OLMo-2-1124-7B-Instruct

High-performance English dialogue generation model

CommonProductchattingNatural Language ProcessingDialogue Generation

OLMo-2-1124-7B-Instruct is a large language model developed by the Allen Institute for AI, focusing on dialogue generation tasks. This model has been optimized for various tasks including mathematical problem-solving, GSM8K, IFEval, and has undergone supervised fine-tuning on the Tülu 3 dataset. It is built on the Transformers library and can be used for research and educational purposes. The main advantages of the model include high performance, multi-task adaptability, and being open-source, making it an essential tool in the realm of natural language processing.

? Trained on the Dolma dataset
providing code
checkpoints
and training details.
? Supports various tasks
including chatting and mathematical problem-solving.
? Enhanced performance and adaptability through supervised fine-tuning and DPO training.
? Easily integrable with the Hugging Face platform for convenient loading and usage.
? Offers chat templates to streamline the dialogue generation process.
? The model has limited safety training but can produce diverse outputs.

The target audience includes researchers
developers
and educators in the field of natural language processing (NLP). This model is well-suited for them as it provides a powerful tool to explore and implement the science of language modeling
particularly in dialogue generation and multi-task learning.

Researchers utilize the model to investigate the behavior and performance of dialogue systems.
Developers leverage the model to create chatbots and customer service assistants.
Educators employ the model in classrooms to teach the fundamentals of natural language processing.

1. Install the latest version of the Transformers library: use pip to install it.
2. Load the model: utilize the code snippets provided by Hugging Face to load the model.
3. Use conversation templates: create dialogues following the provided format.
4. Fine-tune the model: adjust the model for specific tasks.
5. Evaluate model performance: use the provided evaluation tools and datasets.

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OLMo-2-1124-7B-Instruct

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Llama-3.1-70B-Instruct-AWQ-INT4 — Text generation model with 70 billion parameters

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CosyVoice Speech Generation Model 2.0-0.5B — Efficient, multilingual speech synthesis model

tulu-3-sft-olmo-2-mixture — A large-scale multilingual text dataset.

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ChatGPT Pro — Scalable access plan for cutting-edge AI models.

PaliGemma 2 — PaliGemma 2 is a powerful visual language model that is easy to fine-tune.

OLMo 2 1124 7B Preference Mixture — A large-scale textual dataset for preference mixture research.

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

OLMo-2-1124-7B-DPO — An advanced text generation model supporting diverse task handling.

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

ProactiveAgent — A proactive agent based on large language models that predicts user needs and offers assistance.

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

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