Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +615 -0
- config.json +7 -0
- model.bin +3 -0
- special_tokens_map.json +34 -0
- tokenizer.json +3 -0
- tokenizer_config.json +1517 -0
- vocabulary.json +0 -0
.gitattributes
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README.md
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|
1 |
+
---
|
2 |
+
library_name: transformers
|
3 |
+
widget:
|
4 |
+
- messages:
|
5 |
+
- role: user
|
6 |
+
content: How does the brain work?
|
7 |
+
inference:
|
8 |
+
parameters:
|
9 |
+
max_new_tokens: 200
|
10 |
+
extra_gated_heading: Access Gemma on Hugging Face
|
11 |
+
extra_gated_prompt: >-
|
12 |
+
To access Gemma on Hugging Face, you’re required to review and agree to
|
13 |
+
Google’s usage license. To do this, please ensure you’re logged-in to Hugging
|
14 |
+
Face and click below. Requests are processed immediately.
|
15 |
+
extra_gated_button_content: Acknowledge license
|
16 |
+
license: gemma
|
17 |
+
---
|
18 |
+
|
19 |
+
# Gemma Model Card
|
20 |
+
|
21 |
+
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
|
22 |
+
|
23 |
+
This model card corresponds to the latest 2B instruct version of the Gemma model. Here you can find other models in the Gemma family:
|
24 |
+
|
25 |
+
| | Base | Instruct |
|
26 |
+
|----|----------------------------------------------------|----------------------------------------------------------------------|
|
27 |
+
| 2B | [gemma-2b](https://huggingface.co/google/gemma-2b) | [**gemma-1.1-2b-it**](https://huggingface.co/google/gemma-1.1-2b-it) |
|
28 |
+
| 7B | [gemma-7b](https://huggingface.co/google/gemma-7b) | [gemma-1.1-7b-it](https://huggingface.co/google/gemma-1.1-7b-it) |
|
29 |
+
|
30 |
+
|
31 |
+
**Release Notes**
|
32 |
+
|
33 |
+
This is Gemma 1.1 2B (IT), an update over the original instruction-tuned Gemma release.
|
34 |
+
|
35 |
+
Gemma 1.1 was trained using a novel RLHF method, leading to substantial gains on quality, coding capabilities, factuality, instruction following and multi-turn conversation quality. We also fixed a bug in multi-turn conversations, and made sure that model responses don't always start with `"Sure,"`.
|
36 |
+
|
37 |
+
We believe this release represents an improvement for most use cases, but we encourage users to test in their particular applications. The previous model [will continue to be available in the same repo](https://huggingface.co/google/gemma-2b-it). We appreciate the enthusiastic adoption of Gemma, and we continue to welcome all feedback from the community.
|
38 |
+
|
39 |
+
|
40 |
+
**Resources and Technical Documentation**:
|
41 |
+
|
42 |
+
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
|
43 |
+
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
|
44 |
+
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
|
45 |
+
|
46 |
+
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
|
47 |
+
|
48 |
+
**Authors**: Google
|
49 |
+
|
50 |
+
## Model Information
|
51 |
+
|
52 |
+
Summary description and brief definition of inputs and outputs.
|
53 |
+
|
54 |
+
### Description
|
55 |
+
|
56 |
+
Gemma is a family of lightweight, state-of-the-art open models from Google,
|
57 |
+
built from the same research and technology used to create the Gemini models.
|
58 |
+
They are text-to-text, decoder-only large language models, available in English,
|
59 |
+
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
|
60 |
+
models are well-suited for a variety of text generation tasks, including
|
61 |
+
question answering, summarization, and reasoning. Their relatively small size
|
62 |
+
makes it possible to deploy them in environments with limited resources such as
|
63 |
+
a laptop, desktop or your own cloud infrastructure, democratizing access to
|
64 |
+
state of the art AI models and helping foster innovation for everyone.
|
65 |
+
|
66 |
+
### Usage
|
67 |
+
|
68 |
+
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
|
69 |
+
|
70 |
+
#### Running the model on a CPU
|
71 |
+
|
72 |
+
As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary.
|
73 |
+
|
74 |
+
```python
|
75 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
76 |
+
import torch
|
77 |
+
|
78 |
+
tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
|
79 |
+
model = AutoModelForCausalLM.from_pretrained(
|
80 |
+
"google/gemma-1.1-2b-it",
|
81 |
+
torch_dtype=torch.bfloat16
|
82 |
+
)
|
83 |
+
|
84 |
+
input_text = "Write me a poem about Machine Learning."
|
85 |
+
input_ids = tokenizer(input_text, return_tensors="pt")
|
86 |
+
|
87 |
+
outputs = model.generate(**input_ids, max_new_tokens=50)
|
88 |
+
print(tokenizer.decode(outputs[0]))
|
89 |
+
```
|
90 |
+
|
91 |
+
#### Running the model on a single / multi GPU
|
92 |
+
|
93 |
+
|
94 |
+
```python
|
95 |
+
# pip install accelerate
|
96 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
97 |
+
import torch
|
98 |
+
|
99 |
+
tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
|
100 |
+
model = AutoModelForCausalLM.from_pretrained(
|
101 |
+
"google/gemma-1.1-2b-it",
|
102 |
+
device_map="auto",
|
103 |
+
torch_dtype=torch.bfloat16
|
104 |
+
)
|
105 |
+
|
106 |
+
input_text = "Write me a poem about Machine Learning."
|
107 |
+
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
|
108 |
+
|
109 |
+
outputs = model.generate(**input_ids)
|
110 |
+
print(tokenizer.decode(outputs[0]))
|
111 |
+
```
|
112 |
+
|
113 |
+
<a name="precisions"></a>
|
114 |
+
#### Running the model on a GPU using different precisions
|
115 |
+
|
116 |
+
The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision.
|
117 |
+
|
118 |
+
You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below.
|
119 |
+
|
120 |
+
* _Using `torch.float16`_
|
121 |
+
|
122 |
+
```python
|
123 |
+
# pip install accelerate
|
124 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
125 |
+
import torch
|
126 |
+
|
127 |
+
tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
|
128 |
+
model = AutoModelForCausalLM.from_pretrained(
|
129 |
+
"google/gemma-1.1-2b-it",
|
130 |
+
device_map="auto",
|
131 |
+
torch_dtype=torch.float16,
|
132 |
+
revision="float16",
|
133 |
+
)
|
134 |
+
|
135 |
+
input_text = "Write me a poem about Machine Learning."
|
136 |
+
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
|
137 |
+
|
138 |
+
outputs = model.generate(**input_ids)
|
139 |
+
print(tokenizer.decode(outputs[0]))
|
140 |
+
```
|
141 |
+
|
142 |
+
* _Using `torch.bfloat16`_
|
143 |
+
|
144 |
+
```python
|
145 |
+
# pip install accelerate
|
146 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
147 |
+
|
148 |
+
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
|
149 |
+
model = AutoModelForCausalLM.from_pretrained(
|
150 |
+
"google/gemma-1.1-2b-it",
|
151 |
+
device_map="auto",
|
152 |
+
torch_dtype=torch.bfloat16
|
153 |
+
)
|
154 |
+
|
155 |
+
input_text = "Write me a poem about Machine Learning."
|
156 |
+
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
|
157 |
+
|
158 |
+
outputs = model.generate(**input_ids)
|
159 |
+
print(tokenizer.decode(outputs[0]))
|
160 |
+
```
|
161 |
+
|
162 |
+
|
163 |
+
* _Upcasting to `torch.float32`_
|
164 |
+
|
165 |
+
```python
|
166 |
+
# pip install accelerate
|
167 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
168 |
+
|
169 |
+
tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
|
170 |
+
model = AutoModelForCausalLM.from_pretrained(
|
171 |
+
"google/gemma-1.1-2b-it",
|
172 |
+
device_map="auto"
|
173 |
+
)
|
174 |
+
|
175 |
+
input_text = "Write me a poem about Machine Learning."
|
176 |
+
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
|
177 |
+
|
178 |
+
outputs = model.generate(**input_ids)
|
179 |
+
print(tokenizer.decode(outputs[0]))
|
180 |
+
```
|
181 |
+
|
182 |
+
#### Quantized Versions through `bitsandbytes`
|
183 |
+
|
184 |
+
* _Using 8-bit precision (int8)_
|
185 |
+
|
186 |
+
```python
|
187 |
+
# pip install bitsandbytes accelerate
|
188 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
189 |
+
|
190 |
+
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
|
191 |
+
|
192 |
+
tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
|
193 |
+
model = AutoModelForCausalLM.from_pretrained(
|
194 |
+
"google/gemma-1.1-2b-it",
|
195 |
+
quantization_config=quantization_config
|
196 |
+
)
|
197 |
+
|
198 |
+
input_text = "Write me a poem about Machine Learning."
|
199 |
+
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
|
200 |
+
|
201 |
+
outputs = model.generate(**input_ids)
|
202 |
+
print(tokenizer.decode(outputs[0]))
|
203 |
+
```
|
204 |
+
|
205 |
+
* _Using 4-bit precision_
|
206 |
+
|
207 |
+
```python
|
208 |
+
# pip install bitsandbytes accelerate
|
209 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
210 |
+
|
211 |
+
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
|
212 |
+
|
213 |
+
tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
|
214 |
+
model = AutoModelForCausalLM.from_pretrained(
|
215 |
+
"google/gemma-1.1-2b-it",
|
216 |
+
quantization_config=quantization_config
|
217 |
+
)
|
218 |
+
|
219 |
+
input_text = "Write me a poem about Machine Learning."
|
220 |
+
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
|
221 |
+
|
222 |
+
outputs = model.generate(**input_ids)
|
223 |
+
print(tokenizer.decode(outputs[0]))
|
224 |
+
```
|
225 |
+
|
226 |
+
|
227 |
+
#### Other optimizations
|
228 |
+
|
229 |
+
* _Flash Attention 2_
|
230 |
+
|
231 |
+
First make sure to install `flash-attn` in your environment `pip install flash-attn`
|
232 |
+
|
233 |
+
```diff
|
234 |
+
model = AutoModelForCausalLM.from_pretrained(
|
235 |
+
model_id,
|
236 |
+
torch_dtype=torch.float16,
|
237 |
+
+ attn_implementation="flash_attention_2"
|
238 |
+
).to(0)
|
239 |
+
```
|
240 |
+
|
241 |
+
#### Running the model in JAX / Flax
|
242 |
+
|
243 |
+
Use the `flax` branch of the repository:
|
244 |
+
|
245 |
+
```python
|
246 |
+
import jax.numpy as jnp
|
247 |
+
from transformers import AutoTokenizer, FlaxGemmaForCausalLM
|
248 |
+
|
249 |
+
model_id = "google/gemma-1.1-2b-it"
|
250 |
+
|
251 |
+
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
252 |
+
tokenizer.padding_side = "left"
|
253 |
+
|
254 |
+
model, params = FlaxGemmaForCausalLM.from_pretrained(
|
255 |
+
model_id,
|
256 |
+
dtype=jnp.bfloat16,
|
257 |
+
revision="flax",
|
258 |
+
_do_init=False,
|
259 |
+
)
|
260 |
+
|
261 |
+
inputs = tokenizer("Valencia and Málaga are", return_tensors="np", padding=True)
|
262 |
+
output = model.generate(**inputs, params=params, max_new_tokens=20, do_sample=False)
|
263 |
+
output_text = tokenizer.batch_decode(output.sequences, skip_special_tokens=True)
|
264 |
+
```
|
265 |
+
|
266 |
+
[Check this notebook](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/jax_gemma.ipynb) for a comprehensive walkthrough on how to parallelize JAX inference.
|
267 |
+
|
268 |
+
|
269 |
+
### Chat Template
|
270 |
+
|
271 |
+
The instruction-tuned models use a chat template that must be adhered to for conversational use.
|
272 |
+
The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
|
273 |
+
|
274 |
+
Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
|
275 |
+
|
276 |
+
```py
|
277 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
278 |
+
import transformers
|
279 |
+
import torch
|
280 |
+
|
281 |
+
model_id = "google/gemma-1.1-2b-it"
|
282 |
+
dtype = torch.bfloat16
|
283 |
+
|
284 |
+
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
285 |
+
model = AutoModelForCausalLM.from_pretrained(
|
286 |
+
model_id,
|
287 |
+
device_map="cuda",
|
288 |
+
torch_dtype=dtype,
|
289 |
+
)
|
290 |
+
|
291 |
+
chat = [
|
292 |
+
{ "role": "user", "content": "Write a hello world program" },
|
293 |
+
]
|
294 |
+
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
|
295 |
+
```
|
296 |
+
|
297 |
+
At this point, the prompt contains the following text:
|
298 |
+
|
299 |
+
```
|
300 |
+
<bos><start_of_turn>user
|
301 |
+
Write a hello world program<end_of_turn>
|
302 |
+
<start_of_turn>model
|
303 |
+
```
|
304 |
+
|
305 |
+
As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
|
306 |
+
(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
|
307 |
+
the `<end_of_turn>` token.
|
308 |
+
|
309 |
+
You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
|
310 |
+
chat template.
|
311 |
+
|
312 |
+
After the prompt is ready, generation can be performed like this:
|
313 |
+
|
314 |
+
```py
|
315 |
+
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
|
316 |
+
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
|
317 |
+
```
|
318 |
+
|
319 |
+
### Fine-tuning
|
320 |
+
|
321 |
+
You can find some fine-tuning scripts under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt them to this model, simply change the model-id to `google/gemma-1.1-2b-it`.
|
322 |
+
|
323 |
+
We provide:
|
324 |
+
|
325 |
+
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
|
326 |
+
* A script to perform SFT using FSDP on TPU devices
|
327 |
+
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on the English quotes dataset
|
328 |
+
|
329 |
+
### Inputs and outputs
|
330 |
+
|
331 |
+
* **Input:** Text string, such as a question, a prompt, or a document to be
|
332 |
+
summarized.
|
333 |
+
* **Output:** Generated English-language text in response to the input, such
|
334 |
+
as an answer to a question, or a summary of a document.
|
335 |
+
|
336 |
+
## Model Data
|
337 |
+
|
338 |
+
Data used for model training and how the data was processed.
|
339 |
+
|
340 |
+
### Training Dataset
|
341 |
+
|
342 |
+
These models were trained on a dataset of text data that includes a wide variety
|
343 |
+
of sources, totaling 6 trillion tokens. Here are the key components:
|
344 |
+
|
345 |
+
* Web Documents: A diverse collection of web text ensures the model is exposed
|
346 |
+
to a broad range of linguistic styles, topics, and vocabulary. Primarily
|
347 |
+
English-language content.
|
348 |
+
* Code: Exposing the model to code helps it to learn the syntax and patterns of
|
349 |
+
programming languages, which improves its ability to generate code or
|
350 |
+
understand code-related questions.
|
351 |
+
* Mathematics: Training on mathematical text helps the model learn logical
|
352 |
+
reasoning, symbolic representation, and to address mathematical queries.
|
353 |
+
|
354 |
+
The combination of these diverse data sources is crucial for training a powerful
|
355 |
+
language model that can handle a wide variety of different tasks and text
|
356 |
+
formats.
|
357 |
+
|
358 |
+
### Data Preprocessing
|
359 |
+
|
360 |
+
Here are the key data cleaning and filtering methods applied to the training
|
361 |
+
data:
|
362 |
+
|
363 |
+
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
|
364 |
+
applied at multiple stages in the data preparation process to ensure the
|
365 |
+
exclusion of harmful and illegal content
|
366 |
+
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
|
367 |
+
reliable, automated techniques were used to filter out certain personal
|
368 |
+
information and other sensitive data from training sets.
|
369 |
+
* Additional methods: Filtering based on content quality and safely in line with
|
370 |
+
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
|
371 |
+
|
372 |
+
## Implementation Information
|
373 |
+
|
374 |
+
Details about the model internals.
|
375 |
+
|
376 |
+
### Hardware
|
377 |
+
|
378 |
+
Gemma was trained using the latest generation of
|
379 |
+
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
|
380 |
+
|
381 |
+
Training large language models requires significant computational power. TPUs,
|
382 |
+
designed specifically for matrix operations common in machine learning, offer
|
383 |
+
several advantages in this domain:
|
384 |
+
|
385 |
+
* Performance: TPUs are specifically designed to handle the massive computations
|
386 |
+
involved in training LLMs. They can speed up training considerably compared to
|
387 |
+
CPUs.
|
388 |
+
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
|
389 |
+
for the handling of large models and batch sizes during training. This can
|
390 |
+
lead to better model quality.
|
391 |
+
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
|
392 |
+
handling the growing complexity of large foundation models. You can distribute
|
393 |
+
training across multiple TPU devices for faster and more efficient processing.
|
394 |
+
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
|
395 |
+
solution for training large models compared to CPU-based infrastructure,
|
396 |
+
especially when considering the time and resources saved due to faster
|
397 |
+
training.
|
398 |
+
* These advantages are aligned with
|
399 |
+
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
|
400 |
+
|
401 |
+
### Software
|
402 |
+
|
403 |
+
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
|
404 |
+
|
405 |
+
JAX allows researchers to take advantage of the latest generation of hardware,
|
406 |
+
including TPUs, for faster and more efficient training of large models.
|
407 |
+
|
408 |
+
ML Pathways is Google's latest effort to build artificially intelligent systems
|
409 |
+
capable of generalizing across multiple tasks. This is specially suitable for
|
410 |
+
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
|
411 |
+
these ones.
|
412 |
+
|
413 |
+
Together, JAX and ML Pathways are used as described in the
|
414 |
+
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
|
415 |
+
controller' programming model of Jax and Pathways allows a single Python
|
416 |
+
process to orchestrate the entire training run, dramatically simplifying the
|
417 |
+
development workflow."
|
418 |
+
|
419 |
+
## Evaluation
|
420 |
+
|
421 |
+
Model evaluation metrics and results.
|
422 |
+
|
423 |
+
### Benchmark Results
|
424 |
+
|
425 |
+
The pre-trained base models were evaluated against a large collection of different datasets and
|
426 |
+
metrics to cover different aspects of text generation:
|
427 |
+
|
428 |
+
| Benchmark | Metric | 2B Params | 7B Params |
|
429 |
+
| ------------------------------ | ------------- | ----------- | --------- |
|
430 |
+
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
|
431 |
+
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
|
432 |
+
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
|
433 |
+
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 |
|
434 |
+
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
|
435 |
+
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
|
436 |
+
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
|
437 |
+
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
|
438 |
+
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
|
439 |
+
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
|
440 |
+
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
|
441 |
+
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 |
|
442 |
+
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
|
443 |
+
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
|
444 |
+
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
|
445 |
+
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
|
446 |
+
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
|
447 |
+
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
|
448 |
+
| ------------------------------ | ------------- | ----------- | --------- |
|
449 |
+
| **Average** | | **45.0** | **56.9** |
|
450 |
+
|
451 |
+
## Ethics and Safety
|
452 |
+
|
453 |
+
Ethics and safety evaluation approach and results.
|
454 |
+
|
455 |
+
### Evaluation Approach
|
456 |
+
|
457 |
+
Our evaluation methods include structured evaluations and internal red-teaming
|
458 |
+
testing of relevant content policies. Red-teaming was conducted by a number of
|
459 |
+
different teams, each with different goals and human evaluation metrics. These
|
460 |
+
models were evaluated against a number of different categories relevant to
|
461 |
+
ethics and safety, including:
|
462 |
+
|
463 |
+
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
|
464 |
+
policies including child sexual abuse and exploitation, harassment, violence
|
465 |
+
and gore, and hate speech.
|
466 |
+
* Text-to-Text Representational Harms: Benchmark against relevant academic
|
467 |
+
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
|
468 |
+
* Memorization: Automated evaluation of memorization of training data, including
|
469 |
+
the risk of personally identifiable information exposure.
|
470 |
+
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
|
471 |
+
biological, radiological, and nuclear (CBRN) risks.
|
472 |
+
|
473 |
+
### Evaluation Results
|
474 |
+
|
475 |
+
The results of ethics and safety evaluations are within acceptable thresholds
|
476 |
+
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
|
477 |
+
safety, content safety, representational harms, memorization, large-scale harms.
|
478 |
+
On top of robust internal evaluations, the results of well known safety
|
479 |
+
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
|
480 |
+
are shown here.
|
481 |
+
|
482 |
+
#### Gemma 1.0
|
483 |
+
|
484 |
+
| Benchmark | Metric | Gemma 1.0 IT 2B | Gemma 1.0 IT 7B |
|
485 |
+
| ------------------------ | ------------- | --------------- | --------------- |
|
486 |
+
| [RealToxicity][realtox] | average | 6.86 | 7.90 |
|
487 |
+
| [BOLD][bold] | | 45.57 | 49.08 |
|
488 |
+
| [CrowS-Pairs][crows] | top-1 | 45.82 | 51.33 |
|
489 |
+
| [BBQ Ambig][bbq] | 1-shot, top-1 | 62.58 | 92.54 |
|
490 |
+
| [BBQ Disambig][bbq] | top-1 | 54.62 | 71.99 |
|
491 |
+
| [Winogender][winogender] | top-1 | 51.25 | 54.17 |
|
492 |
+
| [TruthfulQA][truthfulqa] | | 44.84 | 31.81 |
|
493 |
+
| [Winobias 1_2][winobias] | | 56.12 | 59.09 |
|
494 |
+
| [Winobias 2_2][winobias] | | 91.10 | 92.23 |
|
495 |
+
| [Toxigen][toxigen] | | 29.77 | 39.59 |
|
496 |
+
| ------------------------ | ------------- | --------------- | --------------- |
|
497 |
+
|
498 |
+
#### Gemma 1.1
|
499 |
+
|
500 |
+
| Benchmark | Metric | Gemma 1.1 IT 2B | Gemma 1.1 IT 7B |
|
501 |
+
| ------------------------ | ------------- | --------------- | --------------- |
|
502 |
+
| [RealToxicity][realtox] | average | 7.03 | 8.04 |
|
503 |
+
| [BOLD][bold] | | 47.76 | |
|
504 |
+
| [CrowS-Pairs][crows] | top-1 | 45.89 | 49.67 |
|
505 |
+
| [BBQ Ambig][bbq] | 1-shot, top-1 | 58.97 | 86.06 |
|
506 |
+
| [BBQ Disambig][bbq] | top-1 | 53.90 | 85.08 |
|
507 |
+
| [Winogender][winogender] | top-1 | 50.14 | 57.64 |
|
508 |
+
| [TruthfulQA][truthfulqa] | | 44.24 | 45.34 |
|
509 |
+
| [Winobias 1_2][winobias] | | 55.93 | 59.22 |
|
510 |
+
| [Winobias 2_2][winobias] | | 89.46 | 89.2 |
|
511 |
+
| [Toxigen][toxigen] | | 29.64 | 38.75 |
|
512 |
+
| ------------------------ | ------------- | --------------- | --------------- |
|
513 |
+
|
514 |
+
|
515 |
+
## Usage and Limitations
|
516 |
+
|
517 |
+
These models have certain limitations that users should be aware of.
|
518 |
+
|
519 |
+
### Intended Usage
|
520 |
+
|
521 |
+
Open Large Language Models (LLMs) have a wide range of applications across
|
522 |
+
various industries and domains. The following list of potential uses is not
|
523 |
+
comprehensive. The purpose of this list is to provide contextual information
|
524 |
+
about the possible use-cases that the model creators considered as part of model
|
525 |
+
training and development.
|
526 |
+
|
527 |
+
* Content Creation and Communication
|
528 |
+
* Text Generation: These models can be used to generate creative text formats
|
529 |
+
such as poems, scripts, code, marketing copy, and email drafts.
|
530 |
+
* Chatbots and Conversational AI: Power conversational interfaces for customer
|
531 |
+
service, virtual assistants, or interactive applications.
|
532 |
+
* Text Summarization: Generate concise summaries of a text corpus, research
|
533 |
+
papers, or reports.
|
534 |
+
* Research and Education
|
535 |
+
* Natural Language Processing (NLP) Research: These models can serve as a
|
536 |
+
foundation for researchers to experiment with NLP techniques, develop
|
537 |
+
algorithms, and contribute to the advancement of the field.
|
538 |
+
* Language Learning Tools: Support interactive language learning experiences,
|
539 |
+
aiding in grammar correction or providing writing practice.
|
540 |
+
* Knowledge Exploration: Assist researchers in exploring large bodies of text
|
541 |
+
by generating summaries or answering questions about specific topics.
|
542 |
+
|
543 |
+
### Limitations
|
544 |
+
|
545 |
+
* Training Data
|
546 |
+
* The quality and diversity of the training data significantly influence the
|
547 |
+
model's capabilities. Biases or gaps in the training data can lead to
|
548 |
+
limitations in the model's responses.
|
549 |
+
* The scope of the training dataset determines the subject areas the model can
|
550 |
+
handle effectively.
|
551 |
+
* Context and Task Complexity
|
552 |
+
* LLMs are better at tasks that can be framed with clear prompts and
|
553 |
+
instructions. Open-ended or highly complex tasks might be challenging.
|
554 |
+
* A model's performance can be influenced by the amount of context provided
|
555 |
+
(longer context generally leads to better outputs, up to a certain point).
|
556 |
+
* Language Ambiguity and Nuance
|
557 |
+
* Natural language is inherently complex. LLMs might struggle to grasp subtle
|
558 |
+
nuances, sarcasm, or figurative language.
|
559 |
+
* Factual Accuracy
|
560 |
+
* LLMs generate responses based on information they learned from their
|
561 |
+
training datasets, but they are not knowledge bases. They may generate
|
562 |
+
incorrect or outdated factual statements.
|
563 |
+
* Common Sense
|
564 |
+
* LLMs rely on statistical patterns in language. They might lack the ability
|
565 |
+
to apply common sense reasoning in certain situations.
|
566 |
+
|
567 |
+
### Ethical Considerations and Risks
|
568 |
+
|
569 |
+
The development of large language models (LLMs) raises several ethical concerns.
|
570 |
+
In creating an open model, we have carefully considered the following:
|
571 |
+
|
572 |
+
* Bias and Fairness
|
573 |
+
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
|
574 |
+
biases embedded in the training material. These models underwent careful
|
575 |
+
scrutiny, input data pre-processing described and posterior evaluations
|
576 |
+
reported in this card.
|
577 |
+
* Misinformation and Misuse
|
578 |
+
* LLMs can be misused to generate text that is false, misleading, or harmful.
|
579 |
+
* Guidelines are provided for responsible use with the model, see the
|
580 |
+
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
|
581 |
+
* Transparency and Accountability:
|
582 |
+
* This model card summarizes details on the models' architecture,
|
583 |
+
capabilities, limitations, and evaluation processes.
|
584 |
+
* A responsibly developed open model offers the opportunity to share
|
585 |
+
innovation by making LLM technology accessible to developers and researchers
|
586 |
+
across the AI ecosystem.
|
587 |
+
|
588 |
+
Risks identified and mitigations:
|
589 |
+
|
590 |
+
* Perpetuation of biases: It's encouraged to perform continuous monitoring
|
591 |
+
(using evaluation metrics, human review) and the exploration of de-biasing
|
592 |
+
techniques during model training, fine-tuning, and other use cases.
|
593 |
+
* Generation of harmful content: Mechanisms and guidelines for content safety
|
594 |
+
are essential. Developers are encouraged to exercise caution and implement
|
595 |
+
appropriate content safety safeguards based on their specific product policies
|
596 |
+
and application use cases.
|
597 |
+
* Misuse for malicious purposes: Technical limitations and developer and
|
598 |
+
end-user education can help mitigate against malicious applications of LLMs.
|
599 |
+
Educational resources and reporting mechanisms for users to flag misuse are
|
600 |
+
provided. Prohibited uses of Gemma models are outlined in the
|
601 |
+
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
|
602 |
+
* Privacy violations: Models were trained on data filtered for removal of PII
|
603 |
+
(Personally Identifiable Information). Developers are encouraged to adhere to
|
604 |
+
privacy regulations with privacy-preserving techniques.
|
605 |
+
|
606 |
+
### Benefits
|
607 |
+
|
608 |
+
At the time of release, this family of models provides high-performance open
|
609 |
+
large language model implementations designed from the ground up for Responsible
|
610 |
+
AI development compared to similarly sized models.
|
611 |
+
|
612 |
+
Using the benchmark evaluation metrics described in this document, these models
|
613 |
+
have shown to provide superior performance to other, comparably-sized open model
|
614 |
+
alternatives.
|
615 |
+
|
config.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": "<bos>",
|
3 |
+
"eos_token": "<eos>",
|
4 |
+
"layer_norm_epsilon": 1e-06,
|
5 |
+
"multi_query_attention": true,
|
6 |
+
"unk_token": "<unk>"
|
7 |
+
}
|
model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:08e5707fbbf8c1b1e83c6f9938c9c266272a11b0fad5fdf0523944f661630c54
|
3 |
+
size 2510131524
|
special_tokens_map.json
ADDED
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<start_of_turn>",
|
4 |
+
"<end_of_turn>"
|
5 |
+
],
|
6 |
+
"bos_token": {
|
7 |
+
"content": "<bos>",
|
8 |
+
"lstrip": false,
|
9 |
+
"normalized": false,
|
10 |
+
"rstrip": false,
|
11 |
+
"single_word": false
|
12 |
+
},
|
13 |
+
"eos_token": {
|
14 |
+
"content": "<eos>",
|
15 |
+
"lstrip": false,
|
16 |
+
"normalized": false,
|
17 |
+
"rstrip": false,
|
18 |
+
"single_word": false
|
19 |
+
},
|
20 |
+
"pad_token": {
|
21 |
+
"content": "<pad>",
|
22 |
+
"lstrip": false,
|
23 |
+
"normalized": false,
|
24 |
+
"rstrip": false,
|
25 |
+
"single_word": false
|
26 |
+
},
|
27 |
+
"unk_token": {
|
28 |
+
"content": "<unk>",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false
|
33 |
+
}
|
34 |
+
}
|
tokenizer.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c15eb04bc5ad609fb26533e8525302c5640a945e5f67f65b7c849900acda7d99
|
3 |
+
size 17518497
|
tokenizer_config.json
ADDED
@@ -0,0 +1,1517 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
{
|
2 |
+
"add_bos_token": true,
|
3 |
+
"add_eos_token": false,
|
4 |
+
"added_tokens_decoder": {
|
5 |
+
"0": {
|
6 |
+
"content": "<pad>",
|
7 |
+
"lstrip": false,
|
8 |
+
"normalized": false,
|
9 |
+
"rstrip": false,
|
10 |
+
"single_word": false,
|
11 |
+
"special": true
|
12 |
+
},
|
13 |
+
"1": {
|
14 |
+
"content": "<eos>",
|
15 |
+
"lstrip": false,
|
16 |
+
"normalized": false,
|
17 |
+
"rstrip": false,
|
18 |
+
"single_word": false,
|
19 |
+
"special": true
|
20 |
+
},
|
21 |
+
"2": {
|
22 |
+
"content": "<bos>",
|
23 |
+
"lstrip": false,
|
24 |
+
"normalized": false,
|
25 |
+
"rstrip": false,
|
26 |
+
"single_word": false,
|
27 |
+
"special": true
|
28 |
+
},
|
29 |
+
"3": {
|
30 |
+
"content": "<unk>",
|
31 |
+
"lstrip": false,
|
32 |
+
"normalized": false,
|
33 |
+
"rstrip": false,
|
34 |
+
"single_word": false,
|
35 |
+
"special": true
|
36 |
+
},
|
37 |
+
"4": {
|
38 |
+
"content": "<mask>",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false,
|
43 |
+
"special": false
|
44 |
+
},
|
45 |
+
"5": {
|
46 |
+
"content": "<2mass>",
|
47 |
+
"lstrip": false,
|
48 |
+
"normalized": false,
|
49 |
+
"rstrip": false,
|
50 |
+
"single_word": false,
|
51 |
+
"special": false
|
52 |
+
},
|
53 |
+
"6": {
|
54 |
+
"content": "[@BOS@]",
|
55 |
+
"lstrip": false,
|
56 |
+
"normalized": false,
|
57 |
+
"rstrip": false,
|
58 |
+
"single_word": false,
|
59 |
+
"special": false
|
60 |
+
},
|
61 |
+
"7": {
|
62 |
+
"content": "<unused0>",
|
63 |
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"lstrip": false,
|
64 |
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"normalized": false,
|
65 |
+
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|
66 |
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"single_word": false,
|
67 |
+
"special": false
|
68 |
+
},
|
69 |
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"8": {
|
70 |
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"content": "<unused1>",
|
71 |
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"lstrip": false,
|
72 |
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|
73 |
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|
74 |
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"single_word": false,
|
75 |
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"special": false
|
76 |
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},
|
77 |
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"9": {
|
78 |
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"content": "<unused2>",
|
79 |
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"lstrip": false,
|
80 |
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|
81 |
+
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|
82 |
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"single_word": false,
|
83 |
+
"special": false
|
84 |
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},
|
85 |
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"10": {
|
86 |
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"content": "<unused3>",
|
87 |
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|
88 |
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|
89 |
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|
90 |
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|
91 |
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|
92 |
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},
|
93 |
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"11": {
|
94 |
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|
95 |
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|
96 |
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|
97 |
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|
98 |
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|
99 |
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|
100 |
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},
|
101 |
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"12": {
|
102 |
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|
103 |
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|
104 |
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|
105 |
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|
106 |
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|
107 |
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"special": false
|
108 |
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},
|
109 |
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"13": {
|
110 |
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|
111 |
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|
112 |
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|
113 |
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|
114 |
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|
115 |
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|
116 |
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},
|
117 |
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"14": {
|
118 |
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|
119 |
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|
120 |
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|
121 |
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|
122 |
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|
123 |
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|
124 |
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},
|
125 |
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"15": {
|
126 |
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|
127 |
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|
128 |
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|
129 |
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|
130 |
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|
131 |
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"special": false
|
132 |
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},
|
133 |
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"16": {
|
134 |
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|
135 |
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|
136 |
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|
137 |
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|
138 |
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|
139 |
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|
140 |
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},
|
141 |
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"17": {
|
142 |
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|
143 |
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|
144 |
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|
145 |
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|
146 |
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|
147 |
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"special": false
|
148 |
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},
|
149 |
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"18": {
|
150 |
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|
151 |
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|
152 |
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|
153 |
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|
154 |
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|
155 |
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"special": false
|
156 |
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},
|
157 |
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"19": {
|
158 |
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|
159 |
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|
160 |
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|
161 |
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|
162 |
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|
163 |
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|
164 |
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},
|
165 |
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"20": {
|
166 |
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|
167 |
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|
168 |
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|
169 |
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|
170 |
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|
171 |
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|
172 |
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},
|
173 |
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"21": {
|
174 |
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|
175 |
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|
176 |
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|
177 |
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|
178 |
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|
179 |
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"special": false
|
180 |
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},
|
181 |
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"22": {
|
182 |
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|
183 |
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|
184 |
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|
185 |
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|
186 |
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|
187 |
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|
188 |
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},
|
189 |
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"23": {
|
190 |
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|
191 |
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|
192 |
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|
193 |
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|
194 |
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|
195 |
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"special": false
|
196 |
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},
|
197 |
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"24": {
|
198 |
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|
199 |
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|
200 |
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|
201 |
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|
202 |
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|
203 |
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|
204 |
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},
|
205 |
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"25": {
|
206 |
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|
207 |
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|
208 |
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|
209 |
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|
210 |
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|
211 |
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|
212 |
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},
|
213 |
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"26": {
|
214 |
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|
215 |
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|
216 |
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|
217 |
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|
218 |
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|
219 |
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|
220 |
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},
|
221 |
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"27": {
|
222 |
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|
223 |
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|
224 |
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|
225 |
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|
226 |
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|
227 |
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|
228 |
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|
229 |
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"28": {
|
230 |
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|
231 |
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|
232 |
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|
233 |
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|
234 |
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|
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|
236 |
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|
237 |
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"29": {
|
238 |
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|
239 |
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|
240 |
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|
241 |
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|
242 |
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|
243 |
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|
244 |
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|
245 |
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|
246 |
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|
247 |
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|
248 |
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|
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|
250 |
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|
251 |
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|
252 |
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|
253 |
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|
254 |
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255 |
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|
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|
262 |
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|
263 |
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264 |
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265 |
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|
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1150 |
+
"content": "<tfoot>",
|
1151 |
+
"lstrip": false,
|
1152 |
+
"normalized": false,
|
1153 |
+
"rstrip": false,
|
1154 |
+
"single_word": false,
|
1155 |
+
"special": false
|
1156 |
+
},
|
1157 |
+
"174": {
|
1158 |
+
"content": "<tr>",
|
1159 |
+
"lstrip": false,
|
1160 |
+
"normalized": false,
|
1161 |
+
"rstrip": false,
|
1162 |
+
"single_word": false,
|
1163 |
+
"special": false
|
1164 |
+
},
|
1165 |
+
"175": {
|
1166 |
+
"content": "<th>",
|
1167 |
+
"lstrip": false,
|
1168 |
+
"normalized": false,
|
1169 |
+
"rstrip": false,
|
1170 |
+
"single_word": false,
|
1171 |
+
"special": false
|
1172 |
+
},
|
1173 |
+
"176": {
|
1174 |
+
"content": "<td>",
|
1175 |
+
"lstrip": false,
|
1176 |
+
"normalized": false,
|
1177 |
+
"rstrip": false,
|
1178 |
+
"single_word": false,
|
1179 |
+
"special": false
|
1180 |
+
},
|
1181 |
+
"177": {
|
1182 |
+
"content": "</table>",
|
1183 |
+
"lstrip": false,
|
1184 |
+
"normalized": false,
|
1185 |
+
"rstrip": false,
|
1186 |
+
"single_word": false,
|
1187 |
+
"special": false
|
1188 |
+
},
|
1189 |
+
"178": {
|
1190 |
+
"content": "</caption>",
|
1191 |
+
"lstrip": false,
|
1192 |
+
"normalized": false,
|
1193 |
+
"rstrip": false,
|
1194 |
+
"single_word": false,
|
1195 |
+
"special": false
|
1196 |
+
},
|
1197 |
+
"179": {
|
1198 |
+
"content": "</thead>",
|
1199 |
+
"lstrip": false,
|
1200 |
+
"normalized": false,
|
1201 |
+
"rstrip": false,
|
1202 |
+
"single_word": false,
|
1203 |
+
"special": false
|
1204 |
+
},
|
1205 |
+
"180": {
|
1206 |
+
"content": "</tbody>",
|
1207 |
+
"lstrip": false,
|
1208 |
+
"normalized": false,
|
1209 |
+
"rstrip": false,
|
1210 |
+
"single_word": false,
|
1211 |
+
"special": false
|
1212 |
+
},
|
1213 |
+
"181": {
|
1214 |
+
"content": "</tfoot>",
|
1215 |
+
"lstrip": false,
|
1216 |
+
"normalized": false,
|
1217 |
+
"rstrip": false,
|
1218 |
+
"single_word": false,
|
1219 |
+
"special": false
|
1220 |
+
},
|
1221 |
+
"182": {
|
1222 |
+
"content": "</tr>",
|
1223 |
+
"lstrip": false,
|
1224 |
+
"normalized": false,
|
1225 |
+
"rstrip": false,
|
1226 |
+
"single_word": false,
|
1227 |
+
"special": false
|
1228 |
+
},
|
1229 |
+
"183": {
|
1230 |
+
"content": "</th>",
|
1231 |
+
"lstrip": false,
|
1232 |
+
"normalized": false,
|
1233 |
+
"rstrip": false,
|
1234 |
+
"single_word": false,
|
1235 |
+
"special": false
|
1236 |
+
},
|
1237 |
+
"184": {
|
1238 |
+
"content": "</td>",
|
1239 |
+
"lstrip": false,
|
1240 |
+
"normalized": false,
|
1241 |
+
"rstrip": false,
|
1242 |
+
"single_word": false,
|
1243 |
+
"special": false
|
1244 |
+
},
|
1245 |
+
"185": {
|
1246 |
+
"content": "<h1>",
|
1247 |
+
"lstrip": false,
|
1248 |
+
"normalized": false,
|
1249 |
+
"rstrip": false,
|
1250 |
+
"single_word": false,
|
1251 |
+
"special": false
|
1252 |
+
},
|
1253 |
+
"186": {
|
1254 |
+
"content": "<h2>",
|
1255 |
+
"lstrip": false,
|
1256 |
+
"normalized": false,
|
1257 |
+
"rstrip": false,
|
1258 |
+
"single_word": false,
|
1259 |
+
"special": false
|
1260 |
+
},
|
1261 |
+
"187": {
|
1262 |
+
"content": "<h3>",
|
1263 |
+
"lstrip": false,
|
1264 |
+
"normalized": false,
|
1265 |
+
"rstrip": false,
|
1266 |
+
"single_word": false,
|
1267 |
+
"special": false
|
1268 |
+
},
|
1269 |
+
"188": {
|
1270 |
+
"content": "<h4>",
|
1271 |
+
"lstrip": false,
|
1272 |
+
"normalized": false,
|
1273 |
+
"rstrip": false,
|
1274 |
+
"single_word": false,
|
1275 |
+
"special": false
|
1276 |
+
},
|
1277 |
+
"189": {
|
1278 |
+
"content": "<h5>",
|
1279 |
+
"lstrip": false,
|
1280 |
+
"normalized": false,
|
1281 |
+
"rstrip": false,
|
1282 |
+
"single_word": false,
|
1283 |
+
"special": false
|
1284 |
+
},
|
1285 |
+
"190": {
|
1286 |
+
"content": "<h6>",
|
1287 |
+
"lstrip": false,
|
1288 |
+
"normalized": false,
|
1289 |
+
"rstrip": false,
|
1290 |
+
"single_word": false,
|
1291 |
+
"special": false
|
1292 |
+
},
|
1293 |
+
"191": {
|
1294 |
+
"content": "<blockquote>",
|
1295 |
+
"lstrip": false,
|
1296 |
+
"normalized": false,
|
1297 |
+
"rstrip": false,
|
1298 |
+
"single_word": false,
|
1299 |
+
"special": false
|
1300 |
+
},
|
1301 |
+
"192": {
|
1302 |
+
"content": "</h1>",
|
1303 |
+
"lstrip": false,
|
1304 |
+
"normalized": false,
|
1305 |
+
"rstrip": false,
|
1306 |
+
"single_word": false,
|
1307 |
+
"special": false
|
1308 |
+
},
|
1309 |
+
"193": {
|
1310 |
+
"content": "</h2>",
|
1311 |
+
"lstrip": false,
|
1312 |
+
"normalized": false,
|
1313 |
+
"rstrip": false,
|
1314 |
+
"single_word": false,
|
1315 |
+
"special": false
|
1316 |
+
},
|
1317 |
+
"194": {
|
1318 |
+
"content": "</h3>",
|
1319 |
+
"lstrip": false,
|
1320 |
+
"normalized": false,
|
1321 |
+
"rstrip": false,
|
1322 |
+
"single_word": false,
|
1323 |
+
"special": false
|
1324 |
+
},
|
1325 |
+
"195": {
|
1326 |
+
"content": "</h4>",
|
1327 |
+
"lstrip": false,
|
1328 |
+
"normalized": false,
|
1329 |
+
"rstrip": false,
|
1330 |
+
"single_word": false,
|
1331 |
+
"special": false
|
1332 |
+
},
|
1333 |
+
"196": {
|
1334 |
+
"content": "</h5>",
|
1335 |
+
"lstrip": false,
|
1336 |
+
"normalized": false,
|
1337 |
+
"rstrip": false,
|
1338 |
+
"single_word": false,
|
1339 |
+
"special": false
|
1340 |
+
},
|
1341 |
+
"197": {
|
1342 |
+
"content": "</h6>",
|
1343 |
+
"lstrip": false,
|
1344 |
+
"normalized": false,
|
1345 |
+
"rstrip": false,
|
1346 |
+
"single_word": false,
|
1347 |
+
"special": false
|
1348 |
+
},
|
1349 |
+
"198": {
|
1350 |
+
"content": "</blockquote>",
|
1351 |
+
"lstrip": false,
|
1352 |
+
"normalized": false,
|
1353 |
+
"rstrip": false,
|
1354 |
+
"single_word": false,
|
1355 |
+
"special": false
|
1356 |
+
},
|
1357 |
+
"199": {
|
1358 |
+
"content": "<strong>",
|
1359 |
+
"lstrip": false,
|
1360 |
+
"normalized": false,
|
1361 |
+
"rstrip": false,
|
1362 |
+
"single_word": false,
|
1363 |
+
"special": false
|
1364 |
+
},
|
1365 |
+
"200": {
|
1366 |
+
"content": "<em>",
|
1367 |
+
"lstrip": false,
|
1368 |
+
"normalized": false,
|
1369 |
+
"rstrip": false,
|
1370 |
+
"single_word": false,
|
1371 |
+
"special": false
|
1372 |
+
},
|
1373 |
+
"201": {
|
1374 |
+
"content": "<b>",
|
1375 |
+
"lstrip": false,
|
1376 |
+
"normalized": false,
|
1377 |
+
"rstrip": false,
|
1378 |
+
"single_word": false,
|
1379 |
+
"special": false
|
1380 |
+
},
|
1381 |
+
"202": {
|
1382 |
+
"content": "<i>",
|
1383 |
+
"lstrip": false,
|
1384 |
+
"normalized": false,
|
1385 |
+
"rstrip": false,
|
1386 |
+
"single_word": false,
|
1387 |
+
"special": false
|
1388 |
+
},
|
1389 |
+
"203": {
|
1390 |
+
"content": "<u>",
|
1391 |
+
"lstrip": false,
|
1392 |
+
"normalized": false,
|
1393 |
+
"rstrip": false,
|
1394 |
+
"single_word": false,
|
1395 |
+
"special": false
|
1396 |
+
},
|
1397 |
+
"204": {
|
1398 |
+
"content": "<s>",
|
1399 |
+
"lstrip": false,
|
1400 |
+
"normalized": false,
|
1401 |
+
"rstrip": false,
|
1402 |
+
"single_word": false,
|
1403 |
+
"special": false
|
1404 |
+
},
|
1405 |
+
"205": {
|
1406 |
+
"content": "<sub>",
|
1407 |
+
"lstrip": false,
|
1408 |
+
"normalized": false,
|
1409 |
+
"rstrip": false,
|
1410 |
+
"single_word": false,
|
1411 |
+
"special": false
|
1412 |
+
},
|
1413 |
+
"206": {
|
1414 |
+
"content": "<sup>",
|
1415 |
+
"lstrip": false,
|
1416 |
+
"normalized": false,
|
1417 |
+
"rstrip": false,
|
1418 |
+
"single_word": false,
|
1419 |
+
"special": false
|
1420 |
+
},
|
1421 |
+
"207": {
|
1422 |
+
"content": "<code>",
|
1423 |
+
"lstrip": false,
|
1424 |
+
"normalized": false,
|
1425 |
+
"rstrip": false,
|
1426 |
+
"single_word": false,
|
1427 |
+
"special": false
|
1428 |
+
},
|
1429 |
+
"208": {
|
1430 |
+
"content": "</strong>",
|
1431 |
+
"lstrip": false,
|
1432 |
+
"normalized": false,
|
1433 |
+
"rstrip": false,
|
1434 |
+
"single_word": false,
|
1435 |
+
"special": false
|
1436 |
+
},
|
1437 |
+
"209": {
|
1438 |
+
"content": "</em>",
|
1439 |
+
"lstrip": false,
|
1440 |
+
"normalized": false,
|
1441 |
+
"rstrip": false,
|
1442 |
+
"single_word": false,
|
1443 |
+
"special": false
|
1444 |
+
},
|
1445 |
+
"210": {
|
1446 |
+
"content": "</b>",
|
1447 |
+
"lstrip": false,
|
1448 |
+
"normalized": false,
|
1449 |
+
"rstrip": false,
|
1450 |
+
"single_word": false,
|
1451 |
+
"special": false
|
1452 |
+
},
|
1453 |
+
"211": {
|
1454 |
+
"content": "</i>",
|
1455 |
+
"lstrip": false,
|
1456 |
+
"normalized": false,
|
1457 |
+
"rstrip": false,
|
1458 |
+
"single_word": false,
|
1459 |
+
"special": false
|
1460 |
+
},
|
1461 |
+
"212": {
|
1462 |
+
"content": "</u>",
|
1463 |
+
"lstrip": false,
|
1464 |
+
"normalized": false,
|
1465 |
+
"rstrip": false,
|
1466 |
+
"single_word": false,
|
1467 |
+
"special": false
|
1468 |
+
},
|
1469 |
+
"213": {
|
1470 |
+
"content": "</s>",
|
1471 |
+
"lstrip": false,
|
1472 |
+
"normalized": false,
|
1473 |
+
"rstrip": false,
|
1474 |
+
"single_word": false,
|
1475 |
+
"special": false
|
1476 |
+
},
|
1477 |
+
"214": {
|
1478 |
+
"content": "</sub>",
|
1479 |
+
"lstrip": false,
|
1480 |
+
"normalized": false,
|
1481 |
+
"rstrip": false,
|
1482 |
+
"single_word": false,
|
1483 |
+
"special": false
|
1484 |
+
},
|
1485 |
+
"215": {
|
1486 |
+
"content": "</sup>",
|
1487 |
+
"lstrip": false,
|
1488 |
+
"normalized": false,
|
1489 |
+
"rstrip": false,
|
1490 |
+
"single_word": false,
|
1491 |
+
"special": false
|
1492 |
+
},
|
1493 |
+
"216": {
|
1494 |
+
"content": "</code>",
|
1495 |
+
"lstrip": false,
|
1496 |
+
"normalized": false,
|
1497 |
+
"rstrip": false,
|
1498 |
+
"single_word": false,
|
1499 |
+
"special": false
|
1500 |
+
}
|
1501 |
+
},
|
1502 |
+
"additional_special_tokens": [
|
1503 |
+
"<start_of_turn>",
|
1504 |
+
"<end_of_turn>"
|
1505 |
+
],
|
1506 |
+
"bos_token": "<bos>",
|
1507 |
+
"chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
|
1508 |
+
"clean_up_tokenization_spaces": false,
|
1509 |
+
"eos_token": "<eos>",
|
1510 |
+
"model_max_length": 1000000000000000019884624838656,
|
1511 |
+
"pad_token": "<pad>",
|
1512 |
+
"sp_model_kwargs": {},
|
1513 |
+
"spaces_between_special_tokens": false,
|
1514 |
+
"tokenizer_class": "GemmaTokenizer",
|
1515 |
+
"unk_token": "<unk>",
|
1516 |
+
"use_default_system_prompt": false
|
1517 |
+
}
|
vocabulary.json
ADDED
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|
|