Everythinglm 13B - Details

Everythinglm 13B is a large language model developed by the Totally-Not-An-Llm community, featuring 13b parameters. It operates under the Llama 2 Community License Agreement (LLAMA-2-CLA) and is designed as a Llama 2-based model with an extended 16K context window, making it suitable for complex and lengthy tasks.

Everythinglm 13B is a 13b parameter model based on Llama-2 that leverages LlongMa to achieve a 16k context window, enabling it to handle extended and complex tasks. It is trained on the EverythingLM dataset, which serves as both a testbed for the dataset itself and an exploration of experimental principles. The model is designed to be completely uncensored, offering a raw and unfiltered language processing experience. Its development marks an early phase of testing the EverythingLM dataset's capabilities and innovative training approaches.

Everythinglm 13B features 13b parameters, placing it in the mid-scale range of open-source LLMs, offering a balance between performance and resource efficiency for moderate complexity tasks. Its 16k context length falls into the long-context category, enabling it to process extended texts effectively but requiring more computational resources. This combination makes the model suitable for tasks demanding both depth and extended contextual understanding, though it may not match the scale of larger models or the efficiency of smaller ones.
- Parameter Size: 13b
- Context Length: 16k

Everythinglm 13B is a model with 13b parameters and a 16k context length, designed to support a range of possible applications. Its capacity for automatically triggered chain-of-thought reasoning could enable possible enhancements in tasks requiring step-by-step analysis, though further exploration is needed. The model’s ability to generate verbose and detailed replies suggests possible utility in scenarios where depth of explanation is prioritized, but this would require testing. Creative stories might benefit from its extended context, offering possible opportunities for narrative expansion, though this remains unproven. Improved prompt understanding could lead to possible advancements in interpreting complex queries, though validation is essential. These possible uses highlight the model’s flexibility but underscore the need for careful evaluation before deployment.
- automatically triggered cot reasoning
- verbose and detailed replies
- creative stories
- better prompt understanding

Everythinglm 13B is a 13b parameter model with a 16k context length, offering possible opportunities for tasks requiring extended reasoning or detailed output. Possible applications include automatically triggered chain-of-thought reasoning, which could enhance problem-solving workflows, though its effectiveness would need possible validation. The model’s capacity for verbose and detailed replies suggests possible value in scenarios where in-depth explanations are prioritized, but this would require possible testing. Creative stories might benefit from its long context, enabling possible narrative expansion, though this remains possible to confirm. Better prompt understanding could lead to possible improvements in interpreting complex queries, but possible evaluation is essential. Each of these possible uses requires thorough assessment before implementation.
- automatically triggered cot reasoning
- verbose and detailed replies
- creative stories
- better prompt understanding

Everythinglm 13B in its medium q4 version requires a GPU with at least 16GB VRAM (e.g., RTX 3090) and 32GB+ system memory for smooth operation, balancing precision and performance. This configuration is possible to run on mid-range hardware, but possible variations in workload may demand higher VRAM or optimized drivers. Users should verify their GPU’s capabilities and system resources before deployment.
- fp16, q2, q3, q4, q5, q6, q8

Everythinglm 13B is a 13b parameter model with a 16k context length, based on Llama-2, and licensed under the Llama 2 Community License Agreement. It provides a balance between performance and resource efficiency for moderate complexity tasks, making it suitable for applications requiring extended contextual understanding.

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