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2 days ago
Simple summary on DeepSeek AI's Janus-Pro: A fresh take on multimodal AI!
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
✦ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
posted
an
update
2 days ago
Simple summary on DeepSeek AI's Janus-Pro: A fresh take on multimodal AI!
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
✦ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
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reacted to
their
post with 👍
2 days ago
Post
1837
Simple summary on DeepSeek AI's Janus-Pro: A fresh take on multimodal AI!
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
✦ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
✦ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
posted
an
update
2 days ago
Post
1837
Simple summary on DeepSeek AI's Janus-Pro: A fresh take on multimodal AI!
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
✦ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
It builds on its predecessor, Janus, by tweaking the training methodology rather than the model architecture. The result? Improved performance in understanding and generating multimodal data.
Janus-Pro uses a three-stage training strategy, similar to Janus, but with key modifications:
✦ Stage 1 & 2: Focus on separate training for specific objectives, rather than mixing data.
✦ Stage 3: Fine-tuning with a careful balance of multimodal data.
Benchmarks show Janus-Pro holds its own against specialized models like TokenFlow XL and MetaMorph, and other multimodal models like SD3 Medium and DALL-E 3.
The main limitation? Low image resolution (384x384). However, this seems like a strategic choice to focus on establishing a solid "recipe" for multimodal models. Future work will likely leverage this recipe and increased computing power to achieve higher resolutions.
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7 days ago
Post
1664
New look for AI powered paper reviews from the list by Hugging Face Daily Papers ( managed by the
@akhaliq
)
Bookmark the webpage along, check comprehensive reviews by Google DeepMind Gemini 1.5, and listen to audio podcast made by the same tech used in NotebookLM.
Link: https://deep-diver.github.io/ai-paper-reviewer/
This is not an official service by Hugging Face. It is just a service developed by an individual developer using his own money :)
Bookmark the webpage along, check comprehensive reviews by Google DeepMind Gemini 1.5, and listen to audio podcast made by the same tech used in NotebookLM.
Link: https://deep-diver.github.io/ai-paper-reviewer/
This is not an official service by Hugging Face. It is just a service developed by an individual developer using his own money :)
posted
an
update
7 days ago
Post
1664
New look for AI powered paper reviews from the list by Hugging Face Daily Papers ( managed by the
@akhaliq
)
Bookmark the webpage along, check comprehensive reviews by Google DeepMind Gemini 1.5, and listen to audio podcast made by the same tech used in NotebookLM.
Link: https://deep-diver.github.io/ai-paper-reviewer/
This is not an official service by Hugging Face. It is just a service developed by an individual developer using his own money :)
Bookmark the webpage along, check comprehensive reviews by Google DeepMind Gemini 1.5, and listen to audio podcast made by the same tech used in NotebookLM.
Link: https://deep-diver.github.io/ai-paper-reviewer/
This is not an official service by Hugging Face. It is just a service developed by an individual developer using his own money :)
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9 days ago
Post
1962
Simple summarization of Evolving Deeper LLM Thinking (Google DeepMind)
The process starts by posing a question.
1) The LLM generates initial responses.
2) These generated responses are evaluated according to specific criteria (program-based checker).
3) The LLM critiques the evaluated results.
4) The LLM refines the responses based on the evaluation, critique, and original responses.
The refined response is then fed back into step 2). If it meets the criteria, the process ends. Otherwise, the algorithm generates more responses based on the refined ones (with some being discarded, some remaining, and some responses potentially being merged).
Through this process, it demonstrated excellent performance in complex scheduling problems (travel planning, meeting scheduling, etc.). It's a viable method for finding highly effective solutions in specific scenarios.
However, there are two major drawbacks:
🤔 An excessive number of API calls are required. (While the cost might not be very high, it leads to significant latency.)
🤔 The evaluator is program-based. (This limits its use as a general method. It could potentially be modified/implemented using LLM as Judge, but that would introduce additional API costs for evaluation.)
https://arxiv.org/abs/2501.09891
The process starts by posing a question.
1) The LLM generates initial responses.
2) These generated responses are evaluated according to specific criteria (program-based checker).
3) The LLM critiques the evaluated results.
4) The LLM refines the responses based on the evaluation, critique, and original responses.
The refined response is then fed back into step 2). If it meets the criteria, the process ends. Otherwise, the algorithm generates more responses based on the refined ones (with some being discarded, some remaining, and some responses potentially being merged).
Through this process, it demonstrated excellent performance in complex scheduling problems (travel planning, meeting scheduling, etc.). It's a viable method for finding highly effective solutions in specific scenarios.
However, there are two major drawbacks:
🤔 An excessive number of API calls are required. (While the cost might not be very high, it leads to significant latency.)
🤔 The evaluator is program-based. (This limits its use as a general method. It could potentially be modified/implemented using LLM as Judge, but that would introduce additional API costs for evaluation.)
https://arxiv.org/abs/2501.09891
posted
an
update
9 days ago
Post
1962
Simple summarization of Evolving Deeper LLM Thinking (Google DeepMind)
The process starts by posing a question.
1) The LLM generates initial responses.
2) These generated responses are evaluated according to specific criteria (program-based checker).
3) The LLM critiques the evaluated results.
4) The LLM refines the responses based on the evaluation, critique, and original responses.
The refined response is then fed back into step 2). If it meets the criteria, the process ends. Otherwise, the algorithm generates more responses based on the refined ones (with some being discarded, some remaining, and some responses potentially being merged).
Through this process, it demonstrated excellent performance in complex scheduling problems (travel planning, meeting scheduling, etc.). It's a viable method for finding highly effective solutions in specific scenarios.
However, there are two major drawbacks:
🤔 An excessive number of API calls are required. (While the cost might not be very high, it leads to significant latency.)
🤔 The evaluator is program-based. (This limits its use as a general method. It could potentially be modified/implemented using LLM as Judge, but that would introduce additional API costs for evaluation.)
https://arxiv.org/abs/2501.09891
The process starts by posing a question.
1) The LLM generates initial responses.
2) These generated responses are evaluated according to specific criteria (program-based checker).
3) The LLM critiques the evaluated results.
4) The LLM refines the responses based on the evaluation, critique, and original responses.
The refined response is then fed back into step 2). If it meets the criteria, the process ends. Otherwise, the algorithm generates more responses based on the refined ones (with some being discarded, some remaining, and some responses potentially being merged).
Through this process, it demonstrated excellent performance in complex scheduling problems (travel planning, meeting scheduling, etc.). It's a viable method for finding highly effective solutions in specific scenarios.
However, there are two major drawbacks:
🤔 An excessive number of API calls are required. (While the cost might not be very high, it leads to significant latency.)
🤔 The evaluator is program-based. (This limits its use as a general method. It could potentially be modified/implemented using LLM as Judge, but that would introduce additional API costs for evaluation.)
https://arxiv.org/abs/2501.09891
reacted to
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post with 👍
10 days ago
Post
1977
Simple Summarization on DeepSeek-R1 from DeepSeek AI
The RL stage is very important.
↳ However, it is difficult to create a truly helpful AI for people solely through RL.
↳ So, we applied a learning pipeline consisting of four stages: providing a good starting point, reasoning RL, SFT, and safety RL, and achieved performance comparable to o1.
↳ Simply fine-tuning other open models with the data generated by R1-Zero (distillation) resulted in performance comparable to o1-mini.
Of course, this is just a brief overview and may not be of much help. All models are accessible on Hugging Face, and the paper can be read through the GitHub repository.
Model: https://huggingface.co/deepseek-ai
Paper: https://github.com/deepseek-ai/DeepSeek-R1
The RL stage is very important.
↳ However, it is difficult to create a truly helpful AI for people solely through RL.
↳ So, we applied a learning pipeline consisting of four stages: providing a good starting point, reasoning RL, SFT, and safety RL, and achieved performance comparable to o1.
↳ Simply fine-tuning other open models with the data generated by R1-Zero (distillation) resulted in performance comparable to o1-mini.
Of course, this is just a brief overview and may not be of much help. All models are accessible on Hugging Face, and the paper can be read through the GitHub repository.
Model: https://huggingface.co/deepseek-ai
Paper: https://github.com/deepseek-ai/DeepSeek-R1
- 1 reply
posted
an
update
10 days ago
Post
1977
Simple Summarization on DeepSeek-R1 from DeepSeek AI
The RL stage is very important.
↳ However, it is difficult to create a truly helpful AI for people solely through RL.
↳ So, we applied a learning pipeline consisting of four stages: providing a good starting point, reasoning RL, SFT, and safety RL, and achieved performance comparable to o1.
↳ Simply fine-tuning other open models with the data generated by R1-Zero (distillation) resulted in performance comparable to o1-mini.
Of course, this is just a brief overview and may not be of much help. All models are accessible on Hugging Face, and the paper can be read through the GitHub repository.
Model: https://huggingface.co/deepseek-ai
Paper: https://github.com/deepseek-ai/DeepSeek-R1
The RL stage is very important.
↳ However, it is difficult to create a truly helpful AI for people solely through RL.
↳ So, we applied a learning pipeline consisting of four stages: providing a good starting point, reasoning RL, SFT, and safety RL, and achieved performance comparable to o1.
↳ Simply fine-tuning other open models with the data generated by R1-Zero (distillation) resulted in performance comparable to o1-mini.
Of course, this is just a brief overview and may not be of much help. All models are accessible on Hugging Face, and the paper can be read through the GitHub repository.
Model: https://huggingface.co/deepseek-ai
Paper: https://github.com/deepseek-ai/DeepSeek-R1
-
- 1 reply
reacted to
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post with 🤗
3 months ago
Post
1949
🎙️ Listen to the audio "Podcast" of every single Hugging Face Daily Papers.
Now, "AI Paper Reviewer" project can automatically generates audio podcasts on any papers published on arXiv, and this is integrated into the GitHub Action pipeline. I sounds pretty similar to hashtag#NotebookLM in my opinion.
🎙️ Try out yourself at https://deep-diver.github.io/ai-paper-reviewer/
This audio podcast is powered by Google technologies: 1) Google DeepMind Gemini 1.5 Flash model to generate scripts of a podcast, then 2) Google Cloud Vertex AI's Text to Speech model to synthesize the voice turning the scripts into the natural sounding voices (with latest addition of "Journey" voice style)
"AI Paper Reviewer" is also an open source project. Anyone can use it to build and own a personal blog on any papers of your interests. Hence, checkout the project repository below if you are interested in!
: https://github.com/deep-diver/paper-reviewer
This project is going to support other models including open weights soon for both text-based content generation and voice synthesis for the podcast. The only reason I chose Gemini model is that it offers a "free-tier" which is enough to shape up this projects with non-realtime batch generations. I'm excited to see how others will use this tool to explore the world of AI research, hence feel free to share your feedback and suggestions!
Now, "AI Paper Reviewer" project can automatically generates audio podcasts on any papers published on arXiv, and this is integrated into the GitHub Action pipeline. I sounds pretty similar to hashtag#NotebookLM in my opinion.
🎙️ Try out yourself at https://deep-diver.github.io/ai-paper-reviewer/
This audio podcast is powered by Google technologies: 1) Google DeepMind Gemini 1.5 Flash model to generate scripts of a podcast, then 2) Google Cloud Vertex AI's Text to Speech model to synthesize the voice turning the scripts into the natural sounding voices (with latest addition of "Journey" voice style)
"AI Paper Reviewer" is also an open source project. Anyone can use it to build and own a personal blog on any papers of your interests. Hence, checkout the project repository below if you are interested in!
: https://github.com/deep-diver/paper-reviewer
This project is going to support other models including open weights soon for both text-based content generation and voice synthesis for the podcast. The only reason I chose Gemini model is that it offers a "free-tier" which is enough to shape up this projects with non-realtime batch generations. I'm excited to see how others will use this tool to explore the world of AI research, hence feel free to share your feedback and suggestions!
- 3 replies
posted
an
update
3 months ago
Post
1949
🎙️ Listen to the audio "Podcast" of every single Hugging Face Daily Papers.
Now, "AI Paper Reviewer" project can automatically generates audio podcasts on any papers published on arXiv, and this is integrated into the GitHub Action pipeline. I sounds pretty similar to hashtag#NotebookLM in my opinion.
🎙️ Try out yourself at https://deep-diver.github.io/ai-paper-reviewer/
This audio podcast is powered by Google technologies: 1) Google DeepMind Gemini 1.5 Flash model to generate scripts of a podcast, then 2) Google Cloud Vertex AI's Text to Speech model to synthesize the voice turning the scripts into the natural sounding voices (with latest addition of "Journey" voice style)
"AI Paper Reviewer" is also an open source project. Anyone can use it to build and own a personal blog on any papers of your interests. Hence, checkout the project repository below if you are interested in!
: https://github.com/deep-diver/paper-reviewer
This project is going to support other models including open weights soon for both text-based content generation and voice synthesis for the podcast. The only reason I chose Gemini model is that it offers a "free-tier" which is enough to shape up this projects with non-realtime batch generations. I'm excited to see how others will use this tool to explore the world of AI research, hence feel free to share your feedback and suggestions!
Now, "AI Paper Reviewer" project can automatically generates audio podcasts on any papers published on arXiv, and this is integrated into the GitHub Action pipeline. I sounds pretty similar to hashtag#NotebookLM in my opinion.
🎙️ Try out yourself at https://deep-diver.github.io/ai-paper-reviewer/
This audio podcast is powered by Google technologies: 1) Google DeepMind Gemini 1.5 Flash model to generate scripts of a podcast, then 2) Google Cloud Vertex AI's Text to Speech model to synthesize the voice turning the scripts into the natural sounding voices (with latest addition of "Journey" voice style)
"AI Paper Reviewer" is also an open source project. Anyone can use it to build and own a personal blog on any papers of your interests. Hence, checkout the project repository below if you are interested in!
: https://github.com/deep-diver/paper-reviewer
This project is going to support other models including open weights soon for both text-based content generation and voice synthesis for the podcast. The only reason I chose Gemini model is that it offers a "free-tier" which is enough to shape up this projects with non-realtime batch generations. I'm excited to see how others will use this tool to explore the world of AI research, hence feel free to share your feedback and suggestions!
-
-
-
- 3 replies
reacted to
their
post with 👍
3 months ago
Post
4751
Effortlessly stay up-to-date with AI research trends using a new AI tool, "AI Paper Reviewer" !!
It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq ) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents.
blog link: https://deep-diver.github.io/ai-paper-reviewer/
Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!
: https://github.com/deep-diver/paper-reviewer
It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq ) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents.
blog link: https://deep-diver.github.io/ai-paper-reviewer/
Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!
: https://github.com/deep-diver/paper-reviewer
posted
an
update
3 months ago
Post
4751
Effortlessly stay up-to-date with AI research trends using a new AI tool, "AI Paper Reviewer" !!
It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq ) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents.
blog link: https://deep-diver.github.io/ai-paper-reviewer/
Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!
: https://github.com/deep-diver/paper-reviewer
It analyzes a list of Hugging Face Daily Papers(w/ @akhaliq ) and turn them into insightful blog posts. This project leverages Gemini models (1.5 Pro, 1.5 Flash, and 1.5 Flash-8B) for content generation and Upstage Document Parse for parsing the layout and contents.
blog link: https://deep-diver.github.io/ai-paper-reviewer/
Also, here is the link of GitHub repository for parsing and generating pipeline. By using this, you can easily build your own GitHub static pages based on any arXiv papers with your own interest!
: https://github.com/deep-diver/paper-reviewer
reacted to
m-ric's
post with 👀
9 months ago
Post
2798
💰❌ 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐆𝐏𝐔 𝐏𝐨𝐨𝐫 - 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐥𝐚𝐰𝐬 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧
🎆 Good news: 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗮𝗶𝗻𝘁 𝟮𝟬𝟬𝟲!
The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗿𝗮𝘁𝗶𝗼 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘇𝗲 𝘃𝘀 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼𝗸𝗲𝗻𝘀. Why is this question so important?
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.
The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.
But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.
💥 They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.
➡️ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.
✅ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!
🎆 Good news: 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗣𝗮𝗶𝗻𝘁 𝟮𝟬𝟬𝟲!
The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗿𝗮𝘁𝗶𝗼 𝗼𝗳 𝗺𝗼𝗱𝗲𝗹 𝘀𝗶𝘇𝗲 𝘃𝘀 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼𝗸𝗲𝗻𝘀. Why is this question so important?
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.
The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.
But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.
💥 They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.
➡️ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.
✅ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!
reacted to
their
post with 👍🔥
9 months ago
Post
4019
🦙🦙 LLaMA Duo project update
Last time, I gave a brief introduction about LLaMA Duo project with @sayakpaul . It is a simple toolset to aligning sLLM with service LLM with coverage dataset 👉🏻 (https://huggingface.co/posts/chansung/708646454991943).
- coverage dataset is what we believe to be the most important/desired (instruction, response) pairs. In system thinking, each instruction could be an analogy of a function from traditional programming. We make unit tests and measure the coverage % for all the features/functions. Similarly, we need to ensure if our fine-tuned model could handle what % of given instructions from coverage dataset satisfactory (hence coverage dataset).
We have tested it with "Coding" category of data from HuggingFaceH4/no_robots dataset. It has about 300 SFT training data points under Coding category. After fine-tuning Gemma 7B model on that, the result was very poor. LLaMA Duo's evaluation tool gave < 20% of metrics in similarity and preciseness on the test split.
So, we used LLaMA Duo's synthetic data generation tool to generate 60k data points that looks similar to the original dataset. We first created ~10k synthetic data points, then created 50k more based on the synthetic dataset itself.
After fine-tuning Gemma 7B on the 60k synthetic dataset, the evaluation result went up to 80~90% high. Also, when testing out the model in UI, it tends to give good responses.
It is a good showcase to transition from service LLM to sLLM or having a backup sLLM for service LLM failure scenarios. I am going to expand this experiments on all categories of no_robots dataset. It will roughly generate > 100k data points.
Here are some links:
- LLaMA Duo project repo: https://github.com/deep-diver/llamaduo
- 60k Coding synthetic dataset: chansung/merged_ds_coding
- Fine-tuned Gemma 7B model: chansung/coding_llamaduo_60k_v0.2
Last time, I gave a brief introduction about LLaMA Duo project with @sayakpaul . It is a simple toolset to aligning sLLM with service LLM with coverage dataset 👉🏻 (https://huggingface.co/posts/chansung/708646454991943).
- coverage dataset is what we believe to be the most important/desired (instruction, response) pairs. In system thinking, each instruction could be an analogy of a function from traditional programming. We make unit tests and measure the coverage % for all the features/functions. Similarly, we need to ensure if our fine-tuned model could handle what % of given instructions from coverage dataset satisfactory (hence coverage dataset).
We have tested it with "Coding" category of data from HuggingFaceH4/no_robots dataset. It has about 300 SFT training data points under Coding category. After fine-tuning Gemma 7B model on that, the result was very poor. LLaMA Duo's evaluation tool gave < 20% of metrics in similarity and preciseness on the test split.
So, we used LLaMA Duo's synthetic data generation tool to generate 60k data points that looks similar to the original dataset. We first created ~10k synthetic data points, then created 50k more based on the synthetic dataset itself.
After fine-tuning Gemma 7B on the 60k synthetic dataset, the evaluation result went up to 80~90% high. Also, when testing out the model in UI, it tends to give good responses.
It is a good showcase to transition from service LLM to sLLM or having a backup sLLM for service LLM failure scenarios. I am going to expand this experiments on all categories of no_robots dataset. It will roughly generate > 100k data points.
Here are some links:
- LLaMA Duo project repo: https://github.com/deep-diver/llamaduo
- 60k Coding synthetic dataset: chansung/merged_ds_coding
- Fine-tuned Gemma 7B model: chansung/coding_llamaduo_60k_v0.2
posted
an
update
9 months ago
Post
4019
🦙🦙 LLaMA Duo project update
Last time, I gave a brief introduction about LLaMA Duo project with @sayakpaul . It is a simple toolset to aligning sLLM with service LLM with coverage dataset 👉🏻 (https://huggingface.co/posts/chansung/708646454991943).
- coverage dataset is what we believe to be the most important/desired (instruction, response) pairs. In system thinking, each instruction could be an analogy of a function from traditional programming. We make unit tests and measure the coverage % for all the features/functions. Similarly, we need to ensure if our fine-tuned model could handle what % of given instructions from coverage dataset satisfactory (hence coverage dataset).
We have tested it with "Coding" category of data from HuggingFaceH4/no_robots dataset. It has about 300 SFT training data points under Coding category. After fine-tuning Gemma 7B model on that, the result was very poor. LLaMA Duo's evaluation tool gave < 20% of metrics in similarity and preciseness on the test split.
So, we used LLaMA Duo's synthetic data generation tool to generate 60k data points that looks similar to the original dataset. We first created ~10k synthetic data points, then created 50k more based on the synthetic dataset itself.
After fine-tuning Gemma 7B on the 60k synthetic dataset, the evaluation result went up to 80~90% high. Also, when testing out the model in UI, it tends to give good responses.
It is a good showcase to transition from service LLM to sLLM or having a backup sLLM for service LLM failure scenarios. I am going to expand this experiments on all categories of no_robots dataset. It will roughly generate > 100k data points.
Here are some links:
- LLaMA Duo project repo: https://github.com/deep-diver/llamaduo
- 60k Coding synthetic dataset: chansung/merged_ds_coding
- Fine-tuned Gemma 7B model: chansung/coding_llamaduo_60k_v0.2
Last time, I gave a brief introduction about LLaMA Duo project with @sayakpaul . It is a simple toolset to aligning sLLM with service LLM with coverage dataset 👉🏻 (https://huggingface.co/posts/chansung/708646454991943).
- coverage dataset is what we believe to be the most important/desired (instruction, response) pairs. In system thinking, each instruction could be an analogy of a function from traditional programming. We make unit tests and measure the coverage % for all the features/functions. Similarly, we need to ensure if our fine-tuned model could handle what % of given instructions from coverage dataset satisfactory (hence coverage dataset).
We have tested it with "Coding" category of data from HuggingFaceH4/no_robots dataset. It has about 300 SFT training data points under Coding category. After fine-tuning Gemma 7B model on that, the result was very poor. LLaMA Duo's evaluation tool gave < 20% of metrics in similarity and preciseness on the test split.
So, we used LLaMA Duo's synthetic data generation tool to generate 60k data points that looks similar to the original dataset. We first created ~10k synthetic data points, then created 50k more based on the synthetic dataset itself.
After fine-tuning Gemma 7B on the 60k synthetic dataset, the evaluation result went up to 80~90% high. Also, when testing out the model in UI, it tends to give good responses.
It is a good showcase to transition from service LLM to sLLM or having a backup sLLM for service LLM failure scenarios. I am going to expand this experiments on all categories of no_robots dataset. It will roughly generate > 100k data points.
Here are some links:
- LLaMA Duo project repo: https://github.com/deep-diver/llamaduo
- 60k Coding synthetic dataset: chansung/merged_ds_coding
- Fine-tuned Gemma 7B model: chansung/coding_llamaduo_60k_v0.2
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10 months ago
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💻 Smoothing the Transition from Service LLM to Local LLM
Imagine your go-to LLM service is down, or you need to use it offline – yikes! This project is all about having that "Plan B" ready to go. Here's LLaMA Duo I've been building with @sayakpaul :
✨ Fine-tune a smaller LLM: We used Hugging Face's alignment-handbook to teach a smaller-sized LLM to mimic my favorite large language model. Think of it as that super-smart AI assistant getting a capable understudy.
🤖 Batch Inference: Let's get that fine-tuned LLM working! My scripts generate lots of text like a champ, and we've made sure things run smoothly even with bigger workloads.
🧐 Evaluation: How well is my small LLM doing? We integrated with the Gemini API to use it as an expert judge – it compares my model's work to the original. Talk about a tough critic!
🪄 Synthetic Data Generation: Need to boost that model's performance? Using Gemini's feedback, we can create even more training data, custom-made to make the LLM better.
🧱 Building Blocks: This isn't just a one-time thing – it's a toolkit for all kinds of LLMOps work. Want to change your evaluation metrics? Bring in models trained differently? Absolutely, let's make it happen.
Why this project is awesome:
💪 Reliability: Keep things running no matter what happens to your main LLM source.
🔒 Privacy: Process sensitive information on your own terms.
🗺️ Offline capable: No internet connection? No problem!
🕰️ Version Control: Lock in your favorite LLM's behavior, even if the service model changes.
We'm excited to share the code on GitHub. Curious to see what you all think! 👉🏻 https://github.com/deep-diver/llamaduo
Imagine your go-to LLM service is down, or you need to use it offline – yikes! This project is all about having that "Plan B" ready to go. Here's LLaMA Duo I've been building with @sayakpaul :
✨ Fine-tune a smaller LLM: We used Hugging Face's alignment-handbook to teach a smaller-sized LLM to mimic my favorite large language model. Think of it as that super-smart AI assistant getting a capable understudy.
🤖 Batch Inference: Let's get that fine-tuned LLM working! My scripts generate lots of text like a champ, and we've made sure things run smoothly even with bigger workloads.
🧐 Evaluation: How well is my small LLM doing? We integrated with the Gemini API to use it as an expert judge – it compares my model's work to the original. Talk about a tough critic!
🪄 Synthetic Data Generation: Need to boost that model's performance? Using Gemini's feedback, we can create even more training data, custom-made to make the LLM better.
🧱 Building Blocks: This isn't just a one-time thing – it's a toolkit for all kinds of LLMOps work. Want to change your evaluation metrics? Bring in models trained differently? Absolutely, let's make it happen.
Why this project is awesome:
💪 Reliability: Keep things running no matter what happens to your main LLM source.
🔒 Privacy: Process sensitive information on your own terms.
🗺️ Offline capable: No internet connection? No problem!
🕰️ Version Control: Lock in your favorite LLM's behavior, even if the service model changes.
We'm excited to share the code on GitHub. Curious to see what you all think! 👉🏻 https://github.com/deep-diver/llamaduo
posted
an
update
10 months ago
Post
4408
💻 Smoothing the Transition from Service LLM to Local LLM
Imagine your go-to LLM service is down, or you need to use it offline – yikes! This project is all about having that "Plan B" ready to go. Here's LLaMA Duo I've been building with @sayakpaul :
✨ Fine-tune a smaller LLM: We used Hugging Face's alignment-handbook to teach a smaller-sized LLM to mimic my favorite large language model. Think of it as that super-smart AI assistant getting a capable understudy.
🤖 Batch Inference: Let's get that fine-tuned LLM working! My scripts generate lots of text like a champ, and we've made sure things run smoothly even with bigger workloads.
🧐 Evaluation: How well is my small LLM doing? We integrated with the Gemini API to use it as an expert judge – it compares my model's work to the original. Talk about a tough critic!
🪄 Synthetic Data Generation: Need to boost that model's performance? Using Gemini's feedback, we can create even more training data, custom-made to make the LLM better.
🧱 Building Blocks: This isn't just a one-time thing – it's a toolkit for all kinds of LLMOps work. Want to change your evaluation metrics? Bring in models trained differently? Absolutely, let's make it happen.
Why this project is awesome:
💪 Reliability: Keep things running no matter what happens to your main LLM source.
🔒 Privacy: Process sensitive information on your own terms.
🗺️ Offline capable: No internet connection? No problem!
🕰️ Version Control: Lock in your favorite LLM's behavior, even if the service model changes.
We'm excited to share the code on GitHub. Curious to see what you all think! 👉🏻 https://github.com/deep-diver/llamaduo
Imagine your go-to LLM service is down, or you need to use it offline – yikes! This project is all about having that "Plan B" ready to go. Here's LLaMA Duo I've been building with @sayakpaul :
✨ Fine-tune a smaller LLM: We used Hugging Face's alignment-handbook to teach a smaller-sized LLM to mimic my favorite large language model. Think of it as that super-smart AI assistant getting a capable understudy.
🤖 Batch Inference: Let's get that fine-tuned LLM working! My scripts generate lots of text like a champ, and we've made sure things run smoothly even with bigger workloads.
🧐 Evaluation: How well is my small LLM doing? We integrated with the Gemini API to use it as an expert judge – it compares my model's work to the original. Talk about a tough critic!
🪄 Synthetic Data Generation: Need to boost that model's performance? Using Gemini's feedback, we can create even more training data, custom-made to make the LLM better.
🧱 Building Blocks: This isn't just a one-time thing – it's a toolkit for all kinds of LLMOps work. Want to change your evaluation metrics? Bring in models trained differently? Absolutely, let's make it happen.
Why this project is awesome:
💪 Reliability: Keep things running no matter what happens to your main LLM source.
🔒 Privacy: Process sensitive information on your own terms.
🗺️ Offline capable: No internet connection? No problem!
🕰️ Version Control: Lock in your favorite LLM's behavior, even if the service model changes.
We'm excited to share the code on GitHub. Curious to see what you all think! 👉🏻 https://github.com/deep-diver/llamaduo
replied to
their
post
11 months ago
awesome! going to add one more env var to switch mode then :)
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11 months ago
Post
2539
Realize LLM powered idea on Hugging Face Space.
I made Space for you to duplicate, then it comes with Gradio and LLM served by Hugging Face's efficient Text Generation Inference(TGI) framework packed into a single machine.
It provides a sample app code snippet with
Have you ever enjoyed playing with Hugging Chat? Then, you will enjoy writing your own idea with this. Because both are built on top of TGI!
Focus on your app code, and go beyond chat!
chansung/gradio_together_tgi
I made Space for you to duplicate, then it comes with Gradio and LLM served by Hugging Face's efficient Text Generation Inference(TGI) framework packed into a single machine.
It provides a sample app code snippet with
gr.ChatInterface. However, it is not limited to chat usage, but you can leverage the efficiency of TGI for any sort of apps built in Gradio. Have you ever enjoyed playing with Hugging Chat? Then, you will enjoy writing your own idea with this. Because both are built on top of TGI!
Focus on your app code, and go beyond chat!
chansung/gradio_together_tgi
-
- 2 replies