Z1: Efficient Test-time Scaling with Code
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2504.00810
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armanc
authored
3
papers
4 months ago
emre
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4 months ago
Post
3582
having trouble with auto train
hello there this is the first time i am testing auto train with a 1.8k SFT dataset. Howevery i am not quite sure the training is going smooth. Logs seem quite confusing, token did not match can not auth, generates confusing train splits, do you know how i can check my running job properly?
what is being used for training as data?
any ideas?
hello there this is the first time i am testing auto train with a 1.8k SFT dataset. Howevery i am not quite sure the training is going smooth. Logs seem quite confusing, token did not match can not auth, generates confusing train splits, do you know how i can check my running job properly?
what is being used for training as data?
any ideas?
- 1 reply
armanc
authored
a
paper
5 months ago
armanc
authored
a
paper
6 months ago
armanc
authored
2
papers
7 months ago
Post
1320
Llama 3.1 405B Instruct beats GPT-4o on MixEval-Hard
Just ran MixEval for 405B, Sonnet-3.5 and 4o, with 405B landing right between the other two at 66.19
The GPT-4o result of 64.7 replicated locally but Sonnet-3.5 actually scored 70.25/69.45 in my replications 🤔 Still well ahead of the other 2 though.
Sammple of 1 of the eval calls here: https://wandb.ai/morgan/MixEval/weave/calls/07b05ae2-2ef5-4525-98a6-c59963b76fe1
Quick auto-logging tracing for openai-compatible clients and many more here: https://wandb.github.io/weave/quickstart/
Just ran MixEval for 405B, Sonnet-3.5 and 4o, with 405B landing right between the other two at 66.19
The GPT-4o result of 64.7 replicated locally but Sonnet-3.5 actually scored 70.25/69.45 in my replications 🤔 Still well ahead of the other 2 though.
Sammple of 1 of the eval calls here: https://wandb.ai/morgan/MixEval/weave/calls/07b05ae2-2ef5-4525-98a6-c59963b76fe1
Quick auto-logging tracing for openai-compatible clients and many more here: https://wandb.github.io/weave/quickstart/
Manel-Hik
authored
a
paper
about 1 year ago
armanc
authored
a
paper
over 1 year ago
tmabraham
authored
a
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over 1 year ago
taisazero
authored
a
paper
over 1 year ago
Post
Fine-tuning LLMs is rad, but how do you manage all your checkpoints and evals in a production setting?
We partnered with @hamel to ship an Enterprise Model Management course packed full of learnings for those training, evaluating and deploying models at work.
Topics include:
- What webhooks are & how to use them to create integrations with different tools
- How to automate train -> eval runs
- Improving model governance and documentation
- Comparing candidate and baseline models
- Design patterns & recipes
- Lots more...
Would love to hear what you think!
👉 https://www.wandb.courses/courses/enterprise-model-management
We partnered with @hamel to ship an Enterprise Model Management course packed full of learnings for those training, evaluating and deploying models at work.
Topics include:
- What webhooks are & how to use them to create integrations with different tools
- How to automate train -> eval runs
- Improving model governance and documentation
- Comparing candidate and baseline models
- Design patterns & recipes
- Lots more...
Would love to hear what you think!
👉 https://www.wandb.courses/courses/enterprise-model-management
armanc
authored
a
paper
over 1 year ago
Post
Delighted to share a course I've learned a ton from about getting better outputs from LLMs
https://www.wandb.courses/courses/steering-language-models
We released it last Thursday (free) and at just 30 minutes of content total, its very information-dense with non-stop learnings covering important concepts around LLM validation, making your approach to LLM prompting more pythonic and quickly covers a basic RAG application at the end.
Would love to hear what ye think!
https://www.wandb.courses/courses/steering-language-models
We released it last Thursday (free) and at just 30 minutes of content total, its very information-dense with non-stop learnings covering important concepts around LLM validation, making your approach to LLM prompting more pythonic and quickly covers a basic RAG application at the end.
Would love to hear what ye think!
tmabraham
authored
a
paper
over 1 year ago
armanc
authored
a
paper
over 1 year ago
armanc
authored
a
paper
almost 2 years ago