Instructions to use AIFunOver/Falcon3-10B-Instruct-openvino-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIFunOver/Falcon3-10B-Instruct-openvino-8bit with Transformers:

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

pipe = pipeline("text-generation", model="AIFunOver/Falcon3-10B-Instruct-openvino-8bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIFunOver/Falcon3-10B-Instruct-openvino-8bit")
model = AutoModelForCausalLM.from_pretrained("AIFunOver/Falcon3-10B-Instruct-openvino-8bit")
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

vLLM

How to use AIFunOver/Falcon3-10B-Instruct-openvino-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIFunOver/Falcon3-10B-Instruct-openvino-8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIFunOver/Falcon3-10B-Instruct-openvino-8bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIFunOver/Falcon3-10B-Instruct-openvino-8bit

SGLang

How to use AIFunOver/Falcon3-10B-Instruct-openvino-8bit 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 "AIFunOver/Falcon3-10B-Instruct-openvino-8bit" \
    --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": "AIFunOver/Falcon3-10B-Instruct-openvino-8bit",
		"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 "AIFunOver/Falcon3-10B-Instruct-openvino-8bit" \
        --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": "AIFunOver/Falcon3-10B-Instruct-openvino-8bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIFunOver/Falcon3-10B-Instruct-openvino-8bit with Docker Model Runner:
```
docker model run hf.co/AIFunOver/Falcon3-10B-Instruct-openvino-8bit
```

This model is a quantized version of tiiuae/Falcon3-10B-Instruct and is converted to the OpenVINO format. This model was obtained via the nncf-quantization space with optimum-intel. First make sure you have optimum-intel installed:

pip install optimum[openvino]

To load your model you can do as follows:

from optimum.intel import OVModelForCausalLM
model_id = "AIFunOver/Falcon3-10B-Instruct-openvino-8bit"
model = OVModelForCausalLM.from_pretrained(model_id)

Downloads last month: 1

Model tree for AIFunOver/Falcon3-10B-Instruct-openvino-8bit

Base model

tiiuae/Falcon3-10B-Base

Finetuned

tiiuae/Falcon3-10B-Instruct

Quantized

(41)

this model

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

78.170
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

44.820
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

25.910
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

10.510
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

13.610
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

38.100