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Norod78

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Fooling around with Generative machine learning models.

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Multilingual Tokenization Showdown
Analyzing 12 LLM Tokenizers Across 204 Languages.

First, I've created a dataset with Wikipedia's "Cat" article text in 272 languages:
Norod78/WikiCat-Multilingual

For each language entry with at least 100 words, I tokenized the text using 12 tokenizers and calculated the "Characters per token" ratio and "Word per token" ratio. The higher this ratio is, the more information each token represents on average for that language (and perhaps allowing the llm to potentially learn more per-parameter if trained on a dataset of that language).

You can see a slideshow summary of the results here:
https://norod.github.io/wikicat-tokenizer-eval/tokenizer-slideshow.html

I hope I interpreted the results correctly, I've made the code available on GitHub so you can re-create the raw results jsonl with this repo:
https://github.com/Norod/wikicat-tokenizer-eval

Post on X:
https://x.com/Norod78/status/1984366900550266999

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I've prepared a Google Colab notebook which allows you to play with interpolating between different people using IP-Adapter SDXL Face-ID Plus.

#Prepare a list t of num_of_results values between 0 and 1
t_space = torch.linspace(0, 1, num_of_results)
for t in tqdm(t_space):
    mix_factor = t.item()
    # interpolate between the two face images 
    image = (image1 * (1 - mix_factor) + image2 * mix_factor).astype(np.uint8)
    # interpolate between the two face embedding 
    faceid_embeds = torch.lerp(faceid_embeds1, faceid_embeds2, t)
   #generate interpolated result
    images = ip_model.generate(prompt=prompt, negative_prompt=negative_prompt, face_image=image, faceid_embeds=faceid_embeds, shortcut=v2, num_samples=2, scale=scale, s_scale=s_scale, guidance_scale=guidance_scale, width=width, height=height, num_inference_steps=steps, seed=seed)