Qwen2.5-Coder Technical Report
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2409.12186
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Published
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136
Nick Emb PRO
nicoism
AI & ML interests
Agents, Coding Generation, Governance, Web3
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Qwen2.5-Coder Technical Report
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This is no Woodstock AI but will be fun nonetheless haha. I’ll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM
1,000 spots available first-come first serve with some surprises during the stream!
You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM
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✂️ Uncensor any LLM with abliteration
I wrote an article about abliteration and how NeuralDaredevil-8B was created. Beyond removing alignment, I believe it's an interesting technique with a lot of potential. It's basically fine-tuning without retraining.
In this article, we see how it works, implement it in Google Colab, and heal the abliterated model to recover the performance drop due to this technique. The final model is an uncensored and high-quality model with the highest MMLU score on the Open LLM Leaderboard (8B category).
https://huggingface.co/blog/mlabonne/abliteration
I wrote an article about abliteration and how NeuralDaredevil-8B was created. Beyond removing alignment, I believe it's an interesting technique with a lot of potential. It's basically fine-tuning without retraining.
In this article, we see how it works, implement it in Google Colab, and heal the abliterated model to recover the performance drop due to this technique. The final model is an uncensored and high-quality model with the highest MMLU score on the Open LLM Leaderboard (8B category).
https://huggingface.co/blog/mlabonne/abliteration
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Here is my latest study on OpenAI🍓o1🍓.
A Case Study of Web App Coding with OpenAI Reasoning Models (2409.13773)
I wrote an easy-to-read blogpost to explain finding.
https://huggingface.co/blog/onekq/daily-software-engineering-work-reasoning-models
INSTRUCTION FOLLOWING is the key.
100% instruction following + Reasoning = new SOTA
But if the model misses or misunderstands one instruction, it can perform far worse than non-reasoning models.
A Case Study of Web App Coding with OpenAI Reasoning Models (2409.13773)
I wrote an easy-to-read blogpost to explain finding.
https://huggingface.co/blog/onekq/daily-software-engineering-work-reasoning-models
INSTRUCTION FOLLOWING is the key.
100% instruction following + Reasoning = new SOTA
But if the model misses or misunderstands one instruction, it can perform far worse than non-reasoning models.
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Transformers.js help
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