Instructions to use EasierAI/Falcon-3-10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EasierAI/Falcon-3-10B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EasierAI/Falcon-3-10B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EasierAI/Falcon-3-10B", dtype="auto")

llama-cpp-python

How to use EasierAI/Falcon-3-10B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EasierAI/Falcon-3-10B",
	filename="Falcon-3-10B-Instruct-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use EasierAI/Falcon-3-10B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EasierAI/Falcon-3-10B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EasierAI/Falcon-3-10B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EasierAI/Falcon-3-10B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EasierAI/Falcon-3-10B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf EasierAI/Falcon-3-10B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf EasierAI/Falcon-3-10B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf EasierAI/Falcon-3-10B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EasierAI/Falcon-3-10B:Q4_K_M

Use Docker

docker model run hf.co/EasierAI/Falcon-3-10B:Q4_K_M

LM Studio
Jan

vLLM

How to use EasierAI/Falcon-3-10B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EasierAI/Falcon-3-10B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EasierAI/Falcon-3-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EasierAI/Falcon-3-10B:Q4_K_M

SGLang

How to use EasierAI/Falcon-3-10B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EasierAI/Falcon-3-10B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EasierAI/Falcon-3-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EasierAI/Falcon-3-10B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EasierAI/Falcon-3-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use EasierAI/Falcon-3-10B with Ollama:
```
ollama run hf.co/EasierAI/Falcon-3-10B:Q4_K_M
```

Unsloth Studio new

How to use EasierAI/Falcon-3-10B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EasierAI/Falcon-3-10B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for EasierAI/Falcon-3-10B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EasierAI/Falcon-3-10B to start chatting

Pi new

How to use EasierAI/Falcon-3-10B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EasierAI/Falcon-3-10B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "EasierAI/Falcon-3-10B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EasierAI/Falcon-3-10B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EasierAI/Falcon-3-10B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default EasierAI/Falcon-3-10B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use EasierAI/Falcon-3-10B with Docker Model Runner:
```
docker model run hf.co/EasierAI/Falcon-3-10B:Q4_K_M
```

Lemonade

How to use EasierAI/Falcon-3-10B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EasierAI/Falcon-3-10B:Q4_K_M

Run and chat with the model

lemonade run user.Falcon-3-10B-Q4_K_M

List all available models

lemonade list

drkameleon

puneeshkhanna commited on Feb 19, 2025

Commit

496cd37

verified ·

0 Parent(s):

Duplicate from tiiuae/Falcon3-10B-Instruct-GGUF

Browse files

Co-authored-by: Puneesh Khanna <puneeshkhanna@users.noreply.huggingface.co>

Files changed (11) hide show

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+---
+language:
+- en
+- fr
+- es
+- pt
+base_model:
+- tiiuae/Falcon3-10B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- falcon3
+---
+<div align="center">
+    <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
+</div>
+# Falcon3-10B-Instruct-GGUF
+**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
+**Falcon3-10B-Instruct** achieves state-of-the-art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
+Falcon3-10B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
+This repository contains the GGUFs instruction-tuned 10B Falcon3 model.
+## Model Details
+- Architecture
+  - Transformer-based causal decoder-only architecture
+  - 40 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
+- Depth up-scaled from **Falcon3-7B-Base** with 2 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}
+}
+```