|
--- |
|
license: other |
|
library_name: transformers |
|
tags: |
|
- falcon3 |
|
base_model: tiiuae/Falcon3-10B-Base |
|
license_name: falcon-llm-license |
|
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html |
|
model-index: |
|
- name: Falcon3-10B-Instruct |
|
results: |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: IFEval (0-Shot) |
|
type: HuggingFaceH4/ifeval |
|
args: |
|
num_few_shot: 0 |
|
metrics: |
|
- type: inst_level_strict_acc and prompt_level_strict_acc |
|
value: 78.17 |
|
name: strict accuracy |
|
source: |
|
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: BBH (3-Shot) |
|
type: BBH |
|
args: |
|
num_few_shot: 3 |
|
metrics: |
|
- type: acc_norm |
|
value: 44.82 |
|
name: normalized accuracy |
|
source: |
|
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: MATH Lvl 5 (4-Shot) |
|
type: hendrycks/competition_math |
|
args: |
|
num_few_shot: 4 |
|
metrics: |
|
- type: exact_match |
|
value: 25.91 |
|
name: exact match |
|
source: |
|
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: GPQA (0-shot) |
|
type: Idavidrein/gpqa |
|
args: |
|
num_few_shot: 0 |
|
metrics: |
|
- type: acc_norm |
|
value: 10.51 |
|
name: acc_norm |
|
source: |
|
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: MuSR (0-shot) |
|
type: TAUR-Lab/MuSR |
|
args: |
|
num_few_shot: 0 |
|
metrics: |
|
- type: acc_norm |
|
value: 13.61 |
|
name: acc_norm |
|
source: |
|
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct |
|
name: Open LLM Leaderboard |
|
- task: |
|
type: text-generation |
|
name: Text Generation |
|
dataset: |
|
name: MMLU-PRO (5-shot) |
|
type: TIGER-Lab/MMLU-Pro |
|
config: main |
|
split: test |
|
args: |
|
num_few_shot: 5 |
|
metrics: |
|
- type: acc |
|
value: 38.1 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct |
|
name: Open LLM Leaderboard |
|
--- |
|
|
|
<div align="center"> |
|
<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/> |
|
</div> |
|
|
|
# Falcon3-10B-Instruct |
|
|
|
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. |
|
|
|
This repository contains the **Falcon3-10B-Instruct**. It achieves state-of-the-art results (at the time of release) 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. |
|
|
|
|
|
## 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](https://www.tii.ae) |
|
- License: TII Falcon-LLM License 2.0 |
|
- Model Release Date: December 2024 |
|
|
|
|
|
## Getting started |
|
|
|
<details> |
|
<summary> Click to expand </summary> |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
model_name = "tiiuae/Falcon3-10B-Instruct" |
|
|
|
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) |
|
``` |
|
|
|
</details> |
|
|
|
<br> |
|
|
|
## Benchmarks |
|
We report in the following table our internal pipeline benchmarks. |
|
- We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). |
|
- We report **raw scores** obtained by applying chat template **without fewshot_as_multiturn** (unlike Llama3.1). |
|
- We use same batch-size across all models. |
|
|
|
|
|
|
|
<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;"> |
|
<colgroup> |
|
<col style="width: 10%;"> |
|
<col style="width: 10%;"> |
|
<col style="width: 7%;"> |
|
<col style="width: 7%;"> |
|
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;"> |
|
</colgroup> |
|
<thead> |
|
<tr> |
|
<th>Category</th> |
|
<th>Benchmark</th> |
|
<th>Yi-1.5-9B-Chat</th> |
|
<th>Mistral-Nemo-Base-2407 (12B)</th> |
|
<th>Falcon3-10B-Instruct</th> |
|
</tr> |
|
</thead> |
|
<tbody> |
|
<tr> |
|
<td rowspan="3">General</td> |
|
<td>MMLU (5-shot)</td> |
|
<td>70</td> |
|
<td>65.9</td> |
|
<td><b>71.6</td> |
|
</tr> |
|
<tr> |
|
<td>MMLU-PRO (5-shot)</td> |
|
<td>39.6</td> |
|
<td>32.7</td> |
|
<td><b>44</td> |
|
</tr> |
|
<tr> |
|
<td>IFEval</td> |
|
<td>57.6</td> |
|
<td>63.4</td> |
|
<td><b>78</td> |
|
</tr> |
|
<tr> |
|
<td rowspan="3">Math</td> |
|
<td>GSM8K (5-shot)</td> |
|
<td>76.6</td> |
|
<td>73.8</td> |
|
<td><b>83.1</td> |
|
</tr> |
|
<tr> |
|
<td>GSM8K (8-shot, COT)</td> |
|
<td>78.5</td> |
|
<td>73.6</td> |
|
<td><b>81.3</td> |
|
</tr> |
|
<tr> |
|
<td>MATH Lvl-5 (4-shot)</td> |
|
<td>8.8</td> |
|
<td>0.4</td> |
|
<td><b>22.1</td> |
|
</tr> |
|
<tr> |
|
<td rowspan="5">Reasoning</td> |
|
<td>Arc Challenge (25-shot)</td> |
|
<td>51.9</td> |
|
<td>61.6</td> |
|
<td><b>64.5</td> |
|
</tr> |
|
<tr> |
|
<td>GPQA (0-shot)</td> |
|
<td><b>35.4</td> |
|
<td>33.2</td> |
|
<td>33.5</td> |
|
</tr> |
|
<tr> |
|
<td>GPQA (0-shot, COT)</td> |
|
<td>16</td> |
|
<td>12.7</td> |
|
<td><b>32.6</td> |
|
</tr> |
|
<tr> |
|
<td>MUSR (0-shot)</td> |
|
<td><b>41.9</td> |
|
<td>38.1</td> |
|
<td>41.1</td> |
|
</tr> |
|
<tr> |
|
<td>BBH (3-shot)</td> |
|
<td>49.2</td> |
|
<td>43.6</td> |
|
<td><b>58.4</td> |
|
</tr> |
|
<tr> |
|
<td rowspan="4">CommonSense Understanding</td> |
|
<td>PIQA (0-shot)</td> |
|
<td>76.4</td> |
|
<td>78.2</td> |
|
<td><b>78.4</td> |
|
</tr> |
|
<tr> |
|
<td>SciQ (0-shot)</td> |
|
<td>61.7</td> |
|
<td>76.4</td> |
|
<td><b>90.4</td> |
|
</tr> |
|
<tr> |
|
<td>Winogrande (0-shot)</td> |
|
<td>-</td> |
|
<td>-</td> |
|
<td>71.3</td> |
|
</tr> |
|
<tr> |
|
<td>OpenbookQA (0-shot)</td> |
|
<td>43.2</td> |
|
<td>47.4</td> |
|
<td><b>48.2</td> |
|
</tr> |
|
<tr> |
|
<td rowspan="2">Instructions following</td> |
|
<td>MT-Bench (avg)</td> |
|
<td>8.28</td> |
|
<td><b>8.6</td> |
|
<td>8.17</td> |
|
</tr> |
|
<tr> |
|
<td>Alpaca (WC)</td> |
|
<td>25.81</td> |
|
<td><b>45.44</td> |
|
<td>24.7</td> |
|
</tr> |
|
<tr> |
|
<td>Tool use</td> |
|
<td>BFCL AST (avg)</td> |
|
<td>48.4</td> |
|
<td>74.2</td> |
|
<td><b>86.3</td> |
|
</tr> |
|
<tr> |
|
<td rowspan="2">Code</td> |
|
<td>EvalPlus (0-shot) (avg)</td> |
|
<td>69.4</td> |
|
<td>58.9</td> |
|
<td><b>74.7</b></td> |
|
</tr> |
|
<tr> |
|
<td>Multipl-E (0-shot) (avg)</td> |
|
<td>-</td> |
|
<td>34.5</td> |
|
<td><b>45.8</b></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
## Useful links |
|
- View our [release blogpost](https://huggingface.co/blog/falcon3). |
|
- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers. |
|
|
|
## Technical Report |
|
|
|
Coming soon.... |
|
|
|
## Citation |
|
If Falcon3 family were helpful in your work, feel free to give us a cite. |
|
|
|
``` |
|
@misc{Falcon3, |
|
title = {The Falcon 3 family of Open Models}, |
|
author = {TII Team}, |
|
month = {December}, |
|
year = {2024} |
|
} |
|
``` |
|
|
|
|
|
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
|
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/tiiuae__Falcon3-10B-Instruct-details) |
|
|
|
| Metric |Value| |
|
|-------------------|----:| |
|
|Avg. |35.19| |
|
|IFEval (0-Shot) |78.17| |
|
|BBH (3-Shot) |44.82| |
|
|MATH Lvl 5 (4-Shot)|25.91| |
|
|GPQA (0-shot) |10.51| |
|
|MuSR (0-shot) |13.61| |
|
|MMLU-PRO (5-shot) |38.10| |
|
|
|
|
