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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- meta |
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- pytorch |
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- llama-3.3 |
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- llama-3.3-instruct |
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- gguf |
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- ollama |
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- text-generation-inference |
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- Text-generation-webui |
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- llama |
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- llama_3.3 |
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- instruct |
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- LLM |
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- 70b |
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model_name: Llama-3.3-70B-Instruct-GGUF |
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arxiv: 2407.21783 |
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base_model: meta-llama/Llama-3.3-70b-instruct.hf |
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inference: false |
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model_creator: Meta Llama 3.3 |
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model_type: llama |
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pipeline_tag: text-generation |
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prompt_template: > |
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[INST] <<SYS>> |
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You are a helpful, respectful and honest assistant. Always answer as helpfully |
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as possible.If a question does not make any sense, or is not factually |
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coherent, explain why instead of answering something that is not correct. If |
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you don't know the answer to a question, do not answer it with false |
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information. |
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<</SYS>> |
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{prompt}[/INST] |
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quantized_by: hierholzer |
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--- |
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[](#) |
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# GGUF Model |
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----------------------------------- |
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Here are Quantized versions of Llama-3.3-70B-Instruct using GGUF |
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## 🤔 What Is GGUF |
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GGUF is designed for use with GGML and other executors. |
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GGUF was developed by @ggerganov who is also the developer of llama.cpp, a popular C/C++ LLM inference framework. |
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Models initially developed in frameworks like PyTorch can be converted to GGUF format for use with those engines. |
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## ☑️Uploaded Quantization Types |
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Here are the quantized versions that I currently have available: |
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- [x] Q2_K |
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- [x] Q3_K_S |
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- [x] Q3_K_M |
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- [x] Q3_K_L |
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- [x] Q4_K_S |
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- [x] Q4_K_M ~ *Recommended* |
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- [x] Q5_K_S ~ *Recommended* |
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- [x] Q5_K_M ~ *Recommended* |
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- [ ] Q6_K |
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- [ ] Q8_0 ~ *NOT Recommended* |
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- [ ] F16 ~ *NOT Recommended* |
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- [ ] F32 ~ *NOT Recommended* |
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*Feel Free to reach out to me if you need a specific Quantization Type that I do not currently offer.* |
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### 📈All Quantization Types Possible |
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Below is a table of all the Quantization Types that are possible as well as short descriptions. |
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| **#** | **or** | **Q#** | **:** | _Description Of Quantization Types_ | |
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|-------|:------:|:------:|:-----:|----------------------------------------------------------------| |
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| 2 | or | Q4_0 | : | small, very high quality loss - legacy, prefer using Q3_K_M | |
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| 3 | or | Q4_1 | : | small, substantial quality loss - legacy, prefer using Q3_K_L | |
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| 8 | or | Q5_0 | : | medium, balanced quality - legacy, prefer using Q4_K_M | |
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| 9 | or | Q5_1 | : | medium, low quality loss - legacy, prefer using Q5_K_M | |
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| 10 | or | Q2_K | : | smallest, extreme quality loss - *NOT Recommended* | |
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| 12 | or | Q3_K | : | alias for Q3_K_M | |
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| 11 | or | Q3_K_S | : | very small, very high quality loss | |
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| 12 | or | Q3_K_M | : | very small, high quality loss | |
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| 13 | or | Q3_K_L | : | small, high quality loss | |
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| 15 | or | Q4_K | : | alias for Q4_K_M | |
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| 14 | or | Q4_K_S | : | small, some quality loss | |
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| 15 | or | Q4_K_M | : | medium, balanced quality - *Recommended* | |
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| 17 | or | Q5_K | : | alias for Q5_K_M | |
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| 16 | or | Q5_K_S | : | large, low quality loss - *Recommended* | |
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| 17 | or | Q5_K_M | : | large, very low quality loss - *Recommended* | |
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| 18 | or | Q6_K | : | very large, very low quality loss | |
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| 7 | or | Q8_0 | : | very large, extremely low quality loss | |
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| 1 | or | F16 | : | extremely large, virtually no quality loss - *NOT Recommended* | |
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| 0 | or | F32 | : | absolutely huge, lossless - *NOT Recommended* | |
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## 💪 Benefits of using GGUF |
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By using a GGUF version of Llama-3.3-70B-Instruct, you will be able to run this LLM while having to use significantly less resources than you would using the non quantized version. |
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This also allows you to run this 70B Model on a machine with less memory than a non quantized version. |
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## ⚙️️Installation |
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-------------------------------------------- |
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Here are 2 different methods you can use to run the quantized versions of Llama-3.3-70B-Instruct |
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### 1️⃣ Text-generation-webui |
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Text-generation-webui is a web UI for Large Language Models that you can run locally. |
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#### ☑️ How to install Text-generation-webui |
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*If you already have Text-generation-webui then skip this section* |
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| # | Download Text-generation-webui | |
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|----|------------------------------------------------------------------------------------------------------------------| |
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| 1. | Clone the text-generation-webui repository from Github by copying the git clone snippet below: | |
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```shell |
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git clone https://github.com/oobabooga/text-generation-webui.git |
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``` |
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| # | Install Text-generation-webui | |
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|----|------------------------------------------------------------------------------------------------------------------| |
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| 1. | Run the `start_linux.sh`, `start_windows.bat`, `start_macos.sh`, or `start_wsl.bat` script depending on your OS. | |
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| 2. | Select your GPU vendor when asked. | |
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| 3. | Once the installation script ends, browse to `http://localhost:7860`. | |
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#### ✅Using Llama-3.3-70B-Instruct-GGUF with Text-generation-webui |
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| # | Using Llama-3.3-70B-Instruct-GGUF with Text-generation-webui | |
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|----|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 1. | Once you are running text-generation-webui in your browser, click on the 'Model' Tab at the top of your window. | |
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| 2. | In the Download Model section, you need to enter the model repo: *hierholzer/Llama-3.3-70B-Instruct-GGUF* and below it, the specific filename to download, such as: *Llama-3.1-70B-Instruct-Q4_K_M.gguf* | |
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| 3. | Click Download and wait for the download to complete. NOTE: you can see the download progress back in your terminal window. | |
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| 4. | Once the download is finished, click the blue refresh icon within the Model tab that you are in. | |
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| 5. | Select your newly downloaded GGUF file in the Model drop-down. once selected, change the settings to best match your system. | |
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### 2️⃣ Ollama |
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Ollama runs as a local service. |
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Although it technically works using a command-line interface, Ollama's best attribute is their REST API. |
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Being able to utilize your locally ran LLMs through the use of this API can give you almost endless possibilities! |
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*Feel free to reach out to me if you would like to know some examples that I use this API for* |
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#### ☑️ How to install Ollama |
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Go To the URL below, and then select which OS you are using |
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```shell |
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https://ollama.com/download |
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``` |
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Using Windows, or Mac you will then download a file and run it. |
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If you are using linux it will just provide a single command that you need to run in your terminal window. |
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*That's about it for installing Ollama* |
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#### ✅Using Llama-3.3-70B-Instruct-GGUF with Ollama |
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Ollama does have a Model Library where you can download models: |
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```shell |
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https://ollama.com/library |
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``` |
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This Model Library offers many different LLM versions that you can use. |
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However at the time of writing this, there is no version of Llama-3.3-Instruct offered in the Ollama library. |
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If you would like to use Llama-3.3-Instruct (70B), do the following: |
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| # | Running the 70B quantized version of Llama 3.3-Instruct with Ollama | |
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|----|----------------------------------------------------------------------------------------------| |
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| 1. | Open up your terminal that you have Ollama Installed on. | |
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| 2. | Paste the following command: | |
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```shell |
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ollama run hf.co/hierholzer/Llama-3.3-70B-Instruct-GGUF:Q4_K_M |
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``` |
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*Replace Q4_K_M with whatever version you would like to use from this repository.* |
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| # | Running the 70B quantized version of Llama 3.3-Instruct with Ollama - *continued* | |
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|----|-----------------------------------------------------------------------------------| |
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| 3. | This will download & run the model. It will also be saved for future use. | |
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------------------------------------------------- |
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[](#) |
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[](https://docs.abblix.com/docs/technical-requirements) |
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[](https://docs.abblix.com/docs/technical-requirements) |
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[](https://forthebadge.com) |
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[](https://forthebadge.com) |
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[](https://forthebadge.com) |
