Yehor Smoliakov egorsmkv

For Large Language Models (LLMs) to learn function calling, the training data typically includes examples that demonstrate the pattern of calling a function. This can be represented in various formats, but a common approach is to use a structured format that resembles a dialogue or a sequence of instructions where a "user" or "assistant" invokes a function with specific arguments. The representation might vary depending on the specific LLM and its intended application, but here's a general idea of how it might look:

Example Format for Training Data

Text-based representation: In this format, function calls are represented as text that the model learns to predict or generate. For instance, if the task involves calling a function get_weather(city), the training data might include examples like:
```
User: What is the weather like in Paris?
Assistant: I need to call get_weather(city="Paris"). The weather in Paris is sunny.
```

The sources mention several links related to computer vision, graphics, and image processing within the Rust ecosystem. These include libraries, projects, and discussions:

Graphics & Rendering Libraries/Frameworks:

Piston: A graphics library. Its image library has been rewritten in pure Rust, and it also features skeletal animation demos and supports "duck typing".
glium: A safe wrapper for OpenGL, used in the simple polar dodging game, Rusty_Dodge. Its project updates and a post-mortem have been noted.
gfx / gfx-rs / gfx-hal: A crate designed to display content on a screen across various platforms. Updates from its development have been highlighted. It also has a hardware abstraction layer (gfx-hal) and an ecosystem overview.
luminance: A type-safe, type-level, and stateless Rust graphics framework.
wgpu: A cross-platform graphics and compute library based on WebGPU, with multi-threading capabilities.
Speedy2D: A crate offering cross-platform hardware-acc

WhisperForConditionalGeneration(
  (model): WhisperModel(
    (encoder): WhisperEncoder(
      (conv1): Conv1d(128, 1280, kernel_size=(3,), stride=(1,), padding=(1,))
      (conv2): Conv1d(1280, 1280, kernel_size=(3,), stride=(2,), padding=(1,))
      (embed_positions): Embedding(1500, 1280)
      (layers): ModuleList(
        (0-31): 32 x WhisperEncoderLayer(
          (self_attn): WhisperSdpaAttention(

	[swscaler @ 0x120008000] No accelerated colorspace conversion found from yuv420p to rgb24.
	best_video_stream_index: 0
	Width: 1920, height: 1080
	Duration: 494.30734451395824 seconds
	FPS: 30.001577377319336
	Successfully sought to frame near index 0.
	Frame time: 0
	Successfully sought to frame near index 30.
	Frame time: 8.332895221071446
	Successfully sought to frame near index 60.

	import evaluate
	import polars as pl
	import time
	import torch
	from tqdm import tqdm
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from datasets import load_dataset

	# --- 1. SETUP ---
	model_id = '/home/smlkw/en-uk-t/final-checkpoints/kulyk-en-uk'

	import torch
	from safetensors.torch import load_file, save_file
	import os

	# --- Configuration ---
	# Specify the path to your input .safetensors file
	input_filepath = "model.safetensors"
	# Specify the path for the new BF16 output file
	output_filepath = "model_bf16.safetensors"

	import pandas as pd

	from datasets import load_dataset
	from rensa import CMinHash, RMinHash
	from tqdm import tqdm

	COLUMN = "source"
	SPLIT = "train"
	ALGORITHM = "CMinHash"

	import json
	from glob import glob
	from os.path import basename

	files_all = glob("data/*.mp3")

	results = []
	for idx, filename in enumerate(files_all):
	duration = 0
	results.append({'file_name': basename(filename), 'duration': duration, 'transcription': '-'})

	fn q_rsqrt(number: f32) -> f32 {
	let threehalfs: f32 = 1.5;
	let x2: f32 = number * 0.5;
	let mut y: f32 = number;

	let i: u32 = y.to_bits(); // safely get the bit representation of the float
	let i: u32 = 0x5f3759df - (i >> 1); // what the heck?
	y = f32::from_bits(i); // safely convert bits back to float

	y * (threehalfs - (x2 * y * y)) // 1st iteration

	import torchaudio
	from speechbrain.pretrained import VAD

	VAD = VAD.from_hparams(source="speechbrain/vad-crdnn-libriparty", savedir="pretrained_models/vad-crdnn-libriparty")

	test_file = 'a.wav'

	boundaries = VAD.get_speech_segments(test_file)
	segments = VAD.get_segments(boundaries, test_file)