AfriqueLLM
Collection
Best open African LLM
ā¢
6 items
ā¢
Updated
ā¢
21
Oyerinde Samuel
Sam4rano
AI & ML interests
NLP || AI for good || Software Engineering
Recent Activity
upvoted
a
collection
about 2 months ago
AfriqueLLM
updated
a model
5 months ago
Sam4rano/nollysenti_afro-xlmr-base
published
a model
5 months ago
Sam4rano/nollysenti_afro-xlmr-base
Organizations
upvoted
a
collection
about 2 months ago
updated
a
model
5 months ago
published
a
model
5 months ago
updated
a
model
5 months ago
published
a
model
5 months ago
liked
a
model
6 months ago
updated
a
model
6 months ago
published
a
model
6 months ago
updated
a
model
6 months ago
published
a
model
6 months ago
updated
a
model
6 months ago
published
a
model
6 months ago
updated
a
model
6 months ago
published
a
model
6 months ago
reacted to
codelion's
post with ā¤ļø
9 months ago
Post
3531
š§ We just implemented Andrej Karpathy's "third paradigm" for LLM learning!
System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.
š How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.
š Results across math benchmarks:
Arena Hard: 29% ā 37.6% (+8.6%)
AIME24: 23.33% ā 30% (+6.67%)
OptILLMBench: 61% ā 65% (+4%)
The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.
āØ Key benefits:
š Cumulative learning over time
š Transparent, inspectable strategies
š Works with any OpenAI-compatible API
ā” Simple integration: just add "spl-" prefix to your model
Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!
This feels like a genuine step toward AI that learns from experience while staying completely interpretable.
š GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
š Full article: https://huggingface.co/blog/codelion/system-prompt-learning
š¦ Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486
Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?
System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.
š How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.
š Results across math benchmarks:
Arena Hard: 29% ā 37.6% (+8.6%)
AIME24: 23.33% ā 30% (+6.67%)
OptILLMBench: 61% ā 65% (+4%)
The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.
āØ Key benefits:
š Cumulative learning over time
š Transparent, inspectable strategies
š Works with any OpenAI-compatible API
ā” Simple integration: just add "spl-" prefix to your model
Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!
This feels like a genuine step toward AI that learns from experience while staying completely interpretable.
š GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
š Full article: https://huggingface.co/blog/codelion/system-prompt-learning
š¦ Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486
Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?
authored
a
paper
over 1 year ago