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// curl -H "Authorization: Bearer ..." "https://$BASE_URL/models/features"
{
  "host_fingerprint": "provider", // should be a unique string for the host or router (e.g. deepseek, openrouter, litellm)
  "data": [
    {
      // Required
      "id": "provider/model",
      "name": "Provider: Model",
      // should either match host_fingerprint if this is a primary provider (ie deepseek) or match the host_fingerprint of
      // the upstream provider, if this is a router. i.e., this string should never be "litellm"

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import math
import os

from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

question = "Generate a random string of the letters A and B, no talk just go."
model1, client1 = "gpt-4o-mini", OpenAI()

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import argparse
import random
import sys

from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
import torch

parser = argparse.ArgumentParser()
parser.add_argument("question", type=str)
parser.add_argument(

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system: |
  Assistant is acting as a helpful reference. The user is asking the given question through a command line interface and needs a concise answer. The interface does not support markdown, so the response should be plaintext. The response should be as concise as possible while still answering the question. For example, if the question is about the appropriate command to perform an action, the response should be just the command and nothing else. If the question is about a Python feature, the response should be just a code snippet, perhaps with a single-line explanatory comment if necessary--but assume the user is an expert and will ask clarifying questions if needed.
  
  # Examples
  
  Human: how do i write a for-loop in bash?
  Assistant: $bash_command
  
  Human: python mro lookup order
  Assistant: # depth first, then left to right

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import json

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

from repeng import ControlVector, ControlModel, DatasetEntry

model_name = "mistralai/Mistral-7B-Instruct-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_name)

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[
  {
    "model": "34m",
    "index": 6426,
    "desc": "Grove startup; Gateway cloning technology",
    "usable": true
  },
  {
    "model": "34m",
    "index": 19443,

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Play this game by pasting the script in http://www.puzzlescript.net/editor.html

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Play this game by pasting the script in http://www.puzzlescript.net/editor.html

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Play this game by pasting the script in http://www.puzzlescript.net/editor.html

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import os

import tqdm
import openai

openai.api_key = os.environ["OPENAI_API_KEY"]


def get_response(usermsg):
    response = openai.ChatCompletion.create(