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AlejandroOlmedo
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updated a model 1 day ago
AlejandroOlmedo/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-8bit-mlx
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AlejandroOlmedo/DeepScaleR-1.5B-Preview-8bit-mlx
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updated 8 models 1 day ago
updated a model 5 days ago
published a model 5 days ago
reacted to tegridydev's post with 👍 6 days ago
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1901
WTF is Fine-Tuning? (intro4devs)

Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃

Here's a cheat sheet for devs (but open to anyone!)

---

TL;DR

- Full Fine-Tuning: Max performance, high resource needs, best reliability.
- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
- RAFT: Best for fact-grounded models with dynamic retrieval.
- RLHF: Produces ethical, high-quality conversational AI, but expensive.

Choose wisely and match your approach to your task, budget, and deployment constraints.

I just posted the full extended article here
if you want to continue reading >>>

https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
reacted to Quazim0t0's post with 👍 6 days ago
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2328
My first attempt at using SmolAgents:
Quazim0t0/CSVAgent

The video attached was an example for this space.

Based on ZennyKenny's SqlAgent:
ZennyKenny/sqlAgent

You can upload a CSV file and it will automatically populate the table, then you can ask questions about the data.

Grab a sample CSV file here: https://github.com/datablist/sample-csv-files

The questions that can be asked may be limited.

_______________________
Second: Quazim0t0/TXTAgent
Created an Agent that converts a .txt file into a CSV file, then you can ask about the data and also download the CSV file that was generated.

_______________________
Third: Quazim0t0/ReportAgent
Upload Multiple TXT/DOC files to then generate a report from those files.

_______________________
Lastly: Quazim0t0/qResearch
A Research tool that uses DuckDuckGo for Web Searches, Wikipedia and tries to refine the answers in MLA Format.
updated 3 models 6 days ago
liked a model 7 days ago
reacted to jasoncorkill's post with 🚀 7 days ago
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2484
This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!

All that at less than the cost of a single hour of a typical ML engineer in Zurich!

The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style, Speed of Time flow and Quality of Physics.

Rapidata/text-2-video-Rich-Human-Feedback
reacted to clem's post with 👍 7 days ago
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3325
We crossed 1B+ tokens routed to inference providers partners on HF, that we released just a few days ago.

Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.

Have you been using any integration and how can we make it better?

https://huggingface.co/blog/inference-providers
updated 2 models 8 days ago