Gemma Model Card
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
--
See axolotl config
axolotl version: 0.4.0
1# use google/gemma-7b if you have access
2base_model: google/gemma-7b-it
3model_type: AutoModelForCausalLM
4tokenizer_type: AutoTokenizer
5
6load_in_8bit: false
7load_in_4bit: true
8strict: false
9
10# huggingface repo
11datasets:
12 - path: ./python-oasst/chunk_1.jsonl
13 type: oasst
14val_set_size: 0.1
15output_dir: ./out
16
17adapter: qlora
18lora_r: 32
19lora_alpha: 16
20lora_dropout: 0.05
21lora_target_linear: true
22
23sequence_len: 4096
24sample_packing: false
25pad_to_sequence_len: true
26
27wandb_project: gemma-7b-it
28wandb_entity:
29wandb_watch:
30wandb_name:
31wandb_log_model:
32
33gradient_accumulation_steps: 6
34micro_batch_size: 4
35num_epochs: 4
36optimizer: adamw_bnb_8bit
37lr_scheduler: cosine
38learning_rate: 0.0002
39
40train_on_inputs: true
41group_by_length: false
42bf16: auto
43fp16:
44tf32: false
45
46gradient_checkpointing: true
47early_stopping_patience:
48resume_from_checkpoint:
49local_rank:
50logging_steps: 1
51xformers_attention:
52flash_attention: true
53
54warmup_ratio: 0.1
55evals_per_epoch: 4
56eval_table_size:
57eval_max_new_tokens: 128
58saves_per_epoch: 1
59debug:
60deepspeed: deepspeed_configs/zero1.json
61weight_decay: 0.0
62fsdp:
63fsdp_config:
64special_tokens:
65
out
This model is a fine-tuned version of
google/gemma-7b-it on the None dataset.
It achieves the following results on the evaluation set:
This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the
2B base model,
7B instruct model, and
2B instruct model.
Resources and Technical Documentation:
Authors: Google
Model Information
Summary description and brief definition of inputs and outputs.
Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
Usage
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.
Fine-tuning examples
You can find fine-tuning notebooks under the
examples/ directory. We provide:
- A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
- A script to perform SFT using FSDP on TPU devices
- A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset. You can also find the copy of the notebook here.
Running the model on a CPU
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
4model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
5
6input_text = "Write me a poem about Machine Learning."
7input_ids = tokenizer(input_text, return_tensors="pt")
8
9outputs = model.generate(**input_ids)
10print(tokenizer.decode(outputs[0]))
Running the model on a single / multi GPU
1# pip install accelerate
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
5model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")
6
7input_text = "Write me a poem about Machine Learning."
8input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
9
10outputs = model.generate(**input_ids)
11print(tokenizer.decode(outputs[0]))
Running the model on a GPU using different precisions
1# pip install accelerate
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
5model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.float16)
6
7input_text = "Write me a poem about Machine Learning."
8input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
9
10outputs = model.generate(**input_ids)
11print(tokenizer.decode(outputs[0]))
1# pip install accelerate
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
5model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16)
6
7input_text = "Write me a poem about Machine Learning."
8input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
9
10outputs = model.generate(**input_ids)
11print(tokenizer.decode(outputs[0]))
Quantized Versions through bitsandbytes
- Using 8-bit precision (int8)
1# pip install bitsandbytes accelerate
2from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
3
4quantization_config = BitsAndBytesConfig(load_in_8bit=True)
5
6tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
7model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
8
9input_text = "Write me a poem about Machine Learning."
10input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
11
12outputs = model.generate(**input_ids)
13print(tokenizer.decode(outputs[0]))
1# pip install bitsandbytes accelerate
2from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
3
4quantization_config = BitsAndBytesConfig(load_in_4bit=True)
5
6tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
7model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
8
9input_text = "Write me a poem about Machine Learning."
10input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
11
12outputs = model.generate(**input_ids)
13print(tokenizer.decode(outputs[0]))
Other optimizations
First make sure to install flash-attn in your environment pip install flash-attn
1model = AutoModelForCausalLM.from_pretrained(
2 model_id,
3 torch_dtype=torch.float16,
4+ attn_implementation="flash_attention_2"
5).to(0)
Inputs and outputs
- Input: Text string, such as a question, a prompt, or a document to be
summarized.
- Output: Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
Model Data
Data used for model training and how the data was processed.
Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
- Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
- Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safely in line with
our policies.
Implementation Information
Details about the model internals.
Hardware
Gemma was trained using the latest generation of
Tensor Processing Unit (TPU) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
- These advantages are aligned with
Google's commitments to operate sustainably.
Software
Training was done using
JAX and
ML Pathways.
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
paper about the Gemini family of models; "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
Evaluation
Model evaluation metrics and results.
Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
Ethics and Safety
Ethics and safety evaluation approach and results.
Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
- Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
- Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as WinoBias and BBQ Dataset.
- Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
- Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting
internal policies for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
Usage and Limitations
These models have certain limitations that users should be aware of.
Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
- Research and Education
- Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
- Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
Limitations
- Training Data
- The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
- The scope of the training dataset determines the subject areas the model can
handle effectively.
- Context and Task Complexity
- LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
- A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
- Factual Accuracy
- LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
- Common Sense
- LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
- Bias and Fairness
- LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
- Misinformation and Misuse
- LLMs can be misused to generate text that is false, misleading, or harmful.
- Guidelines are provided for responsible use with the model, see the
Responsible Generative AI Toolkit.
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
- Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
- Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
- Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
Gemma Prohibited Use Policy.
- Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 9
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|
| 5.0474 | 0.01 | 1 | 5.9279 |
| 1.2191 | 0.26 | 24 | 1.2947 |
| 1.1165 | 0.51 | 48 | 1.1679 |
| 1.0711 | 0.77 | 72 | 1.1377 |
| 0.9546 | 1.02 | 96 | 1.1303 |
| 0.9309 | 1.28 | 120 | 1.1298 |
| 0.9588 | 1.54 | 144 | 1.1242 |
| 0.8553 | 1.79 | 168 | 1.1259 |
| 0.8231 | 2.05 | 192 | 1.1449 |
| 0.8154 | 2.31 | 216 | 1.1514 |
| 0.7354 | 2.56 | 240 | 1.1471 |
| 0.7577 | 2.82 | 264 | 1.1479 |
| 0.6647 | 3.07 | 288 | 1.1923 |
| 0.6928 | 3.33 | 312 | 1.1856 |
| 0.731 | 3.59 | 336 | 1.1890 |
| 0.7193 | 3.84 | 360 | 1.1911 |
Framework versions
- PEFT 0.9.0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0
