MaziyarPanahi/synthetic-medical-conversations-deepseek-v3-chat
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NIONGOLO Chrys Fé-Marty PRO
Svngoku
AI & ML interests
African Culture, LLM, Fine-tuning & Quantization and i love experimenting RAGs & building synthetic datas
Recent Activity
liked
a dataset
about 8 hours ago
MaziyarPanahi/synthetic-medical-conversations-deepseek-v3-chat
new activity
about 11 hours ago
OpenLLM-France/Lucie-7B-Instruct-gguf:Misunderstandings of press about this model
reacted
to
KnutJaegersberg's
post
with 👀
about 13 hours ago
Evolution and The Knightian Blindspot of Machine Learning
The paper discusses machine learning's limitations in addressing Knightian Uncertainty (KU), highlighting the fragility of models like reinforcement learning (RL) in unpredictable, open-world environments. KU refers to uncertainty that can't be quantified or predicted, a challenge that RL fails to handle due to its reliance on fixed data distributions and limited formalisms.
### Key Approaches:
1. **Artificial Life (ALife):** Simulating diverse, evolving systems to generate adaptability, mimicking biological evolution's robustness to unpredictable environments.
2. **Open-Endedness:** Creating AI systems capable of continuous innovation and adaptation, drawing inspiration from human creativity and scientific discovery.
3. **Revising RL Formalisms:** Modifying reinforcement learning (RL) models to handle dynamic, open-world environments by integrating more flexible assumptions and evolutionary strategies.
These approaches aim to address ML’s limitations in real-world uncertainty and move toward more adaptive, general intelligence.
https://arxiv.org/abs/2501.13075
Organizations
Svngoku's activity
liked
a
dataset
about 8 hours ago
Misunderstandings of press about this model
3
#4 opened about 12 hours ago
by
ParisNeo
reacted to
KnutJaegersberg's
post with 👀
about 13 hours ago
Post
1696
Evolution and The Knightian Blindspot of Machine Learning
The paper discusses machine learning's limitations in addressing Knightian Uncertainty (KU), highlighting the fragility of models like reinforcement learning (RL) in unpredictable, open-world environments. KU refers to uncertainty that can't be quantified or predicted, a challenge that RL fails to handle due to its reliance on fixed data distributions and limited formalisms.
### Key Approaches:
1. **Artificial Life (ALife):** Simulating diverse, evolving systems to generate adaptability, mimicking biological evolution's robustness to unpredictable environments.
2. **Open-Endedness:** Creating AI systems capable of continuous innovation and adaptation, drawing inspiration from human creativity and scientific discovery.
3. **Revising RL Formalisms:** Modifying reinforcement learning (RL) models to handle dynamic, open-world environments by integrating more flexible assumptions and evolutionary strategies.
These approaches aim to address ML’s limitations in real-world uncertainty and move toward more adaptive, general intelligence.
https://arxiv.org/abs/2501.13075
The paper discusses machine learning's limitations in addressing Knightian Uncertainty (KU), highlighting the fragility of models like reinforcement learning (RL) in unpredictable, open-world environments. KU refers to uncertainty that can't be quantified or predicted, a challenge that RL fails to handle due to its reliance on fixed data distributions and limited formalisms.
### Key Approaches:
1. **Artificial Life (ALife):** Simulating diverse, evolving systems to generate adaptability, mimicking biological evolution's robustness to unpredictable environments.
2. **Open-Endedness:** Creating AI systems capable of continuous innovation and adaptation, drawing inspiration from human creativity and scientific discovery.
3. **Revising RL Formalisms:** Modifying reinforcement learning (RL) models to handle dynamic, open-world environments by integrating more flexible assumptions and evolutionary strategies.
These approaches aim to address ML’s limitations in real-world uncertainty and move toward more adaptive, general intelligence.
https://arxiv.org/abs/2501.13075
liked
2
models
about 13 hours ago
liked
a
dataset
about 13 hours ago
liked
a
Space
1 day ago
liked
2
models
2 days ago
liked
a
model
3 days ago
upvoted
a
collection
3 days ago
liked
2
models
3 days ago
reacted to
Pendrokar's
post with 🔥🔥
3 days ago
Post
1326
Added
@amphion
MaskGCT &
@hexgrad
StyleTTS fine tuned model by the name of kokoro to the forked TTS Arena Space. If things keep up from what is seen in the preliminary results, then these two may end up in the TOP 5 of all TTS models. 🤞️🍀️
Pendrokar/TTS-Spaces-Arena
Svngoku/maskgct-audio-lab
hexgrad/Kokoro-TTS
I chose @Svngoku 's forked HF space over amphion's due to the overly high ZeroGPU duration demand on the latter. 300s!
amphion/maskgct
Had to remove @mrfakename 's MetaVoice-1B Space from the available models as that space has been down for quite some time. 🤕️
mrfakename/MetaVoice-1B-v0.1
I'm close to syncing the code to the original Arena's code structure. Then I'd like to use ASR in order to validate and create synthetic public datasets from the generated samples. And then make the Arena multilingual, which will surely attract quite the crowd!
Pendrokar/TTS-Spaces-Arena
Svngoku/maskgct-audio-lab
hexgrad/Kokoro-TTS
I chose @Svngoku 's forked HF space over amphion's due to the overly high ZeroGPU duration demand on the latter. 300s!
amphion/maskgct
Had to remove @mrfakename 's MetaVoice-1B Space from the available models as that space has been down for quite some time. 🤕️
mrfakename/MetaVoice-1B-v0.1
I'm close to syncing the code to the original Arena's code structure. Then I'd like to use ASR in order to validate and create synthetic public datasets from the generated samples. And then make the Arena multilingual, which will surely attract quite the crowd!
- 1 reply
liked
a
dataset
3 days ago
liked
a
Space
3 days ago
[bot] Conversion to Parquet
#1 opened 9 months ago
by
parquet-converter
reacted to
lewtun's
post with 🔥
4 days ago
Post
9283
We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open!
🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.
🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.
🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.
Follow along: https://github.com/huggingface/open-r1
🧪 Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.
🧠 Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.
🔥 Step 3: show we can go from base model -> SFT -> RL via multi-stage training.
Follow along: https://github.com/huggingface/open-r1
- 5 replies
liked
a
model
4 days ago
