transformers gguf en fr de es it pt ja ko zh ar base_model:CohereForAI/c4ai-command-r-08-2024 base_model:quantized:CohereForAI/c4ai-command-r-08-2024 license:cc-by-nc-4.0 endpoints_compatible region:us imatrix conversational
---
base_model: CohereForAI/c4ai-command-r-08-2024
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
library_name: transformers
license: cc-by-nc-4.0
model_name: c4ai-command-r-08-2024-GGUF
extra_gated_prompt: By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license)  and
  acknowledge that the information you provide will be collected, used, and shared
  in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy).
extra_gated_fields:
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    - Zambia
    - Zimbabwe
  Receive email updates on C4AI and Cohere research, events, products and services?:
    type: select
    options:
    - 'Yes'
    - 'No'
  I agree to use this model for non-commercial use ONLY: checkbox
quantized_by: brooketh
parameter_count: 32296476672
---
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***
# C4ai Command R 08 2024 34B
- **Creator:** [CohereForAI](https://huggingface.co/CohereForAI/)
- **Original:** [C4ai Command R 08 2024 34B](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024)
- **Date Created:** 2024-08-19
- **Trained Context:** 131072 tokens
- **Description:** Research release of a 32 billion parameter highly performant generative model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Has the capability for multilingual generation, trained on 23 languages and evaluated in 10 languages and highly performant RAG capabilities.
***
## What is a GGUF?
GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.

***
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***