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: 2,092 Bytes

8d00d0f

{%- if tools %}
{{- '<|system|>\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- set remaining_messages = messages[1:] %}
{%- else %}
{%- set remaining_messages = messages %}
{%- endif %}
{{- 'You are a Falcon assistant skilled in function calling. You are helpful, respectful, and concise.\n\n# Tools\n\nYou have access to the following functions. You MUST use them to answer questions when needed. For each function call, you MUST return a JSON object inside <tool_call></tool_call> tags.\n\n<tools>' + tools|tojson(indent=2) + '</tools>\n\n# Output Format\n\nYour response MUST follow this format when making function calls:\n<tool_call>\n[\n  {"name": "function_name", "arguments": {"arg1": "value1", "arg2": "value2"}},\n  {"name": "another_function", "arguments": {"arg": "value"}}\n]\n</tool_call>\nIf no function calls are needed, respond normally without the tool_call tags.\n' }}
{%- for message in remaining_messages %}
{%- if message['role'] == 'user' %}
{{- '<|user|>\n' + message['content'] + '\n' }}
{%- elif message['role'] == 'assistant' %}
{%- if message.content %}
{{- '<|assistant|>\n' + message['content'] }}
{%- endif %}
{%- if message.tool_calls %}
{{- '\n<tool_call>\n' }}
{{- message.tool_calls|tojson(indent=2) }}
{{- '\n</tool_call>' }}
{%- endif %}
{{- eos_token + '\n' }}
{%- elif message['role'] == 'tool' %}
{{- '<|assistant|>\n<tool_response>\n' + message['content'] + '\n</tool_response>\n' }}
{%- endif %}
{%- endfor %}
{{- '<|assistant|>\n' if add_generation_prompt }}
{%- else %}
{%- for message in messages %}
{%- if message['role'] == 'system' %}
{{- '<|system|>\n' + message['content'] + '\n' }}
{%- elif message['role'] == 'user' %}
{{- '<|user|>\n' + message['content'] + '\n' }}
{%- elif message['role'] == 'assistant' %}
{%- if not loop.last %}
{{- '<|assistant|>\n' + message['content'] + eos_token + '\n' }}
{%- else %}
{{- '<|assistant|>\n' + message['content'] + eos_token }}
{%- endif %}
{%- endif %}
{%- if loop.last and add_generation_prompt %}
{{- '<|assistant|>\n' }}
{%- endif %}
{%- endfor %}
{%- endif %}