from 'atproto-firehose'
import Anthropic from '@anthropic-ai/sdk'
import axios from 'axios'