falcon-40b / README.md
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metadata
datasets:
  - tiiuae/falcon-refinedweb
language:
  - en
  - de
  - es
  - fr
inference: false
license: tii-falcon-llm

πŸš€ Falcon-40B

Falcon-40B is a 40B parameters causal decoder-only model built by TII and trained on 1,000B tokens of RefinedWeb enhanced with curated corpora. It is made available under the TII Falcon LLM License.

Paper coming soon 😊.

Why use Falcon-40B?

⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases. If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at Falcon-40B-Instruct.

πŸ’Έ Looking for a smaller, less expensive model? Falcon-7B is Falcon-40B's little brother!

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "tiiuae/falcon-40b"

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']}")

πŸ’₯ Falcon LLMs require PyTorch 2.0 for use with transformers!

Model Card for Falcon-40B

Model Details

Model Description

  • Developed by: https://www.tii.ae;
  • Model type: Causal decoder-only;
  • Language(s) (NLP): English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
  • License: TII Falcon LLM License.

Model Source

  • Paper: coming soon.

Uses

Direct Use

Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)

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-40B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It 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-40B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "tiiuae/falcon-40b"

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-40B was trained on 1,000B tokens of RefinedWeb, a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile (Gao et al., 2020).

Data source Fraction Tokens Sources
RefinedWeb-English 75% 750B massive web crawl
RefinedWeb-Europe 7% 70B European massive web crawl
Books 6% 60B
Conversations 5% 50B Reddit, StackOverflow, HackerNews
Code 5% 50B
Technical 2% 20B arXiv, PubMed, UPSTO, etc.

RefinedWeb-Europe is made of the following languages:

Language Fraction of multilingual data Tokens
German 26% 18B
Spanish 24% 17B
French 23% 16B
Italian 7% 5B
Portuguese 4% 3B
Polish 4% 3B
Dutch 4% 3B
Romanian 3% 2B
Czech 3% 2B
Swedish 2% 1B

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

Training Procedure

Falcon-40B was trained on 384 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=4, DP=12) combined with ZeRO.

Training Hyperparameters

Hyperparameter Value Comment
Precision bfloat16
Optimizer AdamW
Learning rate 1.85e-4 4B tokens warm-up, cosine decay to 1.85e-5
Weight decay 1e-1
Z-loss 1e-4
Batch size 1152 100B tokens ramp-up

Speeds, Sizes, Times

Training started in December 2022 and took two months.

Evaluation

Paper coming soon.

See the OpenLLM Leaderboard for early results.

Technical Specifications

Model Architecture and Objective

Falcon-40B 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:

For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree.

Hyperparameter Value Comment
Layers 60
d_model 8192
head_dim 64 Reduced to optimise for FlashAttention
Vocabulary 65024
Sequence length 2048

Compute Infrastructure

Hardware

Falcon-40B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.

Software

Falcon-40B 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 😊.

License

Falcon-40B is made available under the TII Falcon LLM License. Broadly speaking,

  • You can freely use our models for research and/or personal purpose;
  • You are allowed to share and build derivatives of these models, but you are required to give attribution and to share-alike with the same license;
  • For commercial use, you are exempt from royalties payment if the attributable revenues are inferior to $1M/year, otherwise you should enter in a commercial agreement with TII.

Contact

falconllm@tii.ae