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237 lines
12 KiB
Markdown
237 lines
12 KiB
Markdown
# llama CLI Reference
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The `llama` CLI tool helps you setup and use the Llama Stack. It should be available on your path after installing the `llama-stack` package.
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## Installation
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You have two ways to install Llama Stack:
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1. **Install as a package**:
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You can install the repository directly from [PyPI](https://pypi.org/project/llama-stack/) by running the following command:
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```bash
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pip install llama-stack
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```
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2. **Install from source**:
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If you prefer to install from the source code, follow these steps:
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```bash
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mkdir -p ~/local
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cd ~/local
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git clone git@github.com:meta-llama/llama-stack.git
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conda create -n myenv python=3.10
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conda activate myenv
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cd llama-stack
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$CONDA_PREFIX/bin/pip install -e .
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## `llama` subcommands
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1. `download`: `llama` cli tools supports downloading the model from Meta or Hugging Face.
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2. `model`: Lists available models and their properties.
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3. `stack`: Allows you to build and run a Llama Stack server. You can read more about this [here](../distribution_dev/building_distro.md).
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### Sample Usage
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```
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llama --help
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```
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```
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usage: llama [-h] {download,model,stack} ...
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Welcome to the Llama CLI
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options:
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-h, --help show this help message and exit
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subcommands:
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{download,model,stack}
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```
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## Downloading models
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You first need to have models downloaded locally.
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To download any model you need the **Model Descriptor**.
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This can be obtained by running the command
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```
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llama model list
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```
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You should see a table like this:
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```
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+----------------------------------+------------------------------------------+----------------+
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| Model Descriptor | Hugging Face Repo | Context Length |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-8B | meta-llama/Llama-3.1-8B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-70B | meta-llama/Llama-3.1-70B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-405B:bf16-mp8 | meta-llama/Llama-3.1-405B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-405B | meta-llama/Llama-3.1-405B-FP8 | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-405B:bf16-mp16 | meta-llama/Llama-3.1-405B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-8B-Instruct | meta-llama/Llama-3.1-8B-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-70B-Instruct | meta-llama/Llama-3.1-70B-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-405B-Instruct:bf16-mp8 | meta-llama/Llama-3.1-405B-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-405B-Instruct | meta-llama/Llama-3.1-405B-Instruct-FP8 | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.1-405B-Instruct:bf16-mp16 | meta-llama/Llama-3.1-405B-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-1B | meta-llama/Llama-3.2-1B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-3B | meta-llama/Llama-3.2-3B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-11B-Vision | meta-llama/Llama-3.2-11B-Vision | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-90B-Vision | meta-llama/Llama-3.2-90B-Vision | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-1B-Instruct | meta-llama/Llama-3.2-1B-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-3B-Instruct | meta-llama/Llama-3.2-3B-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-11B-Vision-Instruct | meta-llama/Llama-3.2-11B-Vision-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama3.2-90B-Vision-Instruct | meta-llama/Llama-3.2-90B-Vision-Instruct | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama-Guard-3-11B-Vision | meta-llama/Llama-Guard-3-11B-Vision | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama-Guard-3-1B:int4-mp1 | meta-llama/Llama-Guard-3-1B-INT4 | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama-Guard-3-1B | meta-llama/Llama-Guard-3-1B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama-Guard-3-8B | meta-llama/Llama-Guard-3-8B | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama-Guard-3-8B:int8-mp1 | meta-llama/Llama-Guard-3-8B-INT8 | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Prompt-Guard-86M | meta-llama/Prompt-Guard-86M | 128K |
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+----------------------------------+------------------------------------------+----------------+
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| Llama-Guard-2-8B | meta-llama/Llama-Guard-2-8B | 4K |
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+----------------------------------+------------------------------------------+----------------+
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```
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To download models, you can use the llama download command.
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### Downloading from [Meta](https://llama.meta.com/llama-downloads/)
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Here is an example download command to get the 3B-Instruct/11B-Vision-Instruct model. You will need META_URL which can be obtained from [here](https://llama.meta.com/docs/getting_the_models/meta/)
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Download the required checkpoints using the following commands:
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```bash
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# download the 8B model, this can be run on a single GPU
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llama download --source meta --model-id Llama3.2-3B-Instruct --meta-url META_URL
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# you can also get the 70B model, this will require 8 GPUs however
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llama download --source meta --model-id Llama3.2-11B-Vision-Instruct --meta-url META_URL
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# llama-agents have safety enabled by default. For this, you will need
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# safety models -- Llama-Guard and Prompt-Guard
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llama download --source meta --model-id Prompt-Guard-86M --meta-url META_URL
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llama download --source meta --model-id Llama-Guard-3-1B --meta-url META_URL
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```
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### Downloading from [Hugging Face](https://huggingface.co/meta-llama)
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Essentially, the same commands above work, just replace `--source meta` with `--source huggingface`.
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```bash
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llama download --source huggingface --model-id Llama3.1-8B-Instruct --hf-token <HF_TOKEN>
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llama download --source huggingface --model-id Llama3.1-70B-Instruct --hf-token <HF_TOKEN>
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llama download --source huggingface --model-id Llama-Guard-3-1B --ignore-patterns *original*
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llama download --source huggingface --model-id Prompt-Guard-86M --ignore-patterns *original*
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```
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**Important:** Set your environment variable `HF_TOKEN` or pass in `--hf-token` to the command to validate your access. You can find your token at [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
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> **Tip:** Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.
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## Understand the models
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The `llama model` command helps you explore the model’s interface.
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1. `download`: Download the model from different sources. (meta, huggingface)
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2. `list`: Lists all the models available for download with hardware requirements to deploy the models.
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3. `prompt-format`: Show llama model message formats.
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4. `describe`: Describes all the properties of the model.
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### Sample Usage
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`llama model <subcommand> <options>`
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```
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llama model --help
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```
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```
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usage: llama model [-h] {download,list,prompt-format,describe} ...
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Work with llama models
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options:
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-h, --help show this help message and exit
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model_subcommands:
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{download,list,prompt-format,describe}
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```
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You can use the describe command to know more about a model:
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```
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llama model describe -m Llama3.2-3B-Instruct
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```
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### Describe
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```
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+-----------------------------+----------------------------------+
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| Model | Llama3.2-3B-Instruct |
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+-----------------------------+----------------------------------+
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| Hugging Face ID | meta-llama/Llama-3.2-3B-Instruct |
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+-----------------------------+----------------------------------+
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| Description | Llama 3.2 3b instruct model |
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+-----------------------------+----------------------------------+
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| Context Length | 128K tokens |
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+-----------------------------+----------------------------------+
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| Weights format | bf16 |
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+-----------------------------+----------------------------------+
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| Model params.json | { |
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| | "dim": 3072, |
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| | "n_layers": 28, |
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| | "n_heads": 24, |
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| | "n_kv_heads": 8, |
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| | "vocab_size": 128256, |
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| | "ffn_dim_multiplier": 1.0, |
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| | "multiple_of": 256, |
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| | "norm_eps": 1e-05, |
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| | "rope_theta": 500000.0, |
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| | "use_scaled_rope": true |
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| | } |
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+-----------------------------+----------------------------------+
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| Recommended sampling params | { |
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| | "strategy": "top_p", |
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| | "temperature": 1.0, |
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| | "top_p": 0.9, |
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| | "top_k": 0 |
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| | } |
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+-----------------------------+----------------------------------+
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```
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### Prompt Format
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You can even run `llama model prompt-format` see all of the templates and their tokens:
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```
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llama model prompt-format -m Llama3.2-3B-Instruct
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```
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You will be shown a Markdown formatted description of the model interface and how prompts / messages are formatted for various scenarios.
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**NOTE**: Outputs in terminal are color printed to show special tokens.
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