I yank the source last [...]
I've made several improvements, including adding the `string-distance-percentage' that was mentioned. But let's
forget about that branch [1] or visit that URL for the latest
source on that.
Here, in this thread, the context thing with respect to AI,
anyone having any luck knowing what to do with that?
It is mentioned 14 times in llamafile(1) but not how to
actually set it up with your own data?
Okay, let's do it like this, here is the file, maybe I'm blind.
[1]
https://dataswamp.org/~incal/emacs-init/b-a-original.el
LLAMAFILE(1) BSD General Commands Manual LLAMAFILE(1)
NAME
llamafile — large language model runner
SYNOPSIS
llamafile [--server] [flags...] -m model.gguf [--mmproj vision.gguf]
llamafile [--cli] [flags...] -m model.gguf -p prompt
llamafile [--cli] [flags...] -m model.gguf --mmproj vision.gguf --image
graphic.png -p prompt
DESCRIPTION
llamafile is a large language model tool. It has use cases such as:
- Code completion
- Prose composition
- Chatbot that passes the Turing test
- Text/image summarization and analysis
OPTIONS
The following options are available:
--version
Print version and exit.
-h, --help
Show help message and exit.
--cli Puts program in command line interface mode. This flag is im‐
plied when a prompt is supplied using either the -p or -f flags.
--server
Puts program in server mode. This will launch an HTTP server on
a local port. This server has both a web UI and an OpenAI API
compatible completions endpoint. When the server is run on a
desk system, a tab browser tab will be launched automatically
that displays the web UI. This --server flag is implied if no
prompt is specified, i.e. neither the -p or -f flags are passed.
-m FNAME, --model FNAME
Model path in the GGUF file format.
Default: models/7B/ggml-model-f16.gguf
--mmproj FNAME
Specifies path of the LLaVA vision model in the GGUF file for‐
mat. If this flag is supplied, then the --model and --image
flags should also be supplied.
-s SEED, --seed SEED
Random Number Generator (RNG) seed. A random seed is used if
this is less than zero.
Default: -1
-t N, --threads N
Number of threads to use during generation.
Default: $(nproc)/2
-tb N, --threads-batch N
Set the number of threads to use during batch and prompt pro‐
cessing. In some systems, it is beneficial to use a higher num‐
ber of threads during batch processing than during generation.
If not specified, the number of threads used for batch process‐
ing will be the same as the number of threads used for genera‐
tion.
Default: Same as --threads
-td N, --threads-draft N
Number of threads to use during generation.
Default: Same as --threads
-tbd N, --threads-batch-draft N
Number of threads to use during batch and prompt processing.
Default: Same as --threads-draft
--in-prefix-bos
Prefix BOS to user inputs, preceding the --in-prefix string.
--in-prefix STRING
This 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:
./main -r "User:" --in-prefix " "
Default: empty
--in-suffix STRING
This flag is used to add a suffix after your input. This is use‐
ful for adding an "Assistant:" prompt after the user's input.
It's added after the new-line character (\n) that's automati‐
cally 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:
./main -r "User:" --in-prefix " " --in-suffix "Assistant:"
Default: empty
-n N, --n-predict N
Number of tokens to predict.
- -1 = infinity
- -2 = until context filled
Default: -1
-c N, --ctx-size N
Set the size of the prompt context. A larger context size helps
the model to better comprehend and generate responses for longer
input or conversations. The LLaMA models were built with a con‐
text of 2048, which yields the best results on longer input /
inference.
- 0 = loaded automatically from model
Default: 512
-b N, --batch-size N
Batch size for prompt processing.
Default: 512
--top-k N
Top-k sampling.
- 0 = disabled
Default: 40
--top-p N
Top-p sampling.
- 1.0 = disabled
Default: 0.9
--min-p N
Min-p sampling.
- 0.0 = disabled
Default: 0.1
--tfs N
Tail free sampling, parameter z.
- 1.0 = disabled
Default: 1.0
--typical N
Locally typical sampling, parameter p.
- 1.0 = disabled
Default: 1.0
--repeat-last-n N
Last n tokens to consider for penalize.
- 0 = disabled
- -1 = ctx_size
Default: 64
--repeat-penalty N
Penalize repeat sequence of tokens.
- 1.0 = disabled
Default: 1.1
--presence-penalty N
Repeat alpha presence penalty.
- 0.0 = disabled
Default: 0.0
--frequency-penalty N
Repeat alpha frequency penalty.
- 0.0 = disabled
Default: 0.0
--mirostat N
Use Mirostat sampling. Top K, Nucleus, Tail Free and Locally
Typical samplers are ignored if used..
- 0 = disabled
- 1 = Mirostat
- 2 = Mirostat 2.0
Default: 0
--mirostat-lr N
Mirostat learning rate, parameter eta.
Default: 0.1
--mirostat-ent N
Mirostat target entropy, parameter tau.
Default: 5.0
-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS
Modifies the likelihood of token appearing in the completion,
i.e. --logit-bias 15043+1 to increase likelihood of token
' Hello', or --logit-bias 15043-1 to decrease likelihood of to‐
ken ' Hello'.
-md FNAME, --model-draft FNAME
Draft model for speculative decoding.
Default: models/7B/ggml-model-f16.gguf
--cfg-negative-prompt PROMPT
Negative prompt to use for guidance..
Default: empty
--cfg-negative-prompt-file FNAME
Negative prompt file to use for guidance.
Default: empty
--cfg-scale N
Strength of guidance.
- 1.0 = disable
Default: 1.0
--rope-scaling {none,linear,yarn}
RoPE frequency scaling method, defaults to linear unless speci‐
fied by the model
--rope-scale N
RoPE context scaling factor, expands context by a factor of N
where N is the linear scaling factor used by the fine-tuned
model. Some fine-tuned models have extended the context length
by scaling RoPE. For example, if the original pre-trained model
have a context length (max sequence length) of 4096 (4k) and the
fine-tuned model have 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-freq-base N
RoPE base frequency, used by NTK-aware scaling.
Default: loaded from model
--rope-freq-scale N
RoPE frequency scaling factor, expands context by a factor of
1/N
--yarn-orig-ctx N
YaRN: original context size of model.
Default: 0 = model training context size
--yarn-ext-factor N
YaRN: extrapolation mix factor.
- 0.0 = full interpolation
Default: 1.0
--yarn-attn-factor N
YaRN: scale sqrt(t) or attention magnitude.
Default: 1.0
--yarn-beta-slow N
YaRN: high correction dim or alpha.
Default: 1.0
--yarn-beta-fast N
YaRN: low correction dim or beta.
Default: 32.0
--ignore-eos
Ignore end of stream token and continue generating (implies
--logit-bias 2-inf)
--no-penalize-nl
Do not penalize newline token.
--temp N
Temperature.
Default: 0.8
--logits-all
Return logits for all tokens in the batch.
Default: disabled
--hellaswag
Compute HellaSwag score over random tasks from datafile supplied
with -f
--hellaswag-tasks N
Number of tasks to use when computing the HellaSwag score.
Default: 400
--keep N
This flag 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, where N is the
number of tokens from the initial prompt to retain when the
model resets its internal context.
- 0 = no tokens are kept from initial prompt
- -1 = retain all tokens from initial prompt
Default: 0
--draft N
Number of tokens to draft for speculative decoding.
Default: 16
--chunks N
Max number of chunks to process.
- -1 = all
Default: -1
-ns N, --sequences N
Number of sequences to decode.
Default: 1
-pa N, --p-accept N
speculative decoding accept probability.
Default: 0.5
-ps N, --p-split N
Speculative decoding split probability.
Default: 0.1
--mlock
Force system to keep model in RAM rather than swapping or com‐
pressing.
--no-mmap
Do not memory-map model (slower load but may reduce pageouts if
not using mlock).
--numa Attempt optimizations that help on some NUMA systems if run
without this previously, it is recommended to drop the system
page cache before using this. See
https://github.com/ggerganov/llama.cpp/issues/1437.
--recompile
Force GPU support to be recompiled at runtime if possible.
--nocompile
Never compile GPU support at runtime.
If the appropriate DSO file already exists under ~/.llamafile/
then it'll be linked as-is without question. If a prebuilt DSO
is present in the PKZIP content of the executable, then it'll be
extracted and linked if possible. Otherwise, llamafile will skip
any attempt to compile GPU support and simply fall back to using
CPU inference.
--gpu GPU
Specifies which brand of GPU should be used. Valid choices are:
- AUTO: Use any GPU if possible, otherwise fall back to CPU
inference (default)
- APPLE: Use Apple Metal GPU. This is only available on MacOS
ARM64. If Metal could not be used for any reason, then a fa‐
tal error will be raised.
- AMD: Use AMD GPUs. The AMD HIP ROCm SDK should be installed
in which case we assume the HIP_PATH environment variable
has been defined. The set of gfx microarchitectures needed
to run on the host machine is determined automatically based
on the output of the hipInfo command. On Windows, llamafile
release binaries are distributed with a tinyBLAS DLL so
it'll work out of the box without requiring the HIP SDK to
be installed. However, tinyBLAS is slower than rocBLAS for
batch and image processing, so it's recommended that the SDK
be installed anyway. If an AMD GPU could not be used for any
reason, then a fatal error will be raised.
- NVIDIA: Use NVIDIA GPUs. If an NVIDIA GPU could not be used
for any reason, a fatal error will be raised. On Windows,
NVIDIA GPU support will use our tinyBLAS library, since it
works on stock Windows installs. However, tinyBLAS goes
slower for batch and image processing. It's possible to use
NVIDIA's closed-source cuBLAS library instead. To do that,
both MSVC and CUDA need to be installed and the llamafile
command should be run once from the x64 MSVC command prompt
with the --recompile flag passed. The GGML library will then
be compiled and saved to ~/.llamafile/ so the special
process only needs to happen a single time.
- DISABLE: Never use GPU and instead use CPU inference. This
setting is implied by -ngl 0.
-ngl N, --n-gpu-layers N
Number of layers to store in VRAM.
-ngld N, --n-gpu-layers-draft N
Number of layers to store in VRAM for the draft model.
-sm SPLIT_MODE, --split-mode SPLIT_MODE
How to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
-ts SPLIT, --tensor-split SPLIT
When using multiple GPUs this option controls how large tensors
should be split across all GPUs. SPLIT is a comma-separated
list of non-negative values that assigns the proportion of data
that each GPU should get in order. For example, "3,2" will as‐
sign 60% of the data to GPU 0 and 40% to GPU 1. By default the
data is split in proportion to VRAM but this may not be optimal
for performance. Requires cuBLAS. How to split tensors across
multiple GPUs, comma-separated list of proportions, e.g. 3,1
-mg i, --main-gpu i
The GPU to use for scratch and small tensors.
-nommq, --no-mul-mat-q
Use cuBLAS instead of custom mul_mat_q CUDA kernels. Not recom‐
mended since this is both slower and uses more VRAM.
--verbose-prompt
Print prompt before generation.
--simple-io
Use basic IO for better compatibility in subprocesses and lim‐
ited consoles.
--lora FNAME
Apply LoRA adapter (implies --no-mmap)
--lora-scaled FNAME S
Apply LoRA adapter with user defined scaling S (implies
--no-mmap)
--lora-base FNAME
Optional model to use as a base for the layers modified by the
LoRA adapter
--unsecure
Disables pledge() sandboxing on Linux and OpenBSD.
--samplers
Samplers that will be used for generation in the order, sepa‐
rated by semicolon, for example: top_k;tfs;typi‐
cal;top_p;min_p;temp
--samplers-seq
Simplified sequence for samplers that will be used.
-cml, --chatml
Run in chatml mode (use with ChatML-compatible models)
-dkvc, --dump-kv-cache
Verbose print of the KV cache.
-nkvo, --no-kv-offload
Disable KV offload.
-ctk TYPE, --cache-type-k TYPE
KV cache data type for K.
-ctv TYPE, --cache-type-v TYPE
KV cache data type for V.
-gan N, --grp-attn-n N
Group-attention factor.
Default: 1
-gaw N, --grp-attn-w N
Group-attention width.
Default: 512
-bf FNAME, --binary-file FNAME
Binary file containing multiple choice tasks.
--winogrande
Compute Winogrande score over random tasks from datafile sup‐
plied by the -f flag.
--winogrande-tasks N
Number of tasks to use when computing the Winogrande score.
Default: 0
--multiple-choice
Compute multiple choice score over random tasks from datafile
supplied by the -f flag.
--multiple-choice-tasks N
Number of tasks to use when computing the multiple choice score.
Default: 0
--kl-divergence
Computes KL-divergence to logits provided via the
--kl-divergence-base flag.
--save-all-logits FNAME, --kl-divergence-base FNAME
Save logits to filename.
-ptc N, --print-token-count N
Print token count every N tokens.
Default: -1
--pooling KIND
Specifies pooling type for embeddings. This may be one of:
- none
- mean
- cls
The model default is used if unspecified.
CLI OPTIONS
The following options may be specified when llamafile is running in
--cli mode.
-e, --escape
Process prompt escapes sequences (\n, \r, \t, \´, \", \\)
-p STRING, --prompt STRING
Prompt to start text generation. Your LLM works by auto-complet‐
ing this text. For example:
llamafile -m model.gguf -p "four score and"
Stands a pretty good chance of printing Lincoln's Gettysburg Ad‐
dress. Prompts can take on a structured format too. Depending
on how your model was trained, it may specify in its docs an in‐
struction notation. With some models that might be:
llamafile -p "[INST]Summarize this: $(cat file)[/INST]"
In most cases, simply colons and newlines will work too:
llamafile -e -p "User: What is best in life?\nAssistant:"
-f FNAME, --file FNAME
Prompt file to start generation.
--grammar GRAMMAR
BNF-like grammar to constrain which tokens may be selected when
generating text. For example, the grammar:
root ::= "yes" | "no"
will force the LLM to only output yes or no before exiting. This
is useful for shell scripts when the --no-display-prompt flag is
also supplied.
--grammar-file FNAME
File to read grammar from.
--fast Put llamafile into fast math mode. This disables algorithms that
reduce floating point rounding, e.g. Kahan summation, and cer‐
tain functions like expf() will be vectorized but handle under‐
flows less gracefully. It's unspecified whether llamafile runs
in fast or precise math mode when neither flag is specified.
--precise
Put llamafile into precise math mode. This enables algorithms
that reduce floating point rounding, e.g. Kahan summation, and
certain functions like expf() will always handle subnormals cor‐
rectly. It's unspecified whether llamafile runs in fast or pre‐
cise math mode when neither flag is specified.
--trap Put llamafile into math trapping mode. When floating point ex‐
ceptions occur, such as NaNs, overflow, and divide by zero, lla‐
mafile will print a warning to the console. This warning will
include a C++ backtrace the first time an exception is trapped.
The op graph will also be dumped to a file, and llamafile will
report the specific op where the exception occurred. This is
useful for troubleshooting when reporting issues. USing this
feature will disable sandboxing. Math trapping is only possible
if your CPU supports it. That is generally the case on AMD64,
however it's less common on ARM64.
--prompt-cache FNAME
File to cache prompt state for faster startup.
Default: none
-fa FNAME, --flash-attn
Enable Flash Attention. This is a mathematical shortcut that can
speed up inference for certain models. This feature is still un‐
der active development.
--prompt-cache-all
If specified, saves user input and generations to cache as well.
Not supported with --interactive or other interactive options.
--prompt-cache-ro
If specified, uses the prompt cache but does not update it.
--random-prompt
Start with a randomized prompt.
--image IMAGE_FILE
Path to an image file. This should be used with multimodal mod‐
els. Alternatively, it's possible to embed an image directly
into the prompt instead; in which case, it must be base64 en‐
coded into an HTML img tag URL with the image/jpeg MIME type.
See also the --mmproj flag for supplying the vision model.
-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.
-ins, --instruct
Run the program in instruction mode, which is specifically de‐
signed to work with Alpaca models that excel in completing tasks
based on user instructions.
Technical details: The user's input is internally prefixed with
the reverse prompt (or "### Instruction:" as the default), and
followed by "### Response:" (except if you just press Return
without any input, to keep generating a longer response).
By understanding and utilizing these interaction options, you
can create engaging and dynamic experiences with the LLaMA mod‐
els, tailoring the text generation process to your specific
needs.
-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 conver‐
sational 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.
--color
Enable colorized output to differentiate visually distinguishing
between prompts, user input, and generated text.
--no-display-prompt, --silent-prompt
Don't echo the prompt itself to standard output.
--keep N
Specifies number of tokens to keep from the initial prompt. The
default is -1 which means all tokens.
--multiline-input
Allows you to write or paste multiple lines without ending each
in '\'.
--cont-batching
Enables continuous batching, a.k.a. dynamic batching. is -1
which means all tokens.
--embedding
In CLI mode, the embedding flag may be use to print embeddings
to standard output. By default, embeddings are computed over a
whole prompt. However the --multiline flag may be passed, to
have a separate embeddings array computed for each line of text
in the prompt. In multiline mode, each embedding array will be
printed on its own line to standard output, where individual
floats are separated by space. If both the --multiline-input and
--interactive flags are passed, then a pretty-printed summary of
embeddings along with a cosine similarity matrix will be printed
to the terminal.
SERVER OPTIONS
The following options may be specified when llamafile is running in
--server mode.
--port PORT
Port to listen
Default: 8080
--host IPADDR
IP address to listen.
Default: 127.0.0.1
-to N, --timeout N
Server read/write timeout in seconds.
Default: 600
-np N, --parallel N
Number of slots for process requests.
Default: 1
-cb, --cont-batching
Enable continuous batching (a.k.a dynamic batching).
Default: disabled
-spf FNAME, --system-prompt-file FNAME
Set a file to load a system prompt (initial prompt of all
slots), this is useful for chat applications.
-a ALIAS, --alias ALIAS
Set an alias for the model. This will be added as the model
field in completion responses.
--path PUBLIC_PATH
Path from which to serve static files.
Default: /zip/llama.cpp/server/public
--nobrowser
Do not attempt to open a web browser tab at startup.
-gan N, --grp-attn-n N
Set the group attention factor to extend context size through
self-extend. The default value is 1 which means disabled. This
flag is used together with --grp-attn-w.
-gaw N, --grp-attn-w N
Set the group attention width to extend context size through
self-extend. The default value is 512. This flag is used to‐
gether with --grp-attn-n.
LOG OPTIONS
The following log options are available:
-ld LOGDIR, --logdir LOGDIR
Path under which to save YAML logs (no logging if unset)
--log-test
Run simple logging test
--log-disable
Disable trace logs
--log-enable
Enable trace logs
--log-file
Specify a log filename (without extension)
--log-new
Create a separate new log file on start. Each log file will have
unique name: <name>.<ID>.log
--log-append
Don't truncate the old log file.
EXAMPLES
Here's an example of how to run llama.cpp's built-in HTTP server. This
example uses LLaVA v1.5-7B, a multimodal LLM that works with llama.cpp's
recently-added support for image inputs.
llamafile \
-m llava-v1.5-7b-Q8_0.gguf \
--mmproj llava-v1.5-7b-mmproj-Q8_0.gguf \
--host 0.0.0.0
Here's an example of how to generate code for a libc function using the
llama.cpp command line interface, utilizing WizardCoder-Python-13B
weights:
llamafile \
-m wizardcoder-python-13b-v1.0.Q8_0.gguf --temp 0 -r '}\n' -r '```\n' \
-e -p '```c\nvoid *memcpy(void *dst, const void *src, size_t size) {\n'
Here's a similar example that instead utilizes Mistral-7B-Instruct
weights for prose composition:
llamafile \
-m mistral-7b-instruct-v0.2.Q5_K_M.gguf \
-p '[INST]Write a story about llamas[/INST]'
Here's an example of how llamafile can be used as an interactive chatbot
that lets you query knowledge contained in training data:
llamafile -m llama-65b-Q5_K.gguf -p '
The following is a conversation between a Researcher and their helpful AI
assistant Digital Athena which is a large language model trained on the
sum of human knowledge.
Researcher: Good morning.
Digital Athena: How can I help you today?
Researcher:' --interactive --color --batch_size 1024 --ctx_size 4096 \
--keep -1 --temp 0 --mirostat 2 --in-prefix ' ' --interactive-first \
--in-suffix 'Digital Athena:' --reverse-prompt 'Researcher:'
Here's an example of how you can use llamafile to summarize HTML URLs:
(
echo '[INST]Summarize the following text:'
links -codepage utf-8 \
-force-html \
-width 500 \
-dump
https://www.poetryfoundation.org/poems/48860/the-raven |
sed 's/ */ /g'
echo '[/INST]'
) | llamafile \
-m mistral-7b-instruct-v0.2.Q5_K_M.gguf \
-f /dev/stdin \
-c 0 \
--temp 0 \
-n 500 \
--no-display-prompt 2>/dev/null
Here's how you can use llamafile to describe a jpg/png/gif/bmp image:
llamafile --temp 0 \
--image lemurs.jpg \
-m llava-v1.5-7b-Q4_K.gguf \
--mmproj llava-v1.5-7b-mmproj-Q4_0.gguf \
-e -p '### User: What do you see?\n### Assistant: ' \
--no-display-prompt 2>/dev/null
If you wanted to write a script to rename all your image files, you
could use the following command to generate a safe filename:
llamafile --temp 0 \
--image ~/Pictures/lemurs.jpg \
-m llava-v1.5-7b-Q4_K.gguf \
--mmproj llava-v1.5-7b-mmproj-Q4_0.gguf \
--grammar 'root ::= [a-z]+ (" " [a-z]+)+' \
-e -p '### User: The image has...\n### Assistant: ' \
--no-display-prompt 2>/dev/null |
sed -e's/ /_/g' -e's/$/.jpg/'
three_baby_lemurs_on_the_back_of_an_adult_lemur.jpg
Here's an example of how to make an API request to the OpenAI API com‐
patible completions endpoint when your llamafile is running in the back‐
ground in --server mode.
curl -s
http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" -d '{
"model": "gpt-3.5-turbo",
"stream": true,
"messages": [
{
"role": "system",
"content": "You are a poetic assistant."
},
{
"role": "user",
"content": "Compose a poem that explains FORTRAN."
}
]
}' | python3 -c '
import json
import sys
json.dump(json.load(sys.stdin), sys.stdout, indent=2)
print()
PROTIP
The -ngl 35 flag needs to be passed in order to use GPUs made by NVIDIA
and AMD. It's not enabled by default since it sometimes needs to be
tuned based on the system hardware and model architecture, in order to
achieve optimal performance, and avoid compromising a shared display.
SEE ALSO
llamafile-quantize(1), llamafile-perplexity(1), llava-quantize(1),
zipalign(1), unzip(1)
Mozilla Ocho January 1, 2024 Mozilla Ocho
--
underground experts united
https://dataswamp.org/~incal
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