commit 24d2bd6fe52744116dff3ecdd579d5e92c257b6d Author: ModelHub XC Date: Mon Jun 22 04:33:16 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: EasierAI/Falcon-3-7B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..43fe939 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,52 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx 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transformers +tags: +- falcon3 + +--- + +
+ drawing +
+ +# Falcon3-7B-Instruct-GGUF + + +**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. + +**Falcon3-7B-Instruct** achieves state-of-the-art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks. +Falcon3-7B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K. + +This repository contains the GGUFs instruction-tuned 1B Falcon3 model. + +## Model Details +- Architecture + - Transformer-based causal decoder-only architecture + - 28 decoder blocks + - Grouped Query Attention (GQA) for faster inference: 12 query heads and 4 key-value heads + - Wider head dimension: 256 + - High RoPE value to support long context understanding: 1000042 + - Uses SwiGLU and RMSNorm + - 32K context length + - 131K vocab size +- Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips +- Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data +- Supports EN, FR, ES, PT +- Developed by [Technology Innovation Institute](https://www.tii.ae) +- License: TII Falcon-LLM License 2.0 +- Model Release Date: December 2024 +- Quantization: q2_K, q3_K_M, q4_0, q4_K_M, q5_0, q5_K_M, q6_K, q8_0 + + +## Getting started + +### 1. Download GGUF models from hugging face + +First, download the model from Hugging Face. You can use the `huggingface_hub` library or download it manually: + + +```bash +pip install huggingface_hub +huggingface-cli download {model_name} +``` + +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. + + + +## 2. Install llama.cpp + +You have several options for installing llama.cpp: + +**1. Build from source:** + +This gives you the most flexibility and control. Follow the instructions in the llama.cpp repository to build from source: + +```bash + +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +cmake -B build +cmake --build build --config Release +``` + +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)**. + +**2. Download pre-built binaries:** + +If you prefer a quicker setup, you can download pre-built binaries for your operating system. Check the llama.cpp repository for available binaries. + +**3. Use Docker:** + +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. + +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)**. + +### 3. Start playing with your model + +Run simple text completion +```bash +llama-cli -m {path-to-gguf-model} -p "I believe the meaning of life is" -n 128 +``` + +Run in conversation mode +```bash +llama-cli -m {path-to-gguf-model} -p "You are a helpful assistant" -cnv -co +``` + +## Useful links +- View our [release blogpost](https://huggingface.co/blog/falcon3). +- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers. + +## Technical Report +Coming soon.... + +## Citation +If the Falcon3 family of models were helpful to your work, feel free to give us a cite. + +``` +@misc{Falcon3, + title = {The Falcon 3 Family of Open Models}, + url = {https://huggingface.co/blog/falcon3}, + author = {Falcon-LLM Team}, + month = {December}, + year = {2024} +} +``` \ No newline at end of file