Vaibhavs10

You are Grok 2, a curious AI built by xAI. You are intended to answer almost any question, often taking an outside perspective on humanity, and you always strive towards maximum helpfulness!

Remember that you have these general abilities, and many others as well which are not listed here:

You can analyze individual X posts and their links.
You can answer questions about user profiles on X.
You can analyze content uploaded by user including images and pdfs.
You have realtime access to the web and posts on X.
Remember these are some of the abilities that you do NOT have:

Vaibhavs10

import torch
from diffusers.utils import export_to_video
from diffusers import LTXPipeline, LTXVideoTransformer3DModel, GGUFQuantizationConfig

ckpt_path = (
    "https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf"
)
transformer = LTXVideoTransformer3DModel.from_single_file(
    ckpt_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),

Vaibhavs10

from flask import Flask, redirect, request, Response
import requests

app = Flask(__name__)

@app.route('/v2/<namespace>/<name>/blobs/<sha>', methods=['GET', "HEAD"])
def blobs(namespace, name, sha):
    oid = sha.split(':')[1]
    r = requests.get(f'https://huggingface.co/api/models/{namespace}/{name}/tree/main')
    result = r.json()

Vaibhavs10

from huggingface_hub import HfApi
from huggingface_hub import logging

logging.set_verbosity_info()

api = HfApi()

api.upload_folder(folder_path="<FOLDER NAME>", 
                  repo_id="<DATASET NAME>", 
                  repo_type="dataset", 

Vaibhavs10

import transformers

model_name = 'Intel/neural-chat-7b-v3-1'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)


def generate_response(system_input, user_input):
  
    # Format the input using the provided template

Vaibhavs10

import json
import time
import torch
from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    "openai/whisper-large-v3",
    torch_dtype=torch.float16,
    device="mps",

Vaibhavs10

import json

import argparse
import torch
from transformers import pipeline

parser = argparse.ArgumentParser(description="Automatic Speech Recognition")
parser.add_argument(
    "--file-name",
    required=True,

Vaibhavs10

!pip install transformers optimum
!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name_or_path = "TheBloke/zephyr-7B-beta-GPTQ"

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,

Vaibhavs10

# Load the dataset (locally)

from datasets import load_dataset

cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")

# Stream the dataset

from datasets import load_dataset

Vaibhavs10

#pip install git+https://github.com/huggingface/transformers.git

import datetime
import sys
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_microphone_live

pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=0)
sampling_rate = pipe.feature_extractor.sampling_rate