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tiiuae / Falcon3-10B-Instruct-AWQ
Model's Last Updated: January 13 2025
Introduction of Falcon3-10B-Instruct-AWQ
Model Details of Falcon3-10B-Instruct-AWQ
Falcon3-10B-Instruct-AWQ
Falcon3 family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
Falcon3-10B-Instruct achieves state-of-the-art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-10B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
This repository contains the AWQ-quantized 4-bit instruction-tuned 10B Falcon3 model.
Model Details
-
Architecture
- Transformer-based causal decoder-only architecture
- 40 decoder blocks
- Grouped Query Attention (GQA) for faster inference: 12 query heads and 4 key-value heads
- Wider head dimension: 256
- High RoPE value to support long context understanding: 1000042
- Uses SwiGLu and RMSNorm
- 32K context length
- 131K vocab size
- Depth up-scaled from Falcon3-7B-Base with 2 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
- Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data
- Supports EN, FR, ES, PT
- Developed by Technology Innovation Institute
- License: TII Falcon-LLM License 2.0
- Model Release Date: December 2024
- Quantization: AWQ 4-bit
Getting started
Click to expand
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "tiiuae/Falcon3-10B-Instruct-AWQ"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many hours in one day?"
messages = [
{"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=1024
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
Benchmarks
We report in the following table our internal pipeline benchmarks:
| Benchmark | Falcon 3-10B Instruct | Falcon 3-10B Instruct-GPTQ-Int4 | Falcon 3-10B Instruct-GPTQ-Int8 | Falcon 3-10B Instruct-AWQ |
|---|---|---|---|---|
| MMLU | 72.0 | 69.7 | 71.9 | 71.1 |
| MMLU-PRO | 44.2 | 41.8 | 44.3 | 43.4 |
| IFEval | 79.0 | 77.1 | 78.3 | 76.8 |
Useful links
- View our release blogpost .
- Feel free to join our discord server if you have any questions or to interact with our researchers and developers.
Technical Report
Coming soon....
Citation
If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
@misc{Falcon3,
title = {The Falcon 3 Family of Open Models},
url = {https://huggingface.co/blog/falcon3},
author = {Falcon-LLM Team},
month = {December},
year = {2024}
}
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Updated:October 12 2024
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