datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
β¨ 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 TII Falcon LLM License.
Paper coming soon π.
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).
π¬ 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.
π₯ 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']}")
π₯ Falcon LLMs require PyTorch 2.0 for use with transformers
!
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: TII Falcon LLM License;
- 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 π.
License
Falcon-7B-Instruct 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.