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Model: tiiuae/Falcon-E-3B-Base-prequantized Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- bitnet
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- falcon-e
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- edge
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license: other
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license_name: falcon-llm-license
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license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
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---
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# Table of Contents
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0. [TL;DR](#TL;DR)
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1. [Model Details](#model-details)
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2. [Training Details](#training-details)
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3. [Usage](#usage)
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4. [Evaluation](#evaluation)
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5. [Citation](#citation)
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This is simply the mirror of https://huggingface.co/tiiuae/Falcon-E-3B-Base - branch `prequantized`
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# TL;DR
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# Model Details
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## Model Description
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- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
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- **Model type:** Causal decoder-only / Base version
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- **Architecture:** Pure-transformer - 1.58bit version
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- **Language(s) (NLP):** English
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- **License:** Falcon-LLM License
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# Training details
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For more details about the training protocol of this model, please refer to the [Falcon-E technical blogpost](https://falcon-lm.github.io/blog/falcon-edge/).
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# Usage
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Currently to use this model you can either rely on Hugging Face transformers library or [BitNet](https://github.com/microsoft/BitNet) library. There are multiple ways to interact with the model depending on your target usage. For each of the Falcon-E series model, you have three variants: the BitNet model, the prequantized checkpoint for fine-tuning and the `bfloat16` version of the BitNet model.
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### Inference
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#### 🤗 transformers
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In case you want to perform inference on the BitNet checkpoint run:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "tiiuae/Falcon-E-1B-Base"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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).to("cuda")
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# Perform text generation
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```
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If you want to rather use the classic `bfloat16` version, you can run:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "tiiuae/Falcon-E-1B-Base"
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revision = "bfloat16"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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revision=revision,
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).to("cuda")
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# Perform text generation
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```
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#### BitNet
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```
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git clone https://github.com/microsoft/BitNet && cd BitNet
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pip install -r requirements.txt
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python setup_env.py --hf-repo tiiuae/Falcon-E-1B-Base -q i2_s
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python run_inference.py -m models/Falcon-E-1B-Base/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
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```
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#### Apply mlx-lm
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```
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pip install -U mlx-lm
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```
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Then:
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```
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mlx_lm.generate --model tiiuae/Falcon-E-3B-Instruct --prompt "Implement bubble sort" --max-tokens 100 --temp 0.1
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```
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### Fine-tuning
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For fine-tuning the model, you should load the `prequantized` revision of the model and use the `onebitllms` Python package:
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```diff
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from trl import SFTTrainer
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+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit
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model_id = "tiiuae/Falcon-E-1B-Base"
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tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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+ revision="prequantized"
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)
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+ model = replace_linear_with_bitnet_linear(model)
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trainer = SFTTrainer(
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model,
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...
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)
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trainer.train()
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+ quantize_to_1bit(output_directory)
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```
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# Evaluation
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We report in the following table our internal pipeline benchmarks:
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**Note evaluation results are normalized score from former Hugging Face leaderboard v2 tasks**
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<details>
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<summary class="bold"> For 1B scale models and below </summary>
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| Model | Nb Params | Mem Footprint | IFEVAL | Math-Hard | GPQA | MuSR | BBH | MMLU-Pro | Avg. |
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| -------- | ------- | ------- | ------- | ------ | ----- | ----- | ----- | ------ | ---- |
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| Qwen-2.5-0.5B | 0.5B | 1GB | 16.27 | 3.93 | 0.0 | 2.08 | 6.95 | 10.06 | 6.55 |
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| SmolLM2-360M | 0.36B | 720MB | 21.15 | 1.21 | 0.0 | 7.73 | 5.54 | 1.88 | 6.25 |
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| Qwen-2.5-1.5B | 1.5B | 3.1GB | 26.74 | 9.14 | 16.66 | 5.27 | 20.61 | 4.7 | 13.85 |
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| Llama-3.2-1B | 1.24B | 2.47GB | 14.78 | 1.21 | 4.37 | 2.56 | 2.26 | 0 | 4.2 |
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| SmolLM2-1.7B | 1.7B | 3.4GB | 24.4 | 2.64 | 9.3 | 4.6 | 12.64 | 3.91 | 9.58 |
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| Falcon-3-1B-Base | 1.5B | 3GB | 24.28 | 3.32 | 11.34 | 9.71 | 6.76 | 3.91 | 9.89 |
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| Hymba-1.5B-Base | 1.5B | 3GB | 22.95 | 1.36 | 7.69 | 5.18 | 10.25 | 0.78 | 8.04 |
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| Falcon-E-1B-Base | 1.8B | **635MB** | 32.9 | 10.97 | 2.8 | 3.65 | 12.28 | 17.82 | 13.40 |
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</details>
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<details>
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<summary class="bold"> For 3B scale models </summary>
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| Model | Nb Params | Mem Footprint | IFEVAL | Math-Hard | GPQA | MuSR | BBH | MMLU-Pro | Avg. |
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| -------- | ------- | ------- | ------- | ------ | ----- | ----- | ----- | ------ | ---- |
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| Falcon-3-3B-Base | 3B | 6.46GB | 15.74 | 11.78 | 21.58 | 6.27 | 18.09 | 6.26 | 15.74 |
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| Qwen2.5-3B | 3B | 6.17GB | 26.9 | 14.8 | 24.3 | 11.76 | 24.48 | 6.38 | 18.1 |
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| Falcon-E-3B-Base | 3B | **999MB** | 36.67 | 13.45 | 8.67 | 4.14 | 19.83 | 27.16 | 18.32 |
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</details>
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Below are the results for instruction fine-tuned models:
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<details>
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<summary class="bold"> For 1B scale models and below </summary>
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| Model | Nb Params | Mem Footprint | IFEVAL | Math-Hard | GPQA | MuSR | BBH | MMLU-Pro | Avg. |
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| -------- | ------- | ------- | ------- | ------ | ----- | ----- | ----- | ------ | ---- |
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| Qwen-2.5-0.5B-Instruct | 500M | 1GB | 30.71 | 0 | 8.43 | 0.94 | 7.75 | 0 | 6.59 |
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| SmolLM2-360M-Instruct | 360M | 720MB | 38.42 | 1.51 | 4.17 | 2.77 | 1.3 | 0.67 | 8.14 |
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| Qwen-2.5-1.5B-Instruct | 1.5B | 3.1GB | 44.76 | 22.05 | 19.81 | 3.19 | 19.99 | 0.78 | 18.43 |
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| SmolLM2-1.7B | 1.7B | 3.4GB | 53.68 | 5.82 | 10.92 | 4.1 | 11.71 | 0 | 15.02 |
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| Falcon-3-1B-Instruct | 1.5B | 3GB | 55.57 | 6.34 | 12.96 | 10.56 | 9.32 | 2.24 | 16.16 |
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| Hymba-1.5B-Instruct | 1.5B | 3GB | 60.09 | 2.72 | 4.59 | 1.05 | 11.56 | 5.515 | 14.19 |
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| Falcon-E-1B-Instruct | 1.8B | **635MB** | 54.35 | 9.12 | 16.5 | 2.51 | 19.42 | 9.64 | 18.59 |
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</details>
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<details>
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<summary class="bold"> For 3B scale models </summary>
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| Model | Nb Params | Mem Footprint | IFEVAL | Math-Hard | GPQA | MuSR | BBH | MMLU-Pro | Avg. |
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| -------- | ------- | ------- | ------- | ------ | ----- | ----- | ----- | ------ | ---- |
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| Falcon-3-3B-Instruct | 3B | 6.46GB | 69.77 | 25 | 26.29 | 11.13 | 22.28 | 5.15 | 26.6 |
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| Qwen2.5-3B-Instruct | 3B | 6.17GB | 64.75 | 36.78 | 25.8 | 7.57 | 25.05 | 3.02 | 27.16 |
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| Falcon-E-3B-Instruct | 3B | **999MB** | 60.97 | 15.3 | 23.59 | 2.12 | 26.45 | 7.45 | 22.64666667 |
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</details>
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## Useful links
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- View [our release blogpost](https://falcon-lm.github.io/blog/falcon-edge/).
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- Learn more about [`onebitllms` library](https://github.com/tiiuae/onebitllms).
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- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
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## Citation
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If the Falcon-E family of models were helpful to your work, feel free to give us a cite.
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```
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@misc{tiionebitllms,
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title = {Falcon-E, a series of powerful, universal and fine-tunable 1.58bit language models.},
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author = {Falcon-LLM Team},
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month = {April},
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url = {https://falcon-lm.github.io/blog/falcon-edge},
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year = {2025}
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}
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```
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