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Omni Lingual Models

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ajibawa-2023  new activity 13 days ago
aurora-m/redteam:Chaning License to ODC-By
huu-ontocord  new activity 13 days ago
aurora-m/redteam:Chaning License to ODC-By
huu-ontocord  updated a dataset 13 days ago
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ajibawa-2023 
posted an update 5 days ago
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3489
Technical-Architectures-Large
Dataset: ajibawa-2023/Technical-Architectures-Large

This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.

These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
ajibawa-2023 
in aurora-m/redteam 13 days ago

Chaning License to ODC-By

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#2 opened 13 days ago by
huu-ontocord
huu-ontocord 
in aurora-m/redteam 13 days ago

Chaning License to ODC-By

1
#2 opened 13 days ago by
huu-ontocord
huu-ontocord 
updated a dataset 13 days ago
felfri 
authored a paper 21 days ago
PSaiml 
authored a paper 21 days ago
ajibawa-2023 
posted an update about 1 month ago
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6913
Shell-Code-Large
Dataset: ajibawa-2023/Shell-Code-Large

Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.

By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.

Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
felfri 
authored 3 papers about 1 month ago
Locutusque 
posted an update about 2 months ago
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385
🚀 Introducing Esmeralda-Llama-3.1-8B-control
The first release in the Esmeralda model family by Locutusque.

This model is intentionally small and experimental — a control/baseline proof-of-concept designed to answer one question:

«“How strong is my new "Locutusque/esmeralda-agentic" dataset before scaling to larger runs?”»

Training Details

- Base: Llama 3.1 8B
- Training precision: bf16 mixed precision
- Chat template: modified ChatML
- Dataset size: ~37k examples
- Examples actually used for this run: ~5k

The dataset includes:

- multi-turn agentic traces
- reasoning traces
- structured assistant behavior
- generalist instruction data

Benchmark Results

Compared against:

- Llama 3.1 8B Instruct
- Hermes-3-Llama-3.1-8B

HumanEval

57.3 — Esmeralda
56.1 — Llama 3.1 Instruct
52.4 — Hermes-3

MBPP

53.2 — Esmeralda
56.8 — Llama 3.1 Instruct
48.2 — Hermes-3

GPQA Diamond

15.7 — Esmeralda
15.7 — Llama 3.1 Instruct
18.2 — Hermes-3

EQ-Bench

59.2 — Esmeralda
61.1 — Llama 3.1 Instruct
63.1 — Hermes-3

EQ-Bench Parseable (Syntax Stability)

🔥 100.0% — Esmeralda
92.4% — Llama 3.1 Instruct
91.2% — Hermes-3

Here Be Dragons 🐉

I also experimented with a new TruthfulQA free-generation evaluation setup.

- Responses were judged by Gemma 4 26B A4B
- The judge compared generations directly against ground-truth answers
- Models were evaluated in 8-bit quantized form to speed up inference

TruthfulQA (LLM Judge)

0.682 — Esmeralda-Llama-3.1-8B-control
0.587 — Hermes-3-Llama-3.1-8B (reported MC2 score; methodology differs)

For a lightweight control run trained on only a fraction of the dataset, I’m pretty encouraged by the results.

The model is released under the standard Llama 3.1 license, and I’d genuinely love feedback from people testing it in real workflows.

Model: Locutusque/Esmeralda-Llama-3.1-8B-control

Dataset: Locutusque/esmeralda-agentic
chiennv 
authored 3 papers 2 months ago
chiennv 
submitted a paper to Daily Papers 2 months ago
ajibawa-2023 
posted an update 3 months ago
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2231
Stitched-Reasoning-Trajectories-7M

Dataset: ajibawa-2023/Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.

By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate "topic drift," every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5).

The resulting dataset consists of 709 .jsonl files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.
felfri 
authored a paper 3 months ago
huu-ontocord 
authored a paper 3 months ago
ajibawa-2023 
posted an update 3 months ago
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1347
Ruby-Code-Large
Dataset : ajibawa-2023/Ruby-Code-Large

Ruby-Code-Large is a large-scale corpus of Ruby programming language source code comprising 331,743 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, web application development, and software engineering automation within the Ruby ecosystem.

By offering a substantial, language-focused dataset, Ruby-Code-Large enables targeted experimentation in dynamic programming, object-oriented design, and rapid application development—areas where Ruby is widely used, particularly in web frameworks and scripting.

Ruby-Code-Large addresses the lack of large, curated, Ruby-specific datasets, enabling focused research on expressive syntax, metaprogramming, and high-level abstractions.
ajibawa-2023 
posted an update 3 months ago
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6120
Go-Code-Large
Dataset: ajibawa-2023/Go-Code-Large

Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.

By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.

Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.
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