philschmid

Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

philschmid

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
    - Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p

philschmid

import os
import asyncio
import subprocess
import time
from typing import List, Dict
import torch
from openai import AsyncOpenAI
from tqdm.asyncio import tqdm
import logging

philschmid

from typing import Dict, List
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer


class ArmoRMPipeline:
    def __init__(self, model_id, device_map="auto", torch_dtype=torch.bfloat16, truncation=True, trust_remote_code=False, max_length=4096):
        self.model = AutoModelForSequenceClassification.from_pretrained(
            model_id,
            device_map=device_map,

philschmid

import requests as r
from huggingface_hub import HfFolder
from tqdm import tqdm
from datasets import Dataset 

headers = {"Authorization": f"Bearer {HfFolder.get_token()}"}

sess = r.Session()
sess.headers.update(headers)

philschmid

#!/bin/bash
start=$(date +%s)

# Initialize RESULT_DIRECTORY with default value and HF_MODEL_ID with an empty string
RESULT_DIRECTORY="nous"
HF_MODEL_ID=""
TRUST_REMOTE_CODE="False"
CURRENT_DIR=$(pwd)

# List of Benchmarking Tasks

philschmid

from openai import OpenAI

# initialize the client but point it to TGI
client = OpenAI(
  base_url="https://api-inference.huggingface.co/v1",
  api_key="hf_xxx" # Replace with your token
) 

chat_completion = client.chat.completions.create(
    model="google/gemma-7b-it",

philschmid

torchrun --nnodes 2 --nproc_per_node 32 --master_addr algo-1 --master_port 7777 --node_rank 0 train_llama.py \
 --model_id "meta-llama/Llama-2-70b-hf" \
 --lr 5e-5 \
 --per_device_train_batch_size 16 \
 --bf16 True \
 --epochs 3

philschmid

import re

cli_output = '''
Text Generation Launcher

Usage: text-generation-launcher [OPTIONS]

Options:
      --model-id <MODEL_ID>
          The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`. Or it can be a local directory containing the necessary files as saved by `save_pretrained(...)` methods of transformers

philschmid

Nuremberg (/ˈnjʊərəmbɜːrɡ/ NURE-əm-burg; German: Nürnberg [ˈnʏʁnbɛʁk] (listen); in the local East Franconian dialect: Nämberch [ˈnɛmbɛrç]) is the second-largest city of the German state of Bavaria after its capital Munich, and its 541.000 inhabitants[3] make it the 14th-largest city in Germany. On the Pegnitz River (from its confluence with the Rednitz in Fürth onwards: Regnitz, a tributary of the River Main) and the Rhine–Main–Danube Canal, it lies in the Bavarian administrative region of Middle Franconia, and is the largest city and the unofficial capital of Franconia. Nuremberg forms with the neighbouring cities of Fürth, Erlangen and Schwabach a continuous conurbation with a total population of 800,376 (2019), which is the heart of the urban area region with around 1.4 million inhabitants,[4] while the larger Nuremberg Metropolitan Region has approximately 3.6 million inhabitants. The city lies about 170 kilometres (110 mi) north of Munich. It is the largest city in the East Franconian dialect area (collo