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

Libraries

How to use tiiuae/Falcon-E-3B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon-E-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon-E-3B-Instruct")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiiuae/Falcon-E-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon-E-3B-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/Falcon-E-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon-E-3B-Instruct

SGLang

How to use tiiuae/Falcon-E-3B-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/Falcon-E-3B-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/Falcon-E-3B-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/Falcon-E-3B-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/Falcon-E-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiiuae/Falcon-E-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon-E-3B-Instruct
```

Update README.md

by hangzou - opened May 16, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -50,7 +50,7 @@ In case you want to perform inference on the BitNet checkpoint run:
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = "tiiuae/Falcon-E-1B-Base"
 model = AutoModelForCausalLM.from_pretrained(
   model_id,
@@ -66,7 +66,7 @@ If you want to rather use the classic `bfloat16` version, you can run:
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = "tiiuae/Falcon-E-1B-Base"
 revision = "bfloat16"
 model = AutoModelForCausalLM.from_pretrained(
@@ -84,8 +84,8 @@ model = AutoModelForCausalLM.from_pretrained(
 ```
 git clone https://github.com/microsoft/BitNet && cd BitNet
 pip install -r requirements.txt
-python setup_env.py --hf-repo tiiuae/Falcon-E-1B-Base -q i2_s
-python run_inference.py -m models/Falcon-E-1B-Base/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
 ```
 ### Fine-tuning
@@ -99,7 +99,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from trl import SFTTrainer
 + from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit
-model_id = "tiiuae/Falcon-E-1B-Base"
 tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
 model = AutoModelForCausalLM.from_pretrained(

 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "tiiuae/Falcon-E-3B-Instruct"
 model = AutoModelForCausalLM.from_pretrained(
   model_id,
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "tiiuae/Falcon-E-3B-Instruct"
 revision = "bfloat16"
 model = AutoModelForCausalLM.from_pretrained(
 ```
 git clone https://github.com/microsoft/BitNet && cd BitNet
 pip install -r requirements.txt
+python setup_env.py --hf-repo tiiuae/Falcon-E-3B-Instruct -q i2_s
+python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
 ```
 ### Fine-tuning
 from trl import SFTTrainer
 + from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit
+model_id = "tiiuae/Falcon-E-3B-Instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
 model = AutoModelForCausalLM.from_pretrained(