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fix: fix typos #33

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fix: fix typos #33

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18 changes: 9 additions & 9 deletions ds-with-mac/content/posts/ai-odessy-february-24/index.md
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Expand Up @@ -7,8 +7,8 @@ slug: ai-odyssey-february-24
author: Marcus Elwin

draft: false
date: 2024-02-24T14:40:39+01:00
lastmod:
date: 2024-02-25T14:40:39+01:00
lastmod: 2024-02-26T14:40:39+01:00
expiryDate:
publishDate:

Expand Down Expand Up @@ -47,7 +47,7 @@ On February, 15th, 2024 Google announced the release of [Gemini 1.5](https://blo
* Improved _In-Context_ learning skills from long prompts, without needing fine-tuning.
* With a standard context window of **128,000** tokens, that can be extended to **1** million tokens.

Let that sink in a bit **1 million** tokens which is roughly **700,000+**. A "regular" book :book: has somewhere between _250-300_ words per page. This would mean that you can use a book of between **2300+** pages as context to the Gemini 1.5 Pro model.
Let that sink in a bit **1 million** tokens which is roughly **700,000+** pages. A "regular" book :book: has somewhere between _250-300_ words per page. This would mean that you can use a book of between **2300+** pages as context to the Gemini 1.5 Pro model.

For instance, you could feed in the entire _Lord of the Rings_ and _The Count of Monte Cristo_ at the same time as both of these books are roughly **1200** pages.

Expand All @@ -66,7 +66,7 @@ Finally, in terms of performance, the paper also mentions:
> that 1.5 Pro is able to maintain **near-perfect recall** on multi-modal versions of needle-in-a-haystack
> (see Section 4.2.1.2) and is able to effectively use its context to retrieve and reason over large amounts of data

I always get a bit _skeptical_ (I guess it is the DS in me) when I hear that metrics are close to 100% or near-perfect as this normally means that you are overfitting or doing something wrong. However, it is still very impressive in comparison to GPT-4V on the same dataset. While be exciting to see more benchmarks on this going forward.
I always get a bit _skeptical_ (I guess it is the DS in me) when I hear that metrics are close to 100% or near-perfect as this normally means that you are overfitting or doing something wrong. However, it is still very impressive in comparison to `GPT-4V` on the same dataset. While be exciting to see more benchmarks on this going forward.

[^1]: Similar to the Mixtral model launched last year: https://mistral.ai/news/mixtral-of-experts/

Expand Down Expand Up @@ -99,7 +99,7 @@ Looking at the technical paper _Gemma_ in its base form seems to fair well again

Also looking at the [LLM Leaderboard](https://huggingface.co/blog/gemma) Gemma is ranking highly in comparison to other 7B models. While be interesting to see when we start to see some more fine-tuned versions of Gemma.

Finally, it is also cool to see the quick integration with the Keras library as well where it is as simple as the below to start testing Gemma:
Finally, it is also cool to see the quick integration with the `Keras` library as well where it is as simple as the below to start testing Gemma:
{{< highlight python "linenos=inline, style=monokai" >}}
# load model
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
Expand Down Expand Up @@ -154,8 +154,8 @@ Predibase mentions the following, regarding LoRA [^3]:
![LoRA land benchmarks vs GPT-4](/lora_land_bench.png "LLM Benchmarks: 25 fine-tuned Mistral-7b adapters that outperform GPT-4. Image from Predibase.")

### Why it interests me :eyes:
* You should not sleep on fine-tuning, often a fine-tuned task-specific model can be a more generalized model.
* A more cost-efficient way of serving custom LLMs using Predibase, requiring less computing and giving you more control.
* You should not sleep on fine-tuning, often a fine-tuned task-specific model can _beat_ a more generalized model, on domain-specific tasks.
* A more _cost-efficient_ way of serving custom LLMs using Predibase, requiring less computing and giving you more control.

[^3]: Low-rank Adapter (LoRA) finetuning is a method that reduces memory
requirements by using a small set of trainable parameters, often termed adapters, while not updating
Expand All @@ -171,7 +171,7 @@ the full model parameters that remain fixed.
On February 22nd, 2024 Stability AI announced that they have released (in an early preview) "Stable Diffusion 3". Like SORA this is a text-to-image model.

What is mentioned in the release:
* Range of models from 800M to 8B parameters
* Range of models from **800M** to **8B** parameters
* Combination of diffusion transform architecture and flow matching

No technical report is out yet, but will be interesting to dive deeper when it gets released.
Expand Down Expand Up @@ -310,7 +310,7 @@ According to _Groq_ their chip overcomes the following challenges, for LLMs:
2. Memory bandwidth

When doing a small test it is fast:
![Groq and the meaning of life.](/groq.png "Testing Qroq chat interface with an impressive 500+ tokens/seconds. Served using LPUs.")
![Groq and the meaning of life.](/groq.png "Testing Groq chat interface with an impressive 500+ tokens/seconds. Served using LPUs.")

I think it is very interesting to see other more specialized chip providers show up, such as Groq. However, there are also other providers such as [graphcore](https://www.graphcore.ai/) offering IPUs [^5].

Expand Down
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