OLMo-2-1124-7B-DPO

An advanced text generation model supporting diverse task handling.

CommonProductProductivityText GenerationNatural Language Processing

OLMo-2-1124-7B-DPO is a large language model developed by the Allen Institute for Artificial Intelligence, which has been fine-tuned through supervised training on specific datasets, followed by DPO training. The model is designed to deliver high performance across a variety of tasks, including chat, solving mathematical problems, and text generation. It is built on the Transformers library, supports PyTorch, and is licensed under the Apache 2.0 license.

- Supports text generation: Capable of producing coherent and relevant text content.
- Diverse task handling: Beyond chat
it can also tackle mathematical problems
GSM8K
IFEval
and more.
- Fine-tuning capabilities: Enhanced performance on specific tasks through fine-tuning on particular datasets.
- PyTorch support: Facilitates integration with existing PyTorch projects.
- Adheres to Apache 2.0 license: Allows free use for research and educational purposes.
- Performance data: Provides detailed performance metrics to help users understand the model’s capabilities across various tasks.

\The target audience includes researchers
developers
and educators who require a high-performance model capable of handling complex language tasks. OLMo-2-1124-7B-DPO
with its robust text generation capabilities and multi-tasking abilities
is particularly suitable for users engaged in natural language processing and machine learning research.\

Example 1: Researchers use the OLMo-2-1124-7B-DPO model to develop a chatbot that delivers more natural and accurate conversations.
Example 2: Educational institutions leverage this model to generate teaching materials
such as math problems and solutions
to aid instruction.
Example 3: Developers integrate the model into their applications to provide automated content generation

1. Install the Transformers library: Use pip to install the latest version of the Transformers library.
2. Load the model: Load the OLMo-2-1124-7B-DPO model using the code snippet provided by Hugging Face.
3. Use the chat template: Enter user and assistant dialogue in the format outlined in the provided chat template.
4. Set system prompts: Configure system prompts as needed to guide the model's behavior.
5. Generate text: Take advantage of the model's text generation capabilities by inputting prompts to receive generated text.

Visit

OLMo-2-1124-7B-DPO Visit Over Time

Monthly Visits

19075321

Bounce Rate

45.07%

Page per Visit

5.5

Visit Duration

00:05:32

OLMo-2-1124-7B-DPO Visit Trend

OLMo-2-1124-7B-DPO Visit Geography

OLMo-2-1124-7B-DPO Traffic Sources

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OLMo-2-1124-7B-DPO

OLMo-2-1124-7B-DPO Visit Over Time

OLMo-2-1124-7B-DPO Visit Trend

OLMo-2-1124-7B-DPO Visit Geography

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kan-gpt — A pre-trained generative transformer (GPT) language model implemented using Kolmogorov-Arnold networks (KANs).

OLMo-2-1124-7B-SFT — High-performance English text generation model

OLMo-2-1124-13B-DPO — High-performance English language model suitable for diverse tasks.

dolmino-mix-1124 — A high-quality dataset for the second phase of OLMo2 training.

Llama-3.1-Tulu-3-8B-DPO — An advanced text generation model that supports diverse tasks.

Llama-3.1-Tulu-3-70B — A leading family of instruction-following models, providing open-source data, code, and guidelines.

Qwen Turbo 1M Demo — The Qwen Turbo 1M Demo is a Hugging Face space provided by Qwen.

aya-101 — Multilingual generative language model

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OpenScholar — A retrieval-augmented language model for synthesizing scientific literature.

olmo-mix-1124 — Large-scale multimodal pre-training dataset

OLMo-2-1124-7B-Instruct — High-performance English dialogue generation model

Skywork-o1-Open-PRM-Qwen-2.5-7B — High-performance AI model that enhances reasoning task capabilities.

OLMo 2 — State-of-the-art fully open language model

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ZipPy — A tool for rapid detection of AI-generated text using compression ratios.

LazyGraphRAG — A groundbreaking model setting new standards for quality and cost through enhanced graph-based retrieval and generative technology.

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