metadata

datasets:
  - tiiuae/falcon-refinedweb
language:
  - en
inference: true
widget:
  - text: Hey Falcon! Any recommendations for my holidays in Abu Dhabi?
    example_title: Abu Dhabi Trip
  - text: What's the Everett interpretation of quantum mechanics?
    example_title: 'Q/A: Quantum & Answers'
  - text: >-
      Give me a list of the top 10 dive sites you would recommend around the
      world.
    example_title: Diving Top 10
  - text: Can you tell me more about deep-water soloing?
    example_title: Extreme sports
  - text: >-
      Can you write a short tweet about the Apache 2.0 release of our latest AI
      model, Falcon LLM?
    example_title: Twitter Helper
  - text: What are the responsabilities of a Chief Llama Officer?
    example_title: Trendy Jobs
license: apache-2.0

I am continuously enhancing the structure of these model descriptions, and they now provide even more comprehensive information to help you find the best models for your specific needs.

falcon-7b-instruct - GGUF

Model creator: tiiuae
Original model: falcon-7b-instruct

Note: Important Update for Falcon Models in llama.cpp Versions After October 18, 2023

As noted on the [Llama.cpp](ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++ (github.com) GitHub repository, all new releases of Llama.cpp will require a re-quantization due to the implementation of the new BPE tokenizer. I am working diligently to make the updated models available for you.

Here's what you need to know:

Stay Informed: Application software using llama.cpp libraries will follow soon. Keep an eye on the release schedules of your favorite software applications that rely on llama.cpp. They will likely provide instructions on how to integrate the new models. Monitor Upload Times: Please keep a close watch on the upload times of the available files on my Hugging Face Model pages. This will help you identify which files have already been updated and are ready for download, ensuring you have the most current Falcon models at your disposal. Download Promptly: Once the updated Falcon models are available on my Hugging Face page, be sure to download them promptly to ensure compatibility with the latest [llama.cpp](ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++ (github.com) versions. Please understand that this change specifically affects Falcon and Starcoder models, other models remain unaffected. Consequently, software providers may not emphasize this change as prominently. As a solo operator of this page, I'm doing my best to expedite the process, but please bear with me as this may take some time.

Brief

Tiiuae-Falcon 7B instruct is the original instruction following Falcon model from Tiiuae, converted to gguf format.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available. How to choose the best for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants. Falcon 7B models cannot be quantized to K-quants.

K-quants

K-quants are based on the idea that the quantization of certain parts affects the quality in different ways. If you quantize certain parts more and others less, you get a more powerful model with the same file size, or a smaller file size and lower memory load with comparable performance. So, if possible, use K-quants. With a Q6_K you should find it really hard to find a quality difference to the original model - ask your model two times the same question and you may encounter bigger quality differences.

Original Model Card:

✨ Falcon-7B-Instruct

Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by TII based on Falcon-7B and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.

Paper coming soon 😊.

🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading this great blogpost fron HF!

Why use Falcon-7B-Instruct?

You are looking for a ready-to-use chat/instruct model based on Falcon-7B.
Falcon-7B is a strong base model, outperforming comparable open-source models (e.g., MPT-7B, StableLM, RedPajama etc.), thanks to being trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. See the OpenLLM Leaderboard.
It features an architecture optimized for inference, with FlashAttention (Dao et al., 2022) and multiquery (Shazeer et al., 2019).

💬 This is an instruct model, which may not be ideal for further finetuning. If you are interested in building your own instruct/chat model, we recommend starting from Falcon-7B.

🔥 Looking for an even more powerful model? Falcon-40B-Instruct is Falcon-7B-Instruct's big brother!

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "tiiuae/falcon-7b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
sequences = pipeline(
   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

💥 Falcon LLMs require PyTorch 2.0 for use with transformers!

For fast inference with Falcon, check-out Text Generation Inference! Read more in this blogpost.

You will need at least 16GB of memory to swiftly run inference with Falcon-7B-Instruct.

Model Card for Falcon-7B-Instruct

Model Details

Model Description

Developed by: https://www.tii.ae;
Model type: Causal decoder-only;
Language(s) (NLP): English and French;
License: Apache 2.0;
Finetuned from model: Falcon-7B.

Model Source

Paper: coming soon.

Uses

Direct Use

Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets.

Out-of-Scope Use

Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.

Bias, Risks, and Limitations

Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.

Recommendations

We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "tiiuae/falcon-7b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
sequences = pipeline(
   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Training Details

Training Data

Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets.

Data source	Fraction	Tokens	Description
Bai ze	65%	164M	chat
GPT4All	25%	62M	instruct
GPTeacher	5%	11M	instruct
RefinedWeb-English	5%	13M	massive web crawl

The data was tokenized with the Falcon-7B/40B tokenizer.

Evaluation

Paper coming soon.

See the OpenLLM Leaderboard for early results.

Note that this model variant is not optimized for NLP benchmarks.

Technical Specifications

For more information about pretraining, see Falcon-7B.

Model Architecture and Objective

Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).

The architecture is broadly adapted from the GPT-3 paper (Brown et al., 2020), with the following differences:

Positionnal embeddings: rotary (Su et al., 2021);
Attention: multiquery (Shazeer et al., 2019) and FlashAttention (Dao et al., 2022);
Decoder-block: parallel attention/MLP with a single layer norm.

Hyperparameter	Value	Comment
Layers	32
`d_model`	4544	Increased to compensate for multiquery
`head_dim`	64	Reduced to optimise for FlashAttention
Vocabulary	65024
Sequence length	2048

Compute Infrastructure

Hardware

Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.

Software

Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)

Citation

Paper coming soon 😊. In the meanwhile, you can use the following information to cite:

@article{falcon40b,
  title={{Falcon-40B}: an open large language model with state-of-the-art performance},
  author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
  year={2023}
}

To learn more about the pretraining dataset, see the 📓 RefinedWeb paper.

@article{refinedweb,
  title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
  author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
  journal={arXiv preprint arXiv:2306.01116},
  eprint={2306.01116},
  eprinttype = {arXiv},
  url={https://arxiv.org/abs/2306.01116},
  year={2023}
}

License

Falcon-7B-Instruct is made available under the Apache 2.0 license.

Contact

falconllm@tii.ae

End of original Model File

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Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.