ethicalabs/Echo-DSRN-004-Kurtis-EON1-SFT-Mix-PEFT
Text Generation
•
Updated
•
24
Massimo Roberto Scamarcia PRO
mrs83
AI & ML interests
Natural Language Processing, Text Generation, Question Answering, Data Augmentation, Knowledge Transfer, Chain-of-Thought, ResearchOps, MLOps
Recent Activity
new activity
about 3 hours ago
ethicalabs/Kurtis-EON1:Mid-Training - Phase 005: Kurtis SFT Mix + OpenHermes2.5
new activity
1 day ago
ethicalabs/Kurtis-EON1:Mid-Training - Phase 004: Kurtis SFT Mix
updated
a model
1 day ago
ethicalabs/Echo-DSRN-004-Kurtis-EON1-SFT-Mix-PEFT
Organizations
Mid-Training - Phase 005: Kurtis SFT Mix + OpenHermes2.5
#3 opened about 3 hours ago
by
mrs83
Mid-Training - Phase 004: Kurtis SFT Mix
9
#2 opened 4 days ago
by
mrs83
updated
a
model
1 day ago
replied to
qgallouedec's
post
1 day ago
Thanks for sharing, we are using a similar recipe for our small models đź‘Ź
reacted to
qgallouedec's
post with 🔥
1 day ago
Post
2130
@CohereLabs
just released 🌿 Tiny Aya: a fully open-source 3B parameter model that speaks 70+ languages 🌍! But there’s a catch:
Tiny Aya is just a language model. It doesn’t support tool calling, the key capability that turns frontier models into powerful *agents*.
So the real question is:
How hard is it to turn Tiny Aya into an agent?
Turns out… it’s simple, thanks to Hugging Face TRL.
We’re sharing a hands-on example showing how to train Tiny Aya to turn it into a tool-calling agent using TRL, unlocking what could become the first *massively multilingual open agent*.
Small model. Global reach. Agent capabilities.
👉 https://github.com/huggingface/trl/blob/main/examples/notebooks/sft_tool_calling.ipynb
Tiny Aya is just a language model. It doesn’t support tool calling, the key capability that turns frontier models into powerful *agents*.
So the real question is:
How hard is it to turn Tiny Aya into an agent?
Turns out… it’s simple, thanks to Hugging Face TRL.
We’re sharing a hands-on example showing how to train Tiny Aya to turn it into a tool-calling agent using TRL, unlocking what could become the first *massively multilingual open agent*.
Small model. Global reach. Agent capabilities.
👉 https://github.com/huggingface/trl/blob/main/examples/notebooks/sft_tool_calling.ipynb
-
- 1 reply
replied to
their
post
1 day ago
updated
a
collection
3 days ago
updated
a
collection
4 days ago
published
a
model
4 days ago
liked
a
dataset
4 days ago
Mid-Training - Phase 003: HuggingFaceTB/smoltalk
4
#1 opened 6 days ago
by
mrs83
updated
a
model
5 days ago
updated
a
collection
5 days ago
published
a
model
5 days ago
updated
a
collection
5 days ago
reacted to
kostakoff's
post with 🚀👍
6 days ago
Post
3254
My home lab for AI models - llmlaba v1
After I began learning MLOps I realized that I needed some kind of home lab, there are a lot of GPUs that I need to learn how to set up and test.
So I spent some time to do a researching which platform I could buy or build.
My requirements ware:
- Limited budget
- Power supply 1 kW or higher
- Few PCIe slots to be able to install more than one gpu
- Zero maintenance cost, I don't want spend a lot of time or money to maintain lab hardware, except for the GPUs
I chose the Intel Mac Pro 7.1:
- Prices on eBay acceptable
- Excelent cooling
- 1.4 kW power supply
- 7 PCIe slots
- Zero maintenance: I don't need to do anything with the Mac Pro hardware; it just works
- Classic UEFI boot loader
It requires a bit of OS preparation:
1. Install Ubuntu 24.04 (it works with the general PC ISO image)
2. Set up T2 drivers
3. Install t2fanrd to manually manage fans (/etc/t2fand.conf) https://wiki.t2linux.org/guides/fan/
4. Fix PCIe BAR: add pci=realloc to GRUB_CMDLINE_LINUX_DEFAULT so the Linux kernel will properly initializes server GPUs without Graphics Output Protocol
5. Install NVIDIA GPU driver:
And it works!
I was able to run server-grade Nvidia Tesla P100 (required DIY air duct), and consumer Nvidia Titan X, Titan V, GTX 1080 cards on the old Mac Pro 7.1 - even three in parallel.
llmlaba
After I began learning MLOps I realized that I needed some kind of home lab, there are a lot of GPUs that I need to learn how to set up and test.
So I spent some time to do a researching which platform I could buy or build.
My requirements ware:
- Limited budget
- Power supply 1 kW or higher
- Few PCIe slots to be able to install more than one gpu
- Zero maintenance cost, I don't want spend a lot of time or money to maintain lab hardware, except for the GPUs
I chose the Intel Mac Pro 7.1:
- Prices on eBay acceptable
- Excelent cooling
- 1.4 kW power supply
- 7 PCIe slots
- Zero maintenance: I don't need to do anything with the Mac Pro hardware; it just works
- Classic UEFI boot loader
It requires a bit of OS preparation:
1. Install Ubuntu 24.04 (it works with the general PC ISO image)
2. Set up T2 drivers
sudo apt install -y dkms linux-headers-$(uname -r) applesmc-t2 apple-bce lm-sensors3. Install t2fanrd to manually manage fans (/etc/t2fand.conf) https://wiki.t2linux.org/guides/fan/
4. Fix PCIe BAR: add pci=realloc to GRUB_CMDLINE_LINUX_DEFAULT so the Linux kernel will properly initializes server GPUs without Graphics Output Protocol
5. Install NVIDIA GPU driver:
sudo apt install nvidia-driver-570And it works!
I was able to run server-grade Nvidia Tesla P100 (required DIY air duct), and consumer Nvidia Titan X, Titan V, GTX 1080 cards on the old Mac Pro 7.1 - even three in parallel.
- 3 replies
replied to
their
post
6 days ago
For MLX-LM we can only use Apple MPS. Re GPUs, at this moment I only have a AMD Ryzen AI Max+ 395 and AMD Instinct MI300X for rent.
updated
a
model
7 days ago