Instructions to use tiiuae/Falcon3-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon3-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="tiiuae/Falcon3-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiiuae/Falcon3-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon3-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon3-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon3-7B-Instruct

SGLang

How to use tiiuae/Falcon3-7B-Instruct 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 "tiiuae/Falcon3-7B-Instruct" \
    --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": "tiiuae/Falcon3-7B-Instruct",
		"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 "tiiuae/Falcon3-7B-Instruct" \
        --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": "tiiuae/Falcon3-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiiuae/Falcon3-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon3-7B-Instruct
```

puneeshkhanna commited on Dec 17, 2024

Commit

4cf1db0

verified ·

1 Parent(s): 43ca5d0

Update README.md

Browse files

Correct Math score and rounding

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
 **Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
-This repository contains the **Falcon3-7B-Instruct**. It achieves state of 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 up to 32K.
 ## Model Details
@@ -146,7 +146,7 @@ We report in the following table our internal pipeline benchmarks:
             <td>MATH Lvl-5 (4-shot)</td>
             <td>10.4</td>
             <td>26</td>
-            <td><b>33.1</b></td>
         </tr>
         <tr>
             <td rowspan="5">Reasoning</td>
@@ -207,15 +207,15 @@ We report in the following table our internal pipeline benchmarks:
         <tr>
             <td rowspan="2">Instructions following</td>
             <td>MT-Bench (avg)</td>
-            <td>7.86</td>
-            <td><b>8.54</b></td>
-            <td>8.36</td>
         </tr>
         <tr>
             <td>Alpaca (WC)</td>
-            <td>26.57</td>
             <td><b>31.5</b></td>
-            <td>26.13</td>
         </tr>
         <tr>
             <td>Tool use</td>

 **Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
+This repository contains the **Falcon3-7B-Instruct**. It achieves state of art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks.
 Falcon3-7B-Instruct supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.
 ## Model Details
             <td>MATH Lvl-5 (4-shot)</td>
             <td>10.4</td>
             <td>26</td>
+            <td><b>29.4</b></td>
         </tr>
         <tr>
             <td rowspan="5">Reasoning</td>
         <tr>
             <td rowspan="2">Instructions following</td>
             <td>MT-Bench (avg)</td>
+            <td>7.9</td>
+            <td><b>8.5</b></td>
+            <td>8.4</td>
         </tr>
         <tr>
             <td>Alpaca (WC)</td>
+            <td>26.6</td>
             <td><b>31.5</b></td>
+            <td>26.1</td>
         </tr>
         <tr>
             <td>Tool use</td>