llama.cpp/tools/completion

This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the llama.cpp project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts.

Table of Contents

1. Quick Start
2. Usage
3. Common Options
4. Input Prompts
5. Interaction
6. Context Management
7. Generation Flags
8. Performance Tuning and Memory Options
9. Additional Options

Quick Start

To get started right away, run the following command, making sure to use the correct path for the model you have:

First, we will need to download a model. In these examples, we will use the Gemma model from the ggml-org repo on Hugging Face. https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true

Once downloaded, place your model in the models folder in llama.cpp.

Unix-based systems (Linux, macOS, etc.):

Input prompt (One-and-done)

./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"

Conversation mode (Allow for continuous interaction with the model)

./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma

Conversation mode using built-in jinja chat template

./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja

One-and-done query using jinja with custom system prompt and a starting prompt

./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"

Infinite text from a starting prompt (you can use Ctrl-C to stop it):

./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1

Windows:

Input prompt (One-and-done)

./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"

Conversation mode (Allow for continuous interaction with the model)

./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma

Conversation mode using built-in jinja chat template

./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja

One-and-done query using jinja with custom system prompt and a starting prompt

./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"

Infinite text from a starting prompt (you can use Ctrl-C to stop it):

llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1

Usage

Common params

Sampling params

Completion-specific params

Common Options

In this section, we cover the most commonly used options for running the llama-completion program with the LLaMA models:

• -m FNAME, --model FNAME: Specify the path to the LLaMA model file (e.g., models/gemma-1.1-7b-it.Q4_K_M.gguf; inferred from --model-url if set).
• -mu MODEL_URL --model-url MODEL_URL: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true).
• -i, --interactive: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
• -n N, --n-predict N: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
• -c N, --ctx-size N: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference.
• -mli, --multiline-input: Allows you to write or paste multiple lines without ending each in ''
• -t N, --threads N: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has.
• -ngl N, --n-gpu-layers N: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.

Input Prompts

The llama-completion program provides several ways to interact with the LLaMA models using input prompts:

• --prompt PROMPT: Provide a prompt directly as a command-line option.
• --file FNAME: Provide a file containing a prompt or multiple prompts.
• --system-prompt PROMPT: Provide a system prompt (will otherwise use the default one in the chat template (if provided)).
• --system-prompt-file FNAME: Provide a file containing a system prompt.
• --interactive-first: Run the program in interactive mode and wait for input right away. (More on this below.)

Interaction

The llama-completion program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including --interactive and --interactive-first.

In interactive mode, users can participate in text generation by injecting their input during the process. Users can press Ctrl+C at any time to interject and type their input, followed by pressing Return to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (\) and continue typing.

Interaction Options

• -i, --interactive: Run the program in interactive mode, allowing users to engage in real-time conversations or provide specific instructions to the model.
• --interactive-first: Run the program in interactive mode and immediately wait for user input before starting the text generation.
• -cnv, --conversation: Run the program in conversation mode (does not print special tokens and suffix/prefix, use default or provided chat template) (default: true if chat template found)
• -no-cnv: Disable conversation mode (default: false)
• -st, --single-turn: Only process a single conversation turn (user input) and then exit.
• --jinja: Enable jinja chat template parser, will use the model's built-in template or a user-provided one (default: false)
• --color: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.

By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.

Reverse Prompts

Reverse prompts are a powerful way to create a chat-like experience with a LLaMA model by pausing the text generation when specific text strings are encountered:

• -r PROMPT, --reverse-prompt PROMPT: Specify one or multiple reverse prompts to pause text generation and switch to interactive mode. For example, -r "User:" can be used to jump back into the conversation whenever it's the user's turn to speak. This helps create a more interactive and conversational experience. However, the reverse prompt doesn't work when it ends with a space.

To overcome this limitation, you can use the --in-prefix flag to add a space or any other characters after the reverse prompt.

In-Prefix

The --in-prefix flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the --in-prefix flag in conjunction with the --reverse-prompt flag:

./llama-completion -r "User:" --in-prefix " "

In-Suffix

The --in-suffix flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (\n) that's automatically added to the end of the user's input. Here's an example of how to use the --in-suffix flag in conjunction with the --reverse-prompt flag:

./llama-completion -r "User:" --in-prefix " " --in-suffix "Assistant:"

When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled

Chat templates

--chat-template JINJA_TEMPLATE: This option sets a custom jinja chat template. It accepts a string, not a file name. Default: template taken from model's metadata. Llama.cpp only supports some pre-defined templates. These include llama2, llama3, gemma, monarch, chatml, orion, vicuna, vicuna-orca, deepseek, command-r, zephyr. When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled.

Example usage: --chat-template gemma

--chat-template-file FNAME: Load a custom jinja chat template from an external file, useful if the model contains outdated or incompatible template, some examples can be found in models/templates. Up-to-date chat templates can be downloaded from Hugging Face using scripts/get_chat_template.py

Context Management

During text generation, LLaMA models have a limited context size, which means they can only consider a certain number of tokens from the input and generated text. When the context fills up, the model resets internally, potentially losing some information from the beginning of the conversation or instructions. Context management options help maintain continuity and coherence in these situations.

Context Size

• -c N, --ctx-size N: Set the size of the prompt context (default: 4096, 0 = loaded from model). If a LLaMA model was built with a longer context, increasing this value will yield the best results on longer input/inference.

Extended Context Size

Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model has a context length (max sequence length) of 4096 (4k) and the fine-tuned model has 32k. That is a scaling factor of 8, and should work by setting the above --ctx-size to 32768 (32k) and --rope-scale to 8.

• --rope-scale N: Where N is the linear scaling factor used by the fine-tuned model.

Keep Prompt

The --keep option allows users to retain the original prompt when the model runs out of context, ensuring a connection to the initial instruction or conversation topic is maintained.

• --keep N: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use -1 to retain all tokens from the initial prompt.

By utilizing context management options like --ctx-size and --keep, you can maintain a more coherent and consistent interaction with the LLaMA models, ensuring that the generated text remains relevant to the original prompt or conversation.

Generation Flags

The following options allow you to control the text generation process and fine-tune the diversity, creativity, and quality of the generated text according to your needs. By adjusting these options and experimenting with different combinations of values, you can find the best settings for your specific use case.

Number of Tokens to Predict

• -n N, --predict N: Set the number of tokens to predict when generating text (default: -1, -1 = infinity, -2 = until context filled)

The --predict option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text.

A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after --keep) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in a significant pause in output.

If the pause is undesirable, a value of -2 will stop generation immediately when the context is filled.

The --no-context-shift option allows you to stop the infinite text generation once the finite context window is full.

It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode, text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified --predict value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the --ignore-eos parameter.

Temperature

• --temp N: Adjust the randomness of the generated text (default: 0.8).

Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.

Example usage: --temp 0

Repeat Penalty

• --repeat-penalty N: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
• --repeat-last-n N: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled).

The repeat-penalty option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.

The repeat-last-n option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty.

DRY Repetition Penalty

DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original PR link).

• --dry-multiplier N: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled).
• --dry-base N: Set the DRY sampling base value (default: 1.75).
• --dry-allowed-length N: Set the allowed length for DRY sampling (default: 2).
• --dry-penalty-last-n N: Set DRY penalty for the last n tokens (default: 64, 0 = disable).
• --dry-sequence-breaker STRING: Add a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: ['\n', ':', '"', '*']. If the string "none" is supplied, no sequence breakers are used.

The dry-multiplier option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8.

The dry-base option sets the base value for the exponential penalty calculation in DRY sampling. Higher values lead to more aggressive penalization of repetitions.

The dry-allowed-length option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words.

The dry-penalty-last-n option controls how many recent tokens to consider when applying the DRY penalty. A value of 0 disables the penalty. Use a positive value to limit the consideration to a specific number of recent tokens.

The dry-sequence-breaker option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied.

DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence.

Example usage: --dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n 64 --dry-sequence-breaker "—" --dry-sequence-breaker "##"

Top-K Sampling

• --top-k N: Limit the next token selection to the K most probable tokens (default: 40).

Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top-k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text. The default value is 40.

Example usage: --top-k 30

Top-P Sampling

• --top-p N: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).

Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top-p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. The default value is 0.9.

Example usage: --top-p 0.95

Min-P Sampling

• --min-p N: Sets a minimum base probability threshold for token selection (default: 0.1).

The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with p=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.

Example usage: --min-p 0.05

Adaptive-P Sampling

• --adaptive-target N: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
• --adaptive-decay N: EMA decay for adaptation; history ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99)

Adaptive-P: Select tokens near a configurable target probability over time.

The adaptive-p sampler transforms the token probability distribution to favor tokens that fall near a user-configurable probability target. Internally, the sampler maintains an exponential moving average of the ORIGINAL probabilities of selected tokens at each sampling step. It uses this EMA to compute an adapted target probability at each sampling step, thus maintaining the desired target probability over time. Only mild truncation before this sampler is recommended. It is suggested to apply min-p before adaptive-p as the only other active sampler.

Recommended starting values: --adaptive-target 0.55 --adaptive-decay 0.9

For more info, refer to: llama.cpp#17927

Locally Typical Sampling

• --typical N: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).

Locally typical sampling promotes the generation of contextually coherent and diverse text by sampling tokens that are typical or expected based on the surrounding context. By setting the parameter p between 0 and 1, you can control the balance between producing text that is locally coherent and diverse. A value closer to 1 will promote more contextually coherent tokens, while a value closer to 0 will promote more diverse tokens. A value equal to 1 disables locally typical sampling.

Example usage: --typical 0.9

Mirostat Sampling

• --mirostat N: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
• --mirostat-lr N: Set the Mirostat learning rate, parameter eta (default: 0.1).
• --mirostat-ent N: Set the Mirostat target entropy, parameter tau (default: 5.0).

Mirostat is an algorithm that actively maintains the quality of generated text within a desired range during text generation. It aims to strike a balance between coherence and diversity, avoiding low-quality output caused by excessive repetition (boredom traps) or incoherence (confusion traps).

The --mirostat-lr option sets the Mirostat learning rate (eta). The learning rate influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. The default value is 0.1.

The --mirostat-ent option sets the Mirostat target entropy (tau), which represents the desired perplexity value for the generated text. Adjusting the target entropy allows you to control the balance between coherence and diversity in the generated text. A lower value will result in more focused and coherent text, while a higher value will lead to more diverse and potentially less coherent text. The default value is 5.0.

Example usage: --mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0

XTC Sampling

• --xtc-probability N: Sets the chance for token removal (checked once on sampler start) (default: 0.0).
• --xtc-threshold N: Sets a minimum probability threshold for tokens to be removed (default: 0.1).

Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of xtc-probability it searches for tokens with probabilities of xtc-threshold and above, then removes all such tokens except the least probable one.

By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repetition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models.

Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: --sampling-seq mx --min-p 0.02 --xtc-probability 0.5.

Example usage: --xtc-probability 0.5 --xtc-threshold 0.1

Top-nσ Sampling

• --top-nsigma N: Limit the next token selection to a subset of tokens with pre-softmax logits that are within n * σ less than the max logit (default: -1, -1 = disabled).

Top-nσ sampling is a text generation method that selects tokens based on a statistical threshold in pre-softmax logits. It works by only sampling from tokens with logits that are within n * σ of the maximum logit. This method helps maintain a stable sampling space regardless of temperature scaling, allowing it to perform well on reasoning tasks even in high temperatures. Without complex probability manipulation, it efficiently filters tokens directly on the pre-softmax logits. A higher value for top-nsigma (e.g., 5) will take more noisy tokens into consideration, while a lower value (e.g., 1) will focous on the more informative region of the sampling space.

Example usage: --top-nsigma 1

Logit Bias

• -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS: Modify the likelihood of a token appearing in the generated text completion.

The logit bias option allows you to manually adjust the likelihood of specific tokens appearing in the generated text. By providing a token ID and a positive or negative bias value, you can increase or decrease the probability of that token being generated.

For example, use --logit-bias 15043+1 to increase the likelihood of the token 'Hello', or --logit-bias 15043-1 to decrease its likelihood. Using a value of negative infinity, --logit-bias 15043-inf ensures that the token Hello is never produced.

A more practical use case might be to prevent the generation of \code{begin} and \code{end} by setting the \ token (29905) to negative infinity with -l 29905-inf. (This is due to the prevalence of LaTeX codes that show up in LLaMA model inference.)

Example usage: --logit-bias 29905-inf

RNG Seed

• -s SEED, --seed SEED: Set the random number generator (RNG) seed (default: -1, -1 = random seed).

The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.

Performance Tuning and Memory Options

These options help improve the performance and memory usage of the LLaMA models. By adjusting these settings, you can fine-tune the model's behavior to better suit your system's capabilities and achieve optimal performance for your specific use case.

Number of Threads

• -t N, --threads N: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Using the correct number of threads can greatly improve performance.
• -tb N, --threads-batch N: Set the number of threads to use during batch and prompt processing. In some systems, it is beneficial to use a higher number of threads during batch processing than during generation. If not specified, the number of threads used for batch processing will be the same as the number of threads used for generation.

Model Loading Mode

• -lm MODE, --load-mode MODE: Specify the model loading mode (default: auto).
  • auto: Memory-map the model, unless the device does not support it.
  • none: No special loading mode. Disabling mmap results in slower load times but may reduce pageouts if you're not using mlock. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
  • mmap: Memory-map the model.
  • mlock: Lock the model in memory, preventing it from being swapped out when memory-mapped. This can improve performance but trades away some of the advantages of memory-mapping by requiring more RAM to run and potentially slowing down load times as the model loads into RAM.
  • mmap+mlock: Memory-map the model and lock it in memory.
  • dio: Use DirectIO if available.

NUMA support

• --numa distribute: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes.
• --numa isolate: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node.
• --numa numactl: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitrary core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.

These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.

Batch Size

• -ub N, --ubatch-size N: Physical batch size. This is the maximum number of tokens that may be processed at a time. Increasing this value may improve performance during prompt processing, at the expense of higher memory usage. Default: 512.

• -b N, --batch-size N: Logical batch size. Increasing this value above the value of the physical batch size may improve prompt processing performance when using multiple GPUs with pipeline parallelism. Default: 2048.

Prompt Caching

• --prompt-cache FNAME: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. Note: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation.

Grammars & JSON schemas

• --grammar GRAMMAR, --grammar-file FILE: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the GBNF guide for details on the syntax.

• --json-schema SCHEMA: Specify a JSON schema to constrain model output to (e.g. {"type": "object"} for any JSON object, or {"items": {"type": "string", "minLength": 10, "maxLength": 100}, "minItems": 10} for a JSON array of strings with size constraints).

Quantization

For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary README.

LoRA (Low-Rank Adaptation) adapters

• --lora FNAME: Optional path to a LoRA adapter to use with scaling of 1.0. Can be mixed with --lora-scaled and can be repeated to use multiple adapters.
• --lora-scaled FNAME: Optional path to a LoRA adapter with user-defined scaling. Can be mixed with --lora and can repeated to use multiple adapters.

You can add LoRA adapters using --lora or --lora-scaled. For example: --lora my_adapter_1.gguf --lora my_adapter_2.gguf ... or --lora-scaled lora_task_A.gguf 0.5 --lora-scaled lora_task_B.gguf 0.5.

LoRA adapters should be in GGUF format. To convert from Hugging Face format use the convert-lora-to-gguf.py script. LoRA adapters are loaded separately and applied during inference - they are not merged with the main model. This means that mmap model loading is fully supported when using LoRA adapters. The old --lora-base flag has been removed now that merging is no longer performed.

Additional Options

These options provide extra functionality and customization when running the LLaMA models:

• -h, --help: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
• --verbose-prompt: Print the prompt before generating text.
• --no-display-prompt: Don't print prompt at generation.
• -mg i, --main-gpu i: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
• -ts SPLIT, --tensor-split SPLIT: When using multiple devices this option controls how tensors should be split across devices. SPLIT is a comma-separated list of non-negative values that assigns the proportion of data that each device should get in order. For example, "3,2" will assign 60% of the data to device 0 and 40% to device 1. By default, the data is split in proportion to VRAM, but this may not be optimal for performance. The list of the devices which are being used is printed on startup and can be different from the device list given by --list-devices or e.g. nvidia-smi.
• -hfr URL --hf-repo URL: The url to the Hugging Face model repository. Used in conjunction with --hf-file or -hff. The model is downloaded and stored in the file provided by -m or --model. If -m is not provided, the model is auto-stored in the path specified by the LLAMA_CACHE environment variable or in an OS-specific local cache.