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Model: EasierAI/Falcon-3-7B Source: Original Platform
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---
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language:
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- en
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- fr
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- es
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- pt
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base_model:
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- tiiuae/Falcon3-7B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- falcon3
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---
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<div align="center">
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<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
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</div>
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# Falcon3-7B-Instruct-GGUF
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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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**Falcon3-7B-Instruct** achieves state-of-the-art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
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Falcon3-7B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
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This repository contains the GGUFs instruction-tuned 1B Falcon3 model.
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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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- 28 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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- Pretrained on 14 Teratokens 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](https://www.tii.ae)
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- License: TII Falcon-LLM License 2.0
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- Model Release Date: December 2024
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- Quantization: q2_K, q3_K_M, q4_0, q4_K_M, q5_0, q5_K_M, q6_K, q8_0
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## Getting started
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### 1. Download GGUF models from hugging face
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First, download the model from Hugging Face. You can use the `huggingface_hub` library or download it manually:
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```bash
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pip install huggingface_hub
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huggingface-cli download {model_name}
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```
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This will download the model to your current directory. Make sure to replace {model_name} with the actual username and model name from your Hugging Face repository.
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## 2. Install llama.cpp
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You have several options for installing llama.cpp:
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**1. Build from source:**
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This gives you the most flexibility and control. Follow the instructions in the llama.cpp repository to build from source:
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```bash
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git clone https://github.com/ggerganov/llama.cpp
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cd llama.cpp
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cmake -B build
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cmake --build build --config Release
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```
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For more information about how to build llama.cpp from source please refere to llama.cpp documentation on how to build from source: **[llama.cpp build from source](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)**.
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**2. Download pre-built binaries:**
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If you prefer a quicker setup, you can download pre-built binaries for your operating system. Check the llama.cpp repository for available binaries.
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**3. Use Docker:**
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For a more contained environment, you can use the official llama.cpp Docker image. Refer to the llama.cpp documentation for instructions on how to use the Docker image.
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For detailed instructions and more information, please check the llama.cpp documentation on docker: **[llama.cpp docker](https://github.com/ggerganov/llama.cpp/blob/master/docs/docker.mdg)**.
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### 3. Start playing with your model
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Run simple text completion
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```bash
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llama-cli -m {path-to-gguf-model} -p "I believe the meaning of life is" -n 128
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```
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Run in conversation mode
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```bash
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llama-cli -m {path-to-gguf-model} -p "You are a helpful assistant" -cnv -co
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```
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## Useful links
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- View our [release blogpost](https://huggingface.co/blog/falcon3).
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- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
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## Technical Report
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Coming soon....
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## Citation
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If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
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```
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@misc{Falcon3,
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title = {The Falcon 3 Family of Open Models},
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url = {https://huggingface.co/blog/falcon3},
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author = {Falcon-LLM Team},
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month = {December},
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year = {2024}
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}
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```
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