Duplicate from aegon-h/finetuned-gemma-2b

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README.md

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+---
+library_name: transformers
+tags: []
+extra_gated_heading: "Access Gemma on Hugging Face"
+extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately."
+extra_gated_button_content: "Acknowledge license"
+license: other
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
+inference: false
+---
+
+# Gemma Model Card
+
+**Original Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
+
+This model card corresponds to the 2B base version of the Gemma model. 
+
+**Original Resources and Technical Documentation**:
+
+* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
+* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
+* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
+
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
+
+**Original Authors**: Google
+
+
+### 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.
+
+
+
+
+#### Running the model on a CPU
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(**input_text, return_tensors="pt")
+
+outputs = model.generate(input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a single / multi GPU
+
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a GPU using different precisions
+
+* _Using `torch.float16`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+* _Using `torch.bfloat16`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+#### Quantized Versions through `bitsandbytes`
+
+* _Using 8-bit precision (int8)_
+
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+* _Using 4-bit precision_
+
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Other optimizations
+
+* _Flash Attention 2_
+
+First make sure to install `flash-attn` in your environment `pip install flash-attn`
+
+```diff
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
++   attn_implementation="flash_attention_2"
+).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.
+
+
+
+## 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](http://ai.google.dev/gemma/responsible).
+* 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](https://ai.google.dev/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.
+
+### Benefits
+
+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.
+

config.json

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+{
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "head_dim": 256,
+  "hidden_act": "gelu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.0.dev0",
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

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+  "bos_token_id": 2,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.38.0.dev0"
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