Triangle104
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README.md
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This model was converted to GGUF format from [`tiiuae/Falcon3-3B-Instruct`](https://huggingface.co/tiiuae/Falcon3-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/tiiuae/Falcon3-3B-Instruct) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`tiiuae/Falcon3-3B-Instruct`](https://huggingface.co/tiiuae/Falcon3-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/tiiuae/Falcon3-3B-Instruct) for more details on the model.
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---
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Model details:
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Falcon3 family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
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Falcon3-3B-Instruct achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-3B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
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Model Details
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Architecture
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Transformer-based causal decoder-only architecture
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22 decoder blocks
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Grouped Query Attention (GQA) for faster inference: 12 query heads and 4 key-value heads
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Wider head dimension: 256
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High RoPE value to support long context understanding: 1000042
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Uses SwiGLU and RMSNorm
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32K context length
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131K vocab size
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Pruned and healed from Falcon3-7B-Base on only 100 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
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Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data
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Supports EN, FR, ES, PT
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Developed by Technology Innovation Institute
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License: TII Falcon-LLM License 2.0
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Model Release Date: December 2024
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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