datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
Falcon 7B-Instruct GGML
These files are experimental GGML format model files for Falcon 7B Instruct.
These GGML files will not work in llama.cpp, and at the time of writing they will not work with any UI or library. They cannot be used from Python code.
They can be used with a new fork of llama.cpp that adds Falcon GGML support: cmp-nc/ggllm.cpp
Note: It is not currently possible to use the new k-quant formats with Falcon 7B. This is being worked on.
Repositories available
- 4-bit GPTQ models for GPU inference
- 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
- Unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Compatibility
To build cmp-nct's fork of llama.cpp with Falcon 7B support plus preliminary CUDA acceleration, please try the following steps:
git clone https://github.com/cmp-nct/ggllm.cpp
cd ggllm.cpp
rm -rf build && mkdir build && cd build && cmake -DGGML_CUBLAS=1 .. && cmake --build . --config Release
Compiling on Windows: developer cmp-nct notes: 'I personally compile it using VScode. When compiling with CUDA support using the Microsoft compiler it's essential to select the "Community edition build tools". Otherwise CUDA won't compile.'
Once compiled you can then use bin/falcon_main just like you would use llama.cpp. For example:
bin/falcon_main -t 8 -ngl 100 -b 1 -m falcon7b-instruct.ggmlv3.q4_0.bin -p "What is a falcon?\n### Response:"
You can specify -ngl 100 regardles of your VRAM, as it will automatically detect how much VRAM is available to be used.
Adjust -t 8 (the number of CPU cores to use) according to what performs best on your system. Do not exceed the number of physical CPU cores you have.
-b 1 reduces batch size to 1. This slightly lowers prompt evaluation time, but frees up VRAM to load more of the model on to your GPU. If you find prompt evaluation too slow and have enough spare VRAM, you can remove this parameter.
Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| falcon7b-instruct.ggmlv3.q4_0.bin | q4_0 | 4 | 4.06 GB | 6.56 GB | 4-bit. |
| falcon7b-instruct.ggmlv3.q4_1.bin | q4_1 | 4 | 4.51 GB | 7.01 GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| falcon7b-instruct.ggmlv3.q5_0.bin | q5_0 | 5 | 4.96 GB | 7.46 GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
| falcon7b-instruct.ggmlv3.q5_1.bin | q5_1 | 5 | 5.41 GB | 7.91 GB | 5-bit. Even higher accuracy, resource usage and slower inference. |
| falcon7b-instruct.ggmlv3.q8_0.bin | q8_0 | 8 | 7.67 GB | 10.17 GB | 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
| falcon7b-instruct.ggmlv3.fp16.bin | fp16 | 16 | 14.44 GB | 16.94 GB | 16-bit. Included for further conversions and for experimentation. Not recommended for normal use. |
Notes:
- the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
- It is not currently possible to use the new k-quant formats with Falcon 7B. This is being worked on.
Discord
For further support, and discussions on these models and AI in general, join us at:
Thanks, and how to contribute.
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Original model card: Falcon 7B-Instruct GGML
✨ 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.