tiiuae/Falcon3-10B-Instruct
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity
Files
main
Falcon3-10B-Instruct/README.md
ModelHub XC 800aa0950b 初始化项目，由ModelHub XC社区提供模型 
Model: tiiuae/Falcon3-10B-Instruct
Source: Original Platform
2026-05-01 15:52:07 +08:00

436 lines
12 KiB
Markdown
Raw Permalink Blame History

---
base_model: tiiuae/Falcon3-10B-Base
library_name: transformers
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
tags:
- falcon3
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 the official HuggingFace leaderboard normalized evaluations [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) in the following table.
<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
<colgroup>
<col style="width: 10%;">
<col style="width: 7%;">
<col style="width: 7%;">
<col style="width: 7%;">
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Benchmark</th>
<th>Yi-1.5-9B-Chat</th>
<th>Mistral-Nemo-Instruct-2407 (12B)</th>
<th>Gemma-2-9b-it</th>
<th>Falcon3-10B-Instruct</th>
</tr>
</thead>
<tbody>
<tr>
<td>IFEval</td>
<td>60.46</td>
<td>63.80</td>
<td>74.36</td>
<td><b>78.17</b></td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>36.95</td>
<td>29.68</td>
<td>42.14</td>
<td><b>44.82</b></td>
</tr>
<tr>
<td>MATH Lvl-5 (4-shot)</td>
<td>12.76</td>
<td>6.50</td>
<td>0.23</td>
<td><b>25.91</b></td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td>11.30</td>
<td>5.37</td>
<td><b>14.77</b></td>
<td>10.51</td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td>12.84</td>
<td>8.48</td>
<td>9.74</td>
<td><b>13.61</b></td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>33.06</td>
<td>27.97</td>
<td>31.95</td>
<td><b>38.10</b></td>
</tr>
</tbody>
</table>
Also, 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 and fewshot_as_multiturn.
- 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-Instruct-2407 (12B)</th>
<th>Falcon3-10B-Instruct</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>68.8</td>
<td>66.0</td>
<td><b>73.9</b></td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>38.8</td>
<td>34.3</td>
<td><b>44</b></td>
</tr>
<tr>
<td>IFEval</td>
<td>57.8</td>
<td>63.4</td>
<td><b>78</b></td>
</tr>
<tr>
<td rowspan="3">Math</td>
<td>GSM8K (5-shot)</td>
<td>77.1</td>
<td>77.6</td>
<td><b>84.9</b></td>
</tr>
<tr>
<td>GSM8K (8-shot, COT)</td>
<td>76</td>
<td>80.4</td>
<td><b>84.6</b></td>
</tr>
<tr>
<td>MATH Lvl-5 (4-shot)</td>
<td>3.3</td>
<td>5.9</td>
<td><b>22.1</b></td>
</tr>
<tr>
<td rowspan="5">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>58.3</td>
<td>63.4</td>
<td><b>66.2</b></td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td><b>35.6</b></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</b></td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td><b>41.9</b></td>
<td>38.1</td>
<td>41.1</td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>50.6</td>
<td>47.5</td>
<td><b>58.4</b></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</b></td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>61.7</td>
<td>76.4</td>
<td><b>90.4</b></td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>-</td>
<td>-</td>
<td>71</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>43.2</td>
<td>47.4</td>
<td><b>48.2</b></td>
</tr>
<tr>
<td rowspan="2">Instructions following</td>
<td>MT-Bench (avg)</td>
<td>8.3</td>
<td><b>8.6</b></td>
<td>8.2</td>
</tr>
<tr>
<td>Alpaca (WC)</td>
<td>25.8</td>
<td><b>45.4</b></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>90.5</b></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|
Reference in New Issue View Git Blame Copy Permalink