Instructions to use fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym with Transformers:

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

pipe = pipeline("text-generation", model="fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym")
model = AutoModelForCausalLM.from_pretrained("fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym

SGLang

How to use fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym 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 "fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym" \
    --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": "fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym",
		"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 "fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym" \
        --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": "fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym
```

tiiuae_Falcon3-3B-Instruct-auto_gptq-int4-gs64-sym

File size: 688 Bytes

8d00d0f

{
  "schema_version": 1,
  "model_id": "tiiuae/Falcon3-3B-Instruct",
  "format": "auto_gptq",
  "bits": 4,
  "group_size": 64,
  "sym": true,
  "symmetry_label": "sym",
  "duration_s": 17471.39,
  "completed_at_utc": "2026-07-05T20:13:24.268556+00:00",
  "auto_round_version": "0.13.1",
  "transformers_version": "4.55.3",
  "torch_version": "2.12.1+cpu",
  "torch_dtype": "torch.bfloat16",
  "device": "cpu",
  "calibration": {
    "nsamples": 128,
    "iters": 200,
    "seqlen": 512,
    "batch_size": 4
  },
  "base_model_type": "llama",
  "quality_metrics": {
    "schema_version": 1,
    "per_block": [],
    "aggregate": {
      "n_blocks": 0,
      "available": false
    }
  }
}