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	---
	library_name: transformers
	license: gemma
	license_link: https://ai.google.dev/gemma/terms
	pipeline_tag: text-generation
	tags:
	- GGUF
	extra_gated_heading: Access CodeGemma on Hugging Face
	extra_gated_prompt: To access CodeGemma 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
	widget:
	- text: '<start_of_turn>user Write a Python function to calculate the nth fibonacci
	number.<end_of_turn> <start_of_turn>model

	'
	inference:
	parameters:
	max_new_tokens: 200
	quantized_by: andrijdavid
	---
	# codegemma-7b-it-GGUF
	- Original model: [codegemma-7b-it](https://huggingface.co/google/codegemma-7b-it)

	<!-- description start -->
	## Description

	This repo contains GGUF format model files for [codegemma-7b-it](https://huggingface.co/google/codegemma-7b-it).

	<!-- description end -->
	<!-- README_GGUF.md-about-gguf start -->
	### About GGUF
	GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
	Here is an incomplete list of clients and libraries that are known to support GGUF:
	* [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
	* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
	* [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
	* [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
	* [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
	* [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
	* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
	* [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
	* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
	* [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
	* [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
	* [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
	<!-- README_GGUF.md-about-gguf end -->

	<!-- compatibility_gguf start -->
	## Explanation of quantisation methods
	<details>
	<summary>Click to see details</summary>
	The new methods available are:

	* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
	* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
	* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
	* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
	* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.
	</details>
	<!-- compatibility_gguf end -->

	<!-- README_GGUF.md-how-to-download start -->
	## How to download GGUF files

	Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.

	The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

	* LM Studio
	* LoLLMS Web UI
	* Faraday.dev

	### In `text-generation-webui`

	Under Download Model, you can enter the model repo: LiteLLMs/codegemma-7b-it-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00001.gguf.

	Then click Download.

	### On the command line, including multiple files at once

	I recommend using the `huggingface-hub` Python library:

	```shell
	pip3 install huggingface-hub
	```

	Then you can download any individual model file to the current directory, at high speed, with a command like this:

	```shell
	huggingface-cli download LiteLLMs/codegemma-7b-it-GGUF Q4_0/Q4_0-00001-of-00001.gguf --local-dir . --local-dir-use-symlinks False
	```

	<details>
	<summary>More advanced huggingface-cli download usage (click to read)</summary>

	You can also download multiple files at once with a pattern:

	```shell
	huggingface-cli download LiteLLMs/codegemma-7b-it-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf'
	```

	For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).

	To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:

	```shell
	pip3 install huggingface_hub[hf_transfer]
	```

	And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:

	```shell
	HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/codegemma-7b-it-GGUF Q4_0/Q4_0-00001-of-00001.gguf --local-dir . --local-dir-use-symlinks False
	```

	Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
	</details>
	<!-- README_GGUF.md-how-to-download end -->
	<!-- README_GGUF.md-how-to-run start -->
	## Example `llama.cpp` command

	Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

	```shell
	./main -ngl 35 -m Q4_0/Q4_0-00001-of-00001.gguf --color -c --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
	```

	Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

	Change `-c ` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

	If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

	For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

	## How to run in `text-generation-webui`

	Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

	## How to run from Python code

	You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

	### How to load this model in Python code, using llama-cpp-python

	For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).

	#### First install the package

	Run one of the following commands, according to your system:

	```shell
	# Base ctransformers with no GPU acceleration
	pip install llama-cpp-python
	# With NVidia CUDA acceleration
	CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
	# Or with OpenBLAS acceleration
	CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
	# Or with CLBLast acceleration
	CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
	# Or with AMD ROCm GPU acceleration (Linux only)
	CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
	# Or with Metal GPU acceleration for macOS systems only
	CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
	# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
	$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
	pip install llama-cpp-python
	```

	#### Simple llama-cpp-python example code

	```python
	from llama_cpp import Llama
	# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
	llm = Llama(
	model_path="./Q4_0/Q4_0-00001-of-00001.gguf", # Download the model file first
	n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
	n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
	n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
	)
	# Simple inference example
	output = llm(
	"<PROMPT>", # Prompt
	max_tokens=512, # Generate up to 512 tokens
	stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
	echo=True # Whether to echo the prompt
	)
	# Chat Completion API
	llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00001.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
	llm.create_chat_completion(
	messages = [
	{"role": "system", "content": "You are a story writing assistant."},
	{
	"role": "user",
	"content": "Write a story about llamas."
	}
	]
	)
	```

	## How to use with LangChain

	Here are guides on using llama-cpp-python and ctransformers with LangChain:

	* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
	* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

	<!-- README_GGUF.md-how-to-run end -->

	<!-- footer end -->

	<!-- original-model-card start -->
	# Original model card: codegemma-7b-it


	# CodeGemma

	Model Page
	: [CodeGemma](https://ai.google.dev/gemma/docs/codegemma)

	Resources and Technical Documentation
	: [Technical Report](https://goo.gle/codegemma)
	: [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)

	Terms of Use
	: [Terms](https://www.kaggle.com/models/google/codegemma/license/consent/verify/huggingface?returnModelRepoId=google/codegemma-7b-it)

	Authors
	: Google

	## Model Information

	Summary description and brief definition of inputs and outputs.

	### Description

	CodeGemma is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion.

	\| \| [codegemma-2b](https://huggingface.co/google/codegemma-2b) \| [codegemma-7b](https://huggingface.co/google/codegemma-7b) \| [codegemma-7b-it](https://huggingface.co/google/codegemma-7b-it) \|
	\| -- \| :--: \| :--: \| :: \|
	\| Code Completion \| ✅ \| ✅ \| \|
	\| Generation from natural language \| \| ✅ \| ✅ \|
	\| Chat \| \| \| ✅ \|
	\| Instruction Following \| \| \| ✅ \|

	### Sample Usage

	This model is intended to answer questions about code fragments, to generate code from natural language, or to engage in a conversation with the user about programming or technical problems. If you need to use code completion (for example, integrated in an IDE), we recommend you use one of the pre-trained models instead: [CodeGemma 7B](https://huggingface.co/google/codegemma-7b), or [CodeGemma 2B](https://huggingface.co/google/codegemma-2b).

	#### For Code Generation

	```python
	from transformers import GemmaTokenizer, AutoModelForCausalLM

	tokenizer = GemmaTokenizer.from_pretrained("google/codegemma-7b-it")
	model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it")

	input_text = "Write me a Python function to calculate the nth fibonacci number."
	input_ids = tokenizer(input_text, return_tensors="pt")

	outputs = model.generate(**input_ids)
	print(tokenizer.decode(outputs[0]))
	```

	#### Chat Template

	The instruction-tuned models use a chat template that must be adhered to for conversational use.
	The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.

	Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:

	```py
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import transformers
	import torch

	model_id = "google/codegemma-7b-it"
	dtype = torch.bfloat16

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="cuda",
	torch_dtype=dtype,
	)

	chat = [
	{ "role": "user", "content": "Write a hello world program" },
	]

	prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
	```

	At this point, the prompt contains the following text:

	```
	<bos><start_of_turn>user
	Write a hello world program<end_of_turn>
	<start_of_turn>model
	```

	As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
	(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
	the `<end_of_turn>` token.

	You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
	chat template.

	After the prompt is ready, generation can be performed like this:

	```py
	inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
	outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
	```

	### Inputs and Outputs

	Inputs
	: For pretrained model variants: code prefix and/or suffix for code completion and generation scenarios, or natural language text or prompt
	: For instruction tuned model variant: natural language text or prompt

	Outputs
	: For pretrained model variants: fill-in-the-middle code completion, code and natural language
	: For instruction tuned model variant: code and natural language

	## Model Data

	Data used for model training and how the data was processed.

	### Training Dataset

	Using Gemma as the base model, CodeGemma 2B and 7B pretrained variants are further trained on an additional 500 billion tokens of primarily English language data from publicly available code repositories, open source mathematics datasets and synthetically generated code.

	### Training Data Processing

	The following data pre-processing techniques were applied:

	* FIM Pretrained CodeGemma models focus on fill-in-the-middle (FIM) tasks. The models are trained to work with both PSM and SPM modes. Our FIM settings are 80% FIM rate with 50-50 PSM/SPM.
	* Dependency Graph-based Packing and Unit Test-based Lexical Packing techniques: To improve model alignment with real-world applications, we structured training examples at the project/repository level to co-locate the most relevant source files within each repository. Specifically, we employed two heuristic techniques: dependency graph-based packing and unit test-based lexical packing
	* We developed a novel technique for splitting the documents into prefix, middle, and suffix to make the suffix start in a more syntactically natural point rather than purely random distribution.
	* Safety: Similarly to Gemma, we deployed rigorous safety filtering including filtering personal data, CSAM filtering and other filtering based on content quality and safety in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).

	## Implementation Information

	Information about the hardware and software used to train the models.

	### Hardware

	CodeGemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).

	### Software

	Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/).

	## Evaluation Information

	Model evaluation metrics and results.

	### Evaluation Approach

	We evaluate CodeGemma on a variety of academic benchmarks across several domains:

	* Code completion benchmarks: HumanEval Single Line and Multiple Line Infilling
	* Code generation benchmarks: HumanEval, MBPP, BabelCode (C++, C#, Go, Java, JavaScript, Kotlin, Python, Rust)
	* Q&A: BoolQ, PIQA, TriviaQA
	* Natural Language: ARC-Challenge, HellaSwag, MMLU, WinoGrande
	* Math Reasoning: GSM8K, MATH

	### Evaluation Results

	#### Coding Benchmarks

	\| Benchmark \| 2B \| 7B \| 7B-IT \|
	\| -- \| -- \|
	\| HumanEval \| 31.1 \| 44.5 \| 56.1 \|
	\| MBPP \| 43.6 \| 56.2 \| 54.2 \|
	\| HumanEval Single Line \| 78.41 \| 76.09 \| 68.25 \|
	\| HumanEval Multi Line \| 51.44 \| 58.44 \| 20.05 \|
	\| BC HE C++ \| 24.2 \| 32.9 \| 42.2 \|
	\| BC HE C# \| 10.6 \| 22.4 \| 26.7 \|
	\| BC HE Go \| 20.5 \| 21.7 \| 28.6 \|
	\| BC HE Java \| 29.2 \| 41.0 \| 48.4 \|
	\| BC HE JavaScript \| 21.7 \| 39.8 \| 46.0 \|
	\| BC HE Kotlin \| 28.0 \| 39.8 \| 51.6 \|
	\| BC HE Python \| 21.7 \| 42.2 \| 48.4 \|
	\| BC HE Rust \| 26.7 \| 34.1 \| 36.0 \|
	\| BC MBPP C++ \| 47.1 \| 53.8 \| 56.7 \|
	\| BC MBPP C# \| 28.7 \| 32.5 \| 41.2 \|
	\| BC MBPP Go \| 45.6 \| 43.3 \| 46.2 \|
	\| BC MBPP Java \| 41.8 \| 50.3 \| 57.3 \|
	\| BC MBPP JavaScript \| 45.3 \| 58.2 \| 61.4 \|
	\| BC MBPP Kotlin \| 46.8 \| 54.7 \| 59.9 \|
	\| BC MBPP Python \| 38.6 \| 59.1 \| 62.0 \|
	\| BC MBPP Rust \| 45.3 \| 52.9 \| 53.5 \|

	#### Natural Language Benchmarks

	![CodeGemma Natural Language Benchmarks](./codegemma_nl_benchmarks.png)

	## 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:

	* Human evaluation on prompts covering content safety and representational harms. See the [Gemma model card](https://ai.google.dev/gemma/docs/model_card#evaluation_approach) for more details on evaluation approach.
	* Specific testing of cyber-offence capabilities, focusing on testing autonomous hacking capabilities and ensuring potential harms are limited.

	### Evaluation Results

	The results of ethics and safety evaluations are within acceptable thresholds 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 safety, content safety, representational harms, memorization, large-scale harms. See the [Gemma model card](https://ai.google.dev/gemma/docs/model_card#evaluation_results) for more details.

	## Model Usage & Limitations

	These models have certain limitations that users should be aware of.

	### Intended Usage

	Code Gemma models have a wide range of applications, which vary between IT and PT models. 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.

	Code Completion
	: PT models can be used to complete code with an IDE extension

	Code Generation
	: IT model can be used to generate code with or without an IDE extension

	Code Conversation
	: IT model can power conversation interfaces which discuss code.

	Code Education
	: IT model supports interactive code learning experiences, aids in syntax correction or provides coding practice.

	### Known Limitations

	Large Language Models (LLMs) have limitations based on their training data and the inherent limitations of the technology. See the [Gemma model card](https://ai.google.dev/gemma/docs/model_card#evaluation_results) for more details on the limitations of LLMs.

	### Ethical Considerations & Risks

	The development of large language models (LLMs) raises several ethical concerns. We have carefully considered multiple aspects in the development of these models. Please refer to [the same discussion](https://ai.google.dev/gemma/docs/model_card#ethical_considerations_and_risks) in the Gemma model card for model details.

	### Benefits

	At the time of release, this family of models provides high-performance open code-focused large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models.

	Using the coding benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.

	<!-- original-model-card end -->