Instructions to use PracticalWork/Falcon3-1B-Instruct-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PracticalWork/Falcon3-1B-Instruct-tuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-1B-Instruct") model = PeftModel.from_pretrained(base_model, "PracticalWork/Falcon3-1B-Instruct-tuned") - Transformers
How to use PracticalWork/Falcon3-1B-Instruct-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PracticalWork/Falcon3-1B-Instruct-tuned") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PracticalWork/Falcon3-1B-Instruct-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PracticalWork/Falcon3-1B-Instruct-tuned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PracticalWork/Falcon3-1B-Instruct-tuned" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PracticalWork/Falcon3-1B-Instruct-tuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PracticalWork/Falcon3-1B-Instruct-tuned
- SGLang
How to use PracticalWork/Falcon3-1B-Instruct-tuned 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 "PracticalWork/Falcon3-1B-Instruct-tuned" \ --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": "PracticalWork/Falcon3-1B-Instruct-tuned", "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 "PracticalWork/Falcon3-1B-Instruct-tuned" \ --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": "PracticalWork/Falcon3-1B-Instruct-tuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PracticalWork/Falcon3-1B-Instruct-tuned with Docker Model Runner:
docker model run hf.co/PracticalWork/Falcon3-1B-Instruct-tuned
Download chat_template.jinja from PracticalWork/Falcon3-1B-Instruct-tuned: direct link, hf CLI and curl.
- Browser
- Download file 2.09 kB
-
https://huggingface.co/PracticalWork/Falcon3-1B-Instruct-tuned/resolve/main/chat_template.jinja
- Command line
-
hf download hf://PracticalWork/Falcon3-1B-Instruct-tuned/chat_template.jinja
-
curl -L -o chat_template.jinja https://huggingface.co/PracticalWork/Falcon3-1B-Instruct-tuned/resolve/main/chat_template.jinja
2.09 kB
| {%- 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 %} |