File size: 13,756 Bytes
ecfb868 c766d42 ecfb868 7129725 ecfb868 607b67f ecfb868 fdbd348 ecfb868 fdbd348 ecfb868 1952ac7 ecfb868 914a31a ecfb868 914a31a ecfb868 914a31a ecfb868 914a31a ecfb868 914a31a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 |
---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
---
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/UBgz4VXf">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? patreon.com/TheBlokeAI</a></p>
</div>
</div>
# Falcon-40B-Instruct GPTQ
This repo contains an experimantal GPTQ 4bit model for [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct).
It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
## Need support? Want to discuss? I now have a Discord!
Join me at: https://discord.gg/UBgz4VXf
Want to support me and help pay my cloud computing bill? I also now have a Patreon! https://www.patreon.com/TheBlokeAI
## EXPERIMENTAL
Please note this is an experimental first model. Support for it is currently quite limited.
To use it you will require:
1. AutoGPTQ, from the latest `main` branch and compiled with `pip install .`
2. `pip install einops`
You can then use it immediately from Python code - see example code below
## text-generation-webui
There is also provisional AutoGPTQ support in text-generation-webui.
However at the time I'm writing this, a commit is needed to text-generation-webui to enable it to load this model.
I have [opened a PR here](https://github.com/oobabooga/text-generation-webui/pull/2374); once this is merged, text-generation-webui will support this GPTQ model.
To get it working before the PR is merged, you will need to:
1. Edit `text-generation-webui/modules/AutoGPTQ_loader.py`
2. Make the following change:
Find the line that says:
```
'use_safetensors': use_safetensors,
```
And after it, add:
```
'trust_remote_code': shared.args.trust_remote_code,
```
[Once you are done the file should look like this](https://github.com/oobabooga/text-generation-webui/blob/473a57e35219c063d2fc230cfc7b5a118b448b38/modules/AutoGPTQ_loader.py#L33-L39)
3. Then save and close the file, and launch text-generation-webui as described below
4. Install and compile AutoGPTQ from source - note that this requires compiling the CUDA kernel, which requires CUDA toolkit. This may be an issue for Windows users.
```
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip install . # This step requires CUDA toolkit installed
```
## How to download and use this model in text-generation-webui
1. Launch text-generation-webui with the following command-line arguments: `--autogptq --trust_remote_code`
2. Click the **Model tab**.
3. Under **Download custom model or LoRA**, enter `TheBloke/falcon-7B-instruct-GPTQ`.
4. Click **Download**.
5. Wait until it says it's finished downloading.
6. Click the **Refresh** icon next to **Model** in the top left.
7. In the **Model drop-down**: choose the model you just downloaded, `falcon-7B-instruct-GPTQ`.
8. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
## About `trust_remote_code`
Please be aware that this command line argument causes Python code provided by Falcon to be executed on your machine.
This code is required at the moment because Falcon is too new to be supported by Hugging Face transformers. At some point in the future transformers will support the model natively, and then `trust_remote_code` will no longer be needed.
In this repo you can see two `.py` files - these are the files that get executed. They are copied from the base repo at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).
## Simple Python example code
To run this code you need to install AutoGPTQ from source:
```
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip install . # This step requires CUDA toolkit installed
```
And install einops:
```
pip install einops
```
You can then run this example code:
```python
import torch
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
# Download the model from HF and store it locally, then reference its location here:
quantized_model_dir = "/path/to/falcon7b-instruct-gptq"
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, use_fast=False)
model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0", use_triton=False, use_safetensors=True, torch_dtype=torch.float32, trust_remote_code=True)
prompt = "Write a story about llamas"
prompt_template = f"### Instruction: {prompt}\n### Response:"
tokens = tokenizer(prompt_template, return_tensors="pt").to("cuda:0").input_ids
output = model.generate(input_ids=tokens, max_new_tokens=100, do_sample=True, temperature=0.8)
print(tokenizer.decode(output[0]))
```
## Provided files
**gptq_model-4bit.safetensors**
This will work with AutoGPTQ as of commit `3cb1bf5` (`3cb1bf5a6d43a06dc34c6442287965d1838303d3`)
It was created with no groupsize to reduce VRAM requirements as much as possible, with `desc_act` (act-order) to increase inference quality.
* `gptq_model-4bit.safetensors`
* Works only with latest AutoGPTQ CUDA, compiled from source as of commit `3cb1bf5`
* At this time it does not work with AutoGPTQ Triton, but support will hopefully be added in time.
* Works with text-generation-webui using `--autogptq --trust_remote_code`
* At this time it does NOT work with one-click-installers
* Does not work with any version of GPTQ-for-LLaMa
* Parameters: Groupsize = 64. No act-order.
## Want to support my work?
I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.
So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI proejcts.
* Patreon: coming soon! (just awaiting approval)
* Ko-Fi: https://ko-fi.com/TheBlokeAI
# ✨ Original model card: Falcon-40B-Instruct
# ✨ Falcon-40B-Instruct
**Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) and finetuned on a mixture of [Baize](https://github.com/project-baize/baize-chatbot). It is made available under the [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b-instruct/blob/main/LICENSE.txt).**
*Paper coming soon 😊.*
## Why use Falcon-40B-Instruct?
* **You are looking for a ready-to-use chat/instruct model based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).**
* **Falcon-40B is the best open-source model available.** It outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
💬 **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-40B](https://huggingface.co/tiiuae/falcon-40b).
💸 **Looking for a smaller, less expensive model?** [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) is Falcon-40B-Instruct's small brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b-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']}")
```
# Model Card for Falcon-40B-Instruct
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-7b-instruct/blob/main/LICENSE.txt);
- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Falcon-40B-Instruct has been finetuned on a chat dataset.
### 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-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-40B-Instruct to develop guardrails and to take appropriate precautions for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b-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-40B-Instruct was finetuned on a 150M tokens from [Bai ze](https://github.com/project-baize/baize-chatbot) mixed with 5% of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) data.
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
For more information about pretraining, see [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).
### 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](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
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-Instruct was trained on AWS SageMaker, on 64 A100 40GB GPUs in P4d instances.
#### Software
Falcon-40B-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 😊.*
## License
Falcon-40B-Instruct is made available under the [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b-instruct/blob/main/LICENSE.txt). 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
|