Faster Text Generation with Self-Speculative Decoding
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Aritra Roy Gosthipaty
ariG23498
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Deep Representation Learning
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ariG23498's activity
reacted to
thomwolf's
post with ๐ฅ
about 17 hours ago
Post
1140
Very exciting new
mistralai/Pixtral-Large-Instruct-2411 model from Mistral-AI
Impressive performances, huge congrats @patrickvonplaten @sgvaze @pandora-s @devendrachaplot @sophiamyang and team!
Very nice to have SOTA Multilingual OCR and Chart understanding in an open-weights model
Impressive performances, huge congrats @patrickvonplaten @sgvaze @pandora-s @devendrachaplot @sophiamyang and team!
Very nice to have SOTA Multilingual OCR and Chart understanding in an open-weights model
posted
an
update
9 days ago
reacted to
m-ric's
post with ๐
28 days ago
Post
1936
๐๐ Cohere releases Aya 8B & 32B: SOTA multilingual models for 23 languages !
How did they manage to beat top contenders while also adding 23 languages?
๐ ๐ง๐ฟ๐ฎ๐ถ๐ป ๐ผ๐ป ๐๐๐ป๐๐ต๐ฒ๐๐ถ๐ฐ ๐ฑ๐ฎ๐๐ฎ:
โข Synthetic data has been said to cause model-collapse after too much training
โข Cohere has introduced "data arbitrage" to prevent this by strategically sampling from a pool of several teacher models instead of one single teacher
โข First train a model pool for each different groups of languages, and employ an internal Reward Model named "Arbiter" to evaluate and select the optimal generation. Then only the best generation is kept as the final completion for each prompt
โก๏ธ This process is particularly effective for multilingual setting, where no single teacher model performs in all languages : here "Multilingual Arbitrage" singlehandedly improves win rates of the 8B model vs Gemma-2-9B by 10 points!
๐งฉ ๐จ๐๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น ๐บ๐ฒ๐ฟ๐ด๐ถ๐ป๐ด: Rather than struggling to find the right mix of data in training a single model for multilingual use, just train language specific models then merge them!
โข Maximize diversity between merged checkpoints by training each on different language families.
โข Experimented fancy techniques (SLERP, TIES, DARE-TIES) but found out weighted averaging to be the most consistent!
โก๏ธ Merging had 3x more gains at high 35B scale vs the 8B scale - consistent with literature findings that merging is more effective at scale
โก๏ธ ๐๐ฟ๐ฒ๐ฎ๐ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ: Automatic evaluations on Arena-Hard-Auto dataset:
โก๏ธ Aya Expanse 8B beats models from its weight class such as Gemma 2 9B, Llama 3.1 8B, and the recent Ministral 8B, with win rates ranging from 60.4% to 70.6%
โก๏ธ Aya Expanse 32B outperforms Gemma 2 27B, Mistral 8x22B, and Llama 3.1 70B (2x its size)
โข โ ๏ธ But this performance eval comes from only one benchmark! Let's wait for Open LLM leaderboard evals;
๐ CC by NC license
Blog post here: https://huggingface.co/blog/aya-expanse
How did they manage to beat top contenders while also adding 23 languages?
๐ ๐ง๐ฟ๐ฎ๐ถ๐ป ๐ผ๐ป ๐๐๐ป๐๐ต๐ฒ๐๐ถ๐ฐ ๐ฑ๐ฎ๐๐ฎ:
โข Synthetic data has been said to cause model-collapse after too much training
โข Cohere has introduced "data arbitrage" to prevent this by strategically sampling from a pool of several teacher models instead of one single teacher
โข First train a model pool for each different groups of languages, and employ an internal Reward Model named "Arbiter" to evaluate and select the optimal generation. Then only the best generation is kept as the final completion for each prompt
โก๏ธ This process is particularly effective for multilingual setting, where no single teacher model performs in all languages : here "Multilingual Arbitrage" singlehandedly improves win rates of the 8B model vs Gemma-2-9B by 10 points!
๐งฉ ๐จ๐๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น ๐บ๐ฒ๐ฟ๐ด๐ถ๐ป๐ด: Rather than struggling to find the right mix of data in training a single model for multilingual use, just train language specific models then merge them!
โข Maximize diversity between merged checkpoints by training each on different language families.
โข Experimented fancy techniques (SLERP, TIES, DARE-TIES) but found out weighted averaging to be the most consistent!
โก๏ธ Merging had 3x more gains at high 35B scale vs the 8B scale - consistent with literature findings that merging is more effective at scale
โก๏ธ ๐๐ฟ๐ฒ๐ฎ๐ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ: Automatic evaluations on Arena-Hard-Auto dataset:
โก๏ธ Aya Expanse 8B beats models from its weight class such as Gemma 2 9B, Llama 3.1 8B, and the recent Ministral 8B, with win rates ranging from 60.4% to 70.6%
โก๏ธ Aya Expanse 32B outperforms Gemma 2 27B, Mistral 8x22B, and Llama 3.1 70B (2x its size)
โข โ ๏ธ But this performance eval comes from only one benchmark! Let's wait for Open LLM leaderboard evals;
๐ CC by NC license
Blog post here: https://huggingface.co/blog/aya-expanse
posted
an
update
28 days ago
Post
1497
Cohere drops two new multilingual models!
CohereForAI/aya-expanse-8b
CohereForAI/aya-expanse-32b
Try them out here
CohereForAI/aya_expanse
CohereForAI/aya-expanse-8b
CohereForAI/aya-expanse-32b
Try them out here
CohereForAI/aya_expanse
reacted to
reach-vb's
post with ๐ฅ
about 1 month ago
Post
5361
Multimodal Ichigo Llama 3.1 - Real Time Voice AI ๐ฅ
> WhisperSpeech X Llama 3.1 8B
> Trained on 50K hours of speech (7 languages)
> Continually trained on 45hrs 10x A1000s
> MLS -> WhisperVQ tokens -> Llama 3.1
> Instruction tuned on 1.89M samples
> 70% speech, 20% transcription, 10% text
> Apache 2.0 licensed โก
Architecture:
> WhisperSpeech/ VQ for Semantic Tokens
> Llama 3.1 8B Instruct for Text backbone
> Early fusion (Chameleon)
I'm super bullish on HomeBrew/ Jan and early fusion, audio and text, multimodal models!
(P.S. Play with the demo on Hugging Face: jan-hq/Ichigo-llama3.1-s-instruct)
> WhisperSpeech X Llama 3.1 8B
> Trained on 50K hours of speech (7 languages)
> Continually trained on 45hrs 10x A1000s
> MLS -> WhisperVQ tokens -> Llama 3.1
> Instruction tuned on 1.89M samples
> 70% speech, 20% transcription, 10% text
> Apache 2.0 licensed โก
Architecture:
> WhisperSpeech/ VQ for Semantic Tokens
> Llama 3.1 8B Instruct for Text backbone
> Early fusion (Chameleon)
I'm super bullish on HomeBrew/ Jan and early fusion, audio and text, multimodal models!
(P.S. Play with the demo on Hugging Face: jan-hq/Ichigo-llama3.1-s-instruct)
reacted to
merve's
post with ๐ค
3 months ago
Post
2259
amazing leaderboard by
@rwightman
, compare all the image backbones on various metrics against model performance
below is an example for top-k against inferred samples per second
timm/leaderboard
below is an example for top-k against inferred samples per second
timm/leaderboard
reacted to
joaogante's
post with ๐ค
3 months ago
Post
2785
New sampling strategy dropped in ๐ค transformers -- Min P sampling ๐ฅ
Are you tired of having
Min P consists of a dynamic token filter -- as opposed to Top K, which keeps the K most likely tokens, and Top P, which keeps the most likely tokens up to a fixed cumulative probability, both static filters. Min P takes a base probability (defined in the
๐ High probability token present -> aggressive filter (we don't want to miss on that high-probability case and risk derailing generation)
๐ No high probability token present -> relaxed filter (there are many continuation possibilities that the model finds plausible)
You should set
Kudos to @kalomaze and @menhguin for creating this technique ๐ฅ Read their discussion in the original issue for benchmarks (https://github.com/huggingface/transformers/issues/27670)
Copy-pasteable version of the example in the image below here: https://pastebin.com/VqXNtuxd
Have fun experimenting! ๐
Are you tired of having
top_k arbitrarily discarding high-quality continuations? Or top_p forgetting to exclude low-probability tokens, derailing your generation? Try out the new min_p flag in generate, fresh from a PR merged today! ๐ฅฌMin P consists of a dynamic token filter -- as opposed to Top K, which keeps the K most likely tokens, and Top P, which keeps the most likely tokens up to a fixed cumulative probability, both static filters. Min P takes a base probability (defined in the
min_p flag) and multiplies it by the probability of the most likely token in the distribution for the next token. All tokens less likely than the resulting value are filtered. What happens with this strategy?๐ High probability token present -> aggressive filter (we don't want to miss on that high-probability case and risk derailing generation)
๐ No high probability token present -> relaxed filter (there are many continuation possibilities that the model finds plausible)
You should set
min_p to a low value, between 0.05 and 0.1. It behaves particularly well for creative text generation when paired up with temperature > 1.Kudos to @kalomaze and @menhguin for creating this technique ๐ฅ Read their discussion in the original issue for benchmarks (https://github.com/huggingface/transformers/issues/27670)
Copy-pasteable version of the example in the image below here: https://pastebin.com/VqXNtuxd
Have fun experimenting! ๐
posted
an
update
3 months ago
Post
1606
You can now use DoRA for your embedding layers!
PR: https://github.com/huggingface/peft/pull/2006
I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.
Blog Post: https://huggingface.co/blog/ariG23498/peft-dora
Huge thanks to @BenjaminB for all the help I needed.
PR: https://github.com/huggingface/peft/pull/2006
I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.
Blog Post: https://huggingface.co/blog/ariG23498/peft-dora
Huge thanks to @BenjaminB for all the help I needed.
