Instructions to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO") model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO", 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 tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO" # 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-H1-Tiny-90M-Instruct-Curriculum-pre-DPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO
- SGLang
How to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO 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-H1-Tiny-90M-Instruct-Curriculum-pre-DPO" \ --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-H1-Tiny-90M-Instruct-Curriculum-pre-DPO", "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-H1-Tiny-90M-Instruct-Curriculum-pre-DPO" \ --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-H1-Tiny-90M-Instruct-Curriculum-pre-DPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO with Docker Model Runner:
docker model run hf.co/tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO
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3021b02 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {%- if tools %} {{bos_token}}<|system|>
{%- 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.
# Tools
You 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.
<tools>' + tools|tojson(indent=2) + '</tools>
# Output Format
Your response MUST follow this format when making function calls:
<tool_call>
[
{"name": "function_name", "arguments": {"arg1": "value1", "arg2": "value2"}},
{"name": "another_function", "arguments": {"arg": "value"}}
]
</tool_call>
If no function calls are needed, respond normally without the tool_call tags.' }}
{%- for message in remaining_messages %}
{%- if message['role'] == 'user' %}
<|im_start|>user
{{ message['content'] }}<|im_end|>
{%- elif message['role'] == 'assistant' %}
{%- if message.content %}
<|im_start|>assistant
{{ message['content'] }}
<|im_end|>
{%- endif %}
{%- if message.tool_calls %}
<tool_call>
{{ message.tool_calls|tojson(indent=2) }}
</tool_call>
{%- endif %}
{%- elif message['role'] == 'tool' %}
<|im_start|>assistant
<tool_response>
{{ message['content'] }}
</tool_response><|im_end|>
{%- endif %}
{%- endfor %}
{{ '<|im_start|>assistant
' if add_generation_prompt }}
{%- else %} {{bos_token}}{% for message in messages %} {{ '<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>
' }} {% endfor %} {% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %} {%- endif %} |