AtAndDev/marco-qwq-7B
Updated
alkinun
AtAndDev
AI & ML interests
LLMs, Alignment, Merging, Unsloth, DPO, SFT, ORPO, SPIN..
Recent Activity
updated
a model
about 2 hours ago
AtAndDev/marco-qwq-7B
Reacted to
prithivMLmods's
post
with 🤗
about 14 hours ago
Milestone for Flux.1 Dev 🔥
💢The Flux.1 Dev model has crossed 1️⃣0️⃣,0️⃣0️⃣0️⃣ creative public adapters! 🎈
🔗 https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev
💢This includes:
- 266 Finetunes
- 19 Quants
- 4 Merges
💢 Here’s the 10,000th public adapter : 😜
+ https://huggingface.co/strangerzonehf/Flux-3DXL-Partfile-0006
💢 Page :
+ https://huggingface.co/strangerzonehf
💢 Collection :
+ https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
Reacted to
burtenshaw's
post
with ❤️
about 15 hours ago
For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. It’s not big like Li Yin and @mlabonne, it’s just smol.
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
Organizations
AtAndDev's activity
updated
a
model
about 2 hours ago
reacted to
prithivMLmods's
post with 🤗
about 14 hours ago
Post
849
Milestone for Flux.1 Dev 🔥
💢The Flux.1 Dev model has crossed 1️⃣0️⃣,0️⃣0️⃣0️⃣ creative public adapters! 🎈
🔗 https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev
💢This includes:
- 266 Finetunes
- 19 Quants
- 4 Merges
💢 Here’s the 10,000th public adapter : 😜
+ strangerzonehf/Flux-3DXL-Partfile-0006
💢 Page :
+ https://huggingface.co/strangerzonehf
💢 Collection :
+ prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
💢The Flux.1 Dev model has crossed 1️⃣0️⃣,0️⃣0️⃣0️⃣ creative public adapters! 🎈
🔗 https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev
💢This includes:
- 266 Finetunes
- 19 Quants
- 4 Merges
💢 Here’s the 10,000th public adapter : 😜
+ strangerzonehf/Flux-3DXL-Partfile-0006
💢 Page :
+ https://huggingface.co/strangerzonehf
💢 Collection :
+ prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
reacted to
burtenshaw's
post with ❤️
about 15 hours ago
Post
797
For anyone looking to boost their LLM fine-tuning and alignment skills this decemeber. We're running this free and open course called smol course. It’s not big like Li Yin and
@mlabonne
, it’s just smol.
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
👷 It focuses on practical use cases, so if you’re working on something, bring it along.
👯♀️ It’s peer reviewed and open so you can discuss and get feedback.
🤘 If you’re already a smol pro, feel free to drop a star or issue.
> > Part 1 starts now, and it’s on instruction tuning!
https://github.com/huggingface/smol-course
reacted to
vincentg64's
post with 👀
about 17 hours ago
Post
814
LLM 2.0, the New Generation of Large Language Models https://mltblog.com/49ksOLL
I get many questions about the radically different LLM technology that I started to develop 2 years ago. Initially designed to retrieve information that I could no longer find on the Internet, not with search, OpenAI, Gemini, Perplexity or any other platform, it evolved to become the ideal solution for professional enterprise users. Now agentic and multimodal, automating business tasks at scale with lightning speed, consistently delivering real ROI, bypassing the costs associated to training and GPU with zero weight and explainable AI, tested and developed for Fortune 100 company.
So, what is behind the scenes, how different is it compared to LLM 1.0 (GPT and the likes), how can it be hallucination-free, what makes it a game changer, how did it eliminate prompt engineering, how does it handle knowledge graphs without neural networks, and what are the other benefits?
In a nutshell, the performance is due to building a robust architecture from the ground up and at every step, offering far more than a prompt box, relying on home-made technology rather than faulty Python libraries, and designed by enterprise and tech visionaries for enterprise users.
Contextual smart crawling to retrieve underlying taxonomies, augmented taxonomies, long contextual multi-tokens, real-time fine-tunning, increased security, LLM router with specialized sub-LLMs, an in-memory database architecture of its own to efficiently handle sparsity in keyword associations, contextual backend tables, agents built on the backend, mapping between prompt and corpus keywords, customized PMI rather than cosine similarity, variable-length embeddings, and the scoring engine (the new “PageRank” of LLMs) returning results along with the relevancy scores, are but a few of the differentiators.
➡️ Read the full article, at https://mltblog.com/49ksOLL
I get many questions about the radically different LLM technology that I started to develop 2 years ago. Initially designed to retrieve information that I could no longer find on the Internet, not with search, OpenAI, Gemini, Perplexity or any other platform, it evolved to become the ideal solution for professional enterprise users. Now agentic and multimodal, automating business tasks at scale with lightning speed, consistently delivering real ROI, bypassing the costs associated to training and GPU with zero weight and explainable AI, tested and developed for Fortune 100 company.
So, what is behind the scenes, how different is it compared to LLM 1.0 (GPT and the likes), how can it be hallucination-free, what makes it a game changer, how did it eliminate prompt engineering, how does it handle knowledge graphs without neural networks, and what are the other benefits?
In a nutshell, the performance is due to building a robust architecture from the ground up and at every step, offering far more than a prompt box, relying on home-made technology rather than faulty Python libraries, and designed by enterprise and tech visionaries for enterprise users.
Contextual smart crawling to retrieve underlying taxonomies, augmented taxonomies, long contextual multi-tokens, real-time fine-tunning, increased security, LLM router with specialized sub-LLMs, an in-memory database architecture of its own to efficiently handle sparsity in keyword associations, contextual backend tables, agents built on the backend, mapping between prompt and corpus keywords, customized PMI rather than cosine similarity, variable-length embeddings, and the scoring engine (the new “PageRank” of LLMs) returning results along with the relevancy scores, are but a few of the differentiators.
➡️ Read the full article, at https://mltblog.com/49ksOLL
reacted to
clem's
post with 🔥🚀
about 18 hours ago
Post
2774
Six predictions for AI in 2025 (and a review of how my 2024 predictions turned out):
- There will be the first major public protest related to AI
- A big company will see its market cap divided by two or more because of AI
- At least 100,000 personal AI robots will be pre-ordered
- China will start to lead the AI race (as a consequence of leading the open-source AI race).
- There will be big breakthroughs in AI for biology and chemistry.
- We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face.
How my predictions for 2024 turned out:
- A hyped AI company will go bankrupt or get acquired for a ridiculously low price
✅ (Inflexion, AdeptAI,...)
- Open-source LLMs will reach the level of the best closed-source LLMs
✅ with QwQ and dozens of others
- Big breakthroughs in AI for video, time-series, biology and chemistry
✅ for video 🔴for time-series, biology and chemistry
- We will talk much more about the cost (monetary and environmental) of AI
✅Monetary 🔴Environmental (😢)
- A popular media will be mostly AI-generated
✅ with NotebookLM by Google
- 10 millions AI builders on Hugging Face leading to no increase of unemployment
🔜currently 7M of AI builders on Hugging Face
- There will be the first major public protest related to AI
- A big company will see its market cap divided by two or more because of AI
- At least 100,000 personal AI robots will be pre-ordered
- China will start to lead the AI race (as a consequence of leading the open-source AI race).
- There will be big breakthroughs in AI for biology and chemistry.
- We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face.
How my predictions for 2024 turned out:
- A hyped AI company will go bankrupt or get acquired for a ridiculously low price
✅ (Inflexion, AdeptAI,...)
- Open-source LLMs will reach the level of the best closed-source LLMs
✅ with QwQ and dozens of others
- Big breakthroughs in AI for video, time-series, biology and chemistry
✅ for video 🔴for time-series, biology and chemistry
- We will talk much more about the cost (monetary and environmental) of AI
✅Monetary 🔴Environmental (😢)
- A popular media will be mostly AI-generated
✅ with NotebookLM by Google
- 10 millions AI builders on Hugging Face leading to no increase of unemployment
🔜currently 7M of AI builders on Hugging Face
- 2 replies
reacted to
akhaliq's
post with ❤️
about 19 hours ago
Post
1619
QwQ-32B-Preview is now available in anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: akhaliq/anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: akhaliq/anychat
- 1 reply
updated
2
datasets
about 19 hours ago
reacted to
m-ric's
post with 🚀🔥
about 19 hours ago
Post
1034
🤖 𝗔𝗱𝗼𝗯𝗲'𝘀 𝗰𝗼𝗱𝗲-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗼𝗳 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 - and their paper cites my work!
💡 Reminder: In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.
➡️ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!
Here's what the LLM generations can look like with the proper prompt:
Thought: I need to access the excel file using a different method.
Action:
Then your system executes this and appends the observation to the agent's memory.
Why is this code formulation better than classical tool use formulation as JSON? The paper explains:
"Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."
The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.
And they observe that code agents perform much better, reaching the top of GAIA leaderboard! 🥇
Go take a look, it's really clear and informative!
Paper added to my agents collection 👉 m-ric/agents-65ba776fbd9e29f771c07d4e
💡 Reminder: In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.
➡️ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!
Here's what the LLM generations can look like with the proper prompt:
Thought: I need to access the excel file using a different method.
Action:
def access_excel_file(file_path)
... # rest of the code (the agent does writes it, but I don't have room in this post)
return rowsThen your system executes this and appends the observation to the agent's memory.
Why is this code formulation better than classical tool use formulation as JSON? The paper explains:
"Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."
The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.
And they observe that code agents perform much better, reaching the top of GAIA leaderboard! 🥇
Go take a look, it's really clear and informative!
Paper added to my agents collection 👉 m-ric/agents-65ba776fbd9e29f771c07d4e
reacted to
merve's
post with 🚀🔥😎
2 days ago
Post
1838
small but mighty 🔥
you can fine-tune SmolVLM on an L4 with batch size of 4 and it will only take 16.4 GB VRAM 🫰🏻 also with gradient accumulation simulated batch size is 16 ✨
I made a notebook that includes all the goodies: QLoRA, gradient accumulation, gradient checkpointing with explanations on how they work 💝 https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
you can fine-tune SmolVLM on an L4 with batch size of 4 and it will only take 16.4 GB VRAM 🫰🏻 also with gradient accumulation simulated batch size is 16 ✨
I made a notebook that includes all the goodies: QLoRA, gradient accumulation, gradient checkpointing with explanations on how they work 💝 https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
reacted to
akhaliq's
post with 👀
3 days ago
Post
1619
QwQ-32B-Preview is now available in anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: akhaliq/anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: akhaliq/anychat
-
- 1 reply
reacted to
KnutJaegersberg's
post with 👀
3 days ago
Post
1729
DrNicefellow/Qwen-QwQ-32B-Preview-4.25bpw-exl2
Rumor has it this is currently the best model for 24 GB VRAM local usage.
DrNicefellow/Qwen-QwQ-32B-Preview-4.25bpw-exl2
Rumor has it this is currently the best model for 24 GB VRAM local usage.
DrNicefellow/Qwen-QwQ-32B-Preview-4.25bpw-exl2
liked
a
Space
3 days ago