cognitivecomputations (Cognitive Computations)

Abhaykoul

posted an update 5 days ago

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1473

🔥 BIG ANNOUNCEMENT: THE HELPINGAI API IS LIVE! 🔥

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6 replies

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AtAndDev

posted an update 7 days ago

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298

@s3nh Hey man check your discord! Got some news.

4 replies

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bartowski

posted an update 15 days ago

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7379

Looks like Q4_0_N_M file types are going away

Before you panic, there's a new "preferred" method which is online (I prefer the term on-the-fly) repacking, so if you download Q4_0 and your setup can benefit from repacking the weights into interleaved rows (what Q4_0_4_4 was doing), it will do that automatically and give you similar performance (minor losses I think due to using intrinsics instead of assembly, but intrinsics are more maintainable)

You can see the reference PR here:

https://github.com/ggerganov/llama.cpp/pull/10446

So if you update your llama.cpp past that point, you won't be able to run Q4_0_4_4 (unless they add backwards compatibility back), but Q4_0 should be the same speeds (though it may currently be bugged on some platforms)

As such, I'll stop making those newer model formats soon, probably end of this week unless something changes, but you should be safe to download and Q4_0 quants and use those !

Also IQ4_NL supports repacking though not in as many shapes yet, but should get a respectable speed up on ARM chips, PR for that can be found here: https://github.com/ggerganov/llama.cpp/pull/10541

Remember, these are not meant for Apple silicon since those use the GPU and don't benefit from the repacking of weights

12 replies

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ImranzamanML

posted an update 19 days ago

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435

Deep understanding of (C-index) evaluation measure for better model
Lets start with three patients groups:

Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).

Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.

Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:

Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:

(P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅
(P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
(P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
Total pairs = 3
Total concordant pairs = 3

C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0

Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:

Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:

Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)

Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8

Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16

Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64

Step 4: Interpret the Results
A high Stratified C-index means:

The model predicts well overall (high mean C-index).

1 reply

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cfahlgren1

posted an update 21 days ago

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1807

You can just ask things 🗣️

"show me messages in the coding category that are in the top 10% of reward model scores"

Download really high quality instructions from the Llama3.1 405B synthetic dataset 🔥

argilla/magpie-ultra-v1.0

bartowski

posted an update 22 days ago

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10430

Old mixtral model quants may be broken!

Recently Slaren over on llama.cpp refactored the model loader - in a way that's super awesome and very powerful - but with it came breaking of support for "split tensor MoE models", which applies to older mixtral models

You may have seen my upload of one such older mixtral model, ondurbin/bagel-dpo-8x7b-v0.2, and with the newest changes it seems to be able to run without issue

If you happen to run into issues with any other old mixtral models, drop a link here and I'll try to remake them with the new changes so that we can continue enjoying them :)

1 reply

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cfahlgren1

posted an update 23 days ago

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2989

We just dropped an LLM inside the SQL Console 🤯

The amazing, new Qwen/Qwen2.5-Coder-32B-Instruct model can now write SQL for any Hugging Face dataset ✨

It's 2025, you shouldn't be hand writing SQL! This is a big step in making it where anyone can do in depth analysis on a dataset. Let us know what you think 🤗

HagalazAI

in cognitivecomputations/based 25 days ago

Create based-no-key.json

#3 opened 25 days ago by

HagalazAI

updated a dataset 25 days ago

cognitivecomputations/based

Viewer • Updated Jun 3, 2023 • 44 • 81 • 21

HagalazAI

in cognitivecomputations/based 25 days ago

Upload based-no-key.json

1

#2 opened 25 days ago by

HagalazAI

cfahlgren1

posted an update about 1 month ago

Post

909

observers 🔭 - automatically log all OpenAI compatible requests to a dataset💽

• supports any OpenAI compatible endpoint 💪
• supports DuckDB, Hugging Face Datasets, and Argilla as stores

> pip install observers

No complex framework. Just a few lines of code to start sending your traces somewhere. Let us know what you think! @davidberenstein1957 and I will continue iterating!

Here's an example dataset that was logged to Hugging Face from Ollama: cfahlgren1/llama-3.1-awesome-chatgpt-prompts

ehartford

updated a dataset about 1 month ago

cognitivecomputations/dolphin-fim

Updated Nov 22 • 122 • 3

cfahlgren1

posted an update about 1 month ago

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869

You can create charts, leaderboards, and filters on top of any Hugging Face dataset in less than a minute

• ASCII Bar Charts 📊
• Powered by DuckDB WASM ⚡
• Download results to Parquet 💽
• Embed and Share results with friends 📬

Do you have any interesting queries?

cfahlgren1

posted an update about 1 month ago

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744

What rank are you on Hugging Face Top Yappers? 🗣️

Find your rank here with this link: cfahlgren1/hub-stats

The Top 3:
- @fdaudens
- @singhsidhukuldeep
- @akhaliq

I am at #71 and need to get my numbers up! 📈

4 replies

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cfahlgren1

posted an update about 1 month ago

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3086

You can clean and format datasets entirely in the browser with a few lines of SQL.

In this post, I replicate the process @mlabonne used to clean the new microsoft/orca-agentinstruct-1M-v1 dataset.

The cleaning process consists of:
- Joining the separate splits together / add split column
- Converting string messages into list of structs
- Removing empty system prompts

https://huggingface.co/blog/cfahlgren1/the-beginners-guide-to-cleaning-a-dataset

Here's his new cleaned dataset: mlabonne/orca-agentinstruct-1M-v1-cleaned

1 reply

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cfahlgren1

posted an update about 1 month ago

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2225

Why use Google Drive when you can have:

• Free storage with generous limits🆓
• Dataset Viewer (Sorting, Filtering, FTS) 🔍
• Third Party Library Support
• SQL Console 🟧
• Security 🔒
• Community, Reach, and Visibility 📈

It's a no brainer!

Check out our post on what you get instantly out of the box when you create a dataset.
https://huggingface.co/blog/researcher-dataset-sharing

1 reply

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louisbrulenaudet

posted an update about 1 month ago

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1728

I’ve published a new dataset to simplify model merging 🤗

This dataset facilitates the search for compatible architectures for model merging with @arcee_ai’s mergekit, streamlining the automation of high-performance merge searches 📖

Dataset : louisbrulenaudet/mergekit-configs