Instructions to use tiiuae/Falcon3-1B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiiuae/Falcon3-1B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tiiuae/Falcon3-1B-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-1B-Base") model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-1B-Base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tiiuae/Falcon3-1B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tiiuae/Falcon3-1B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiiuae/Falcon3-1B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tiiuae/Falcon3-1B-Base
- SGLang
How to use tiiuae/Falcon3-1B-Base 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/Falcon3-1B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiiuae/Falcon3-1B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/Falcon3-1B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiiuae/Falcon3-1B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tiiuae/Falcon3-1B-Base with Docker Model Runner:
docker model run hf.co/tiiuae/Falcon3-1B-Base
Delete model.safetensors.index.json
#14
by ybelkada - opened
No description provided.
Fixes: https://huggingface.co/tiiuae/Falcon3-1B-Base/discussions/13
There is no need to have a model.safetensors.index.json if the model weights are not sharded. It looks like this was a mistake at first place - e.g.: https://huggingface.co/tiiuae/Falcon3-1B-Instruct/tree/main does not have an index file
Tested locally if this PR does not break anything with the following snippet:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiiuae/Falcon3-1B-Base"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", revision="refs/pr/4")
tok = AutoTokenizer.from_pretrained(model_id)
print(model)
text = "The capital city of United States of America is"
inputs = tok(text, return_tensors="pt").to(0)
inputs.pop("token_type_ids", None)
out = model.generate(**inputs, max_new_tokens=10, do_sample=False)
print(tok.decode(out[0]))
ybelkada changed pull request status to merged