ajibawa-2023/PHP-Code-Large
Updated
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Feynman Innovations
ajibawa-2023
AI & ML interests
LLM, RL, DL, ML, AGI. Developing LLMs (preferably fully fine tuned ) for various use cases.
Recent Activity
updated
a dataset about 7 hours ago
ajibawa-2023/PHP-Code-Large liked
a dataset about 19 hours ago
ajibawa-2023/PHP-Code-Large published
a dataset about 20 hours ago
ajibawa-2023/PHP-Code-Large Organizations
updated a dataset about 7 hours ago
liked a dataset about 19 hours ago
published a dataset about 20 hours ago
reacted to their post with 👍🚀🔥 5 days ago
Post
3175
JavaScript-Code-Large
ajibawa-2023/JavaScript-Code-Large
JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.
By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks.
JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. .
ajibawa-2023/JavaScript-Code-Large
JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.
By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks.
JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. .
posted an
update 5 days ago
Post
3175
JavaScript-Code-Large
ajibawa-2023/JavaScript-Code-Large
JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.
By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks.
JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. .
ajibawa-2023/JavaScript-Code-Large
JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.
By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks.
JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. .
liked a dataset 5 days ago
updated a dataset 5 days ago
published a dataset 5 days ago
reacted to DavidAU's post with 🚀 6 days ago
Post
5328
Gemma 3 (1b, 4b, 12b and 27b) - Uncensored full Reasoning/Thinking models fine tuned using top distill datasets.
20 Gemma 3 models 1B, 4B, 12B and 27B with full reasoning using GLM 4.7 Flash, GPT, Claude and Gemini datasets and more fully fine tuned using Unsloth.
Most models are Heretic'ed (uncensored) first, and tuned second.
This vastly improves the model.
Models are also bench marked and in almost all cases exceed org model metrics - and in some cases by a lot.
Enjoy the freedom and more powerful THINKING/REASONING and UNCENSORED Gemma 3s !
https://huggingface.co/collections/DavidAU/gemma-3-reasoning-thinking-models-incl-uncensored
UPDATE: Benchmarks added for almost all models, including "VS" with Heretic (untuned) source models too.
20 Gemma 3 models 1B, 4B, 12B and 27B with full reasoning using GLM 4.7 Flash, GPT, Claude and Gemini datasets and more fully fine tuned using Unsloth.
Most models are Heretic'ed (uncensored) first, and tuned second.
This vastly improves the model.
Models are also bench marked and in almost all cases exceed org model metrics - and in some cases by a lot.
Enjoy the freedom and more powerful THINKING/REASONING and UNCENSORED Gemma 3s !
https://huggingface.co/collections/DavidAU/gemma-3-reasoning-thinking-models-incl-uncensored
UPDATE: Benchmarks added for almost all models, including "VS" with Heretic (untuned) source models too.
reacted to their post with 🚀👍 6 days ago
Post
3106
Java-Code-Large ( ajibawa-2023/Java-Code-Large)
Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.
Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.
posted an
update 6 days ago
Post
3106
Java-Code-Large ( ajibawa-2023/Java-Code-Large)
Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.
Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.
Advice for OpenCL
1
#3 opened 7 days ago
by
TylerHilbert
liked a dataset 7 days ago
updated a dataset 7 days ago
published a dataset 7 days ago
liked a dataset 8 days ago
reacted to codelion's post with 🔥 about 1 month ago
Post
3147
Reverse Engineering a $500M Mystery: From HashHop to Memory-Augmented Language Models
I wrote a deep dive into how Magic AI's 100M token context window might work, starting from their HashHop benchmark and building up to MALM - a Memory-Augmented Language Model.
Key insight: treating each key as a single token enables perfect retrieval at unlimited context lengths.
The article covers:
- How HashHop works and why its perfect accuracy is suspicious
- Building a tokenized solver that achieves 100% accuracy
- Scaling to MALM for real code search tasks
- Why this approach could handle 100M+ tokens
Read the full article: https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop
Try the model: codelion/malm-165m
Code: https://github.com/codelion/hash-hop
I wrote a deep dive into how Magic AI's 100M token context window might work, starting from their HashHop benchmark and building up to MALM - a Memory-Augmented Language Model.
Key insight: treating each key as a single token enables perfect retrieval at unlimited context lengths.
The article covers:
- How HashHop works and why its perfect accuracy is suspicious
- Building a tokenized solver that achieves 100% accuracy
- Scaling to MALM for real code search tasks
- Why this approach could handle 100M+ tokens
Read the full article: https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop
Try the model: codelion/malm-165m
Code: https://github.com/codelion/hash-hop
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