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+numind.NuExtract-v1.5.Q5_K_M.llamafile filter=lfs diff=lfs merge=lfs -text

README.md

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+# llamafile
+
+[![ci status](https://github.com/Mozilla-Ocho/llamafile/actions/workflows/ci.yml/badge.svg)](https://github.com/Mozilla-Ocho/llamafile/actions/workflows/ci.yml)<br/>
+
+
+**llamafile lets you distribute and run LLMs with a single file. ([announcement blog post](https://hacks.mozilla.org/2023/11/introducing-llamafile/))**
+
+llamafile aims to make open LLMs much more
+accessible to both developers and end users. They're doing that by
+combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one
+framework that collapses all the complexity of LLMs down to
+a single-file executable (called a "llamafile") that runs
+locally on most computers, with no installation.<br/><br/>
+
+llamafile is a Mozilla Builders project.
+## Quickstart
+
+The easiest way to try it for yourself is to download the example
+llamafile for the [numind.NuExtract](numind/NuExtract-1.5) model (license: [mit],
+[OpenAI](https://openai.com/policies/terms-of-use)). With llamafile, this you can run this model locally while consuming comparitively less resources and having better performance in CPU alone.
+
+1. Download [numind.NuExtract-v1.5.Q5_K_M.llamafile](https://huggingface.co/Devarui379/numind.NuExtract-v1.5-Q5_K_M-llamafile/resolve/main/numind.NuExtract-v1.5.Q5_K_M.llamafile?download=true) (2.78 GB).
+
+2. Open your computer's terminal.
+
+3. If you're using macOS, Linux, or BSD, you'll need to grant permission
+for your computer to execute this new file. (You only need to do this
+once.)
+
+```sh
+chmod +x numind.NuExtract-v1.5.Q5_K_M.llamafile
+```
+
+4. If you're on Windows, rename the file by adding ".exe" on the end.
+
+5. Run the llamafile. e.g.:
+
+```sh
+./numind.NuExtract-v1.5.Q5_K_M.llamafile
+```
+
+6. Your browser should open automatically and display a chat interface.
+(If it doesn't, just open your browser and point it at http://localhost:8080)
+
+7. When you're done chatting, return to your terminal and hit
+`Control-C` to shut down llamafile.
+
+**Having trouble? See the "Gotchas" section in the official github page of [llamafile](https://github.com/Mozilla-Ocho/llamafile) **
+
+## Distribution
+
+One good way to share a llamafile with your friends is by posting it on
+Hugging Face. If you do that, then it's recommended that you mention in
+your Hugging Face commit message what git revision or released version
+of llamafile you used when building your llamafile. That way everyone
+online will be able verify the provenance of its executable content. If
+you've made changes to the llama.cpp or cosmopolitan source code, then
+the Apache 2.0 license requires you to explain what changed. One way you
+can do that is by embedding a notice in your llamafile using `zipalign`
+that describes the changes, and mention it in your Hugging Face commit.
+
+## Documentation
+
+There's a manual page for each of the llamafile programs installed when you
+run `sudo make install`. The command manuals are also typeset as PDF
+files that you can download from the GitHub releases page. Lastly, most
+commands will display that information when passing the `--help` flag.
+
+## Running llamafile with models downloaded by third-party applications
+
+This section answers the question *"I already have a model downloaded locally by application X, can I use it with llamafile?"*. The general answer is "yes, as long as those models are locally stored in GGUF format" but its implementation can be more or less hacky depending on the application. A few examples (tested on a Mac) follow.
+
+### LM Studio
+[LM Studio](https://lmstudio.ai/) stores downloaded models in `~/.cache/lm-studio/models`, in subdirectories with the same name of the models (following HuggingFace's `account_name/model_name` format), with the same filename you saw when you chose to download the file.
+
+ So if you have downloaded e.g. the `llama-2-7b.Q2_K.gguf` file for `TheBloke/Llama-2-7B-GGUF`, you can run llamafile as follows:
+
+```
+cd ~/.cache/lm-studio/models/TheBloke/Llama-2-7B-GGUF
+llamafile -m llama-2-7b.Q2_K.gguf
+```
+
+### Ollama
+
+When you download a new model with [ollama](https://ollama.com), all its metadata will be stored in a manifest file under `~/.ollama/models/manifests/registry.ollama.ai/library/`. The directory and manifest file name are the model name as returned by `ollama list`. For instance, for `llama3:latest` the manifest file will be named `.ollama/models/manifests/registry.ollama.ai/library/llama3/latest`.
+
+The manifest maps each file related to the model (e.g. GGUF weights, license, prompt template, etc) to a sha256 digest. The digest corresponding to the element whose `mediaType` is `application/vnd.ollama.image.model` is the one referring to the model's GGUF file.
+
+Each sha256 digest is also used as a filename in the `~/.ollama/models/blobs` directory (if you look into that directory you'll see *only* those sha256-* filenames). This means you can directly run llamafile by passing the sha256 digest as the model filename. So if e.g. the `llama3:latest` GGUF file digest is `sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29`, you can run llamafile as follows:
+
+```
+cd ~/.ollama/models/blobs
+llamafile -m sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
+```
+
+
+
+## Security
+
+llamafile adds pledge() and SECCOMP sandboxing to llama.cpp. This is
+enabled by default. It can be turned off by passing the `--unsecure`
+flag. Sandboxing is currently only supported on Linux and OpenBSD on
+systems without GPUs; on other platforms it'll simply log a warning.
+
+Our approach to security has these benefits:
+
+1. After it starts up, your HTTP server isn't able to access the
+   filesystem at all. This is good, since it means if someone discovers
+   a bug in the llama.cpp server, then it's much less likely they'll be
+   able to access sensitive information on your machine or make changes
+   to its configuration. On Linux, we're able to sandbox things even
+   further; the only networking related system call the HTTP server will
+   allowed to use after starting up, is accept(). That further limits an
+   attacker's ability to exfiltrate information, in the event that your
+   HTTP server is compromised.
+
+2. The main CLI command won't be able to access the network at all. This
+   is enforced by the operating system kernel. It also won't be able to
+   write to the file system. This keeps your computer safe in the event
+   that a bug is ever discovered in the GGUF file format that lets
+   an attacker craft malicious weights files and post them online. The
+   only exception to this rule is if you pass the `--prompt-cache` flag
+   without also specifying `--prompt-cache-ro`. In that case, security
+   currently needs to be weakened to allow `cpath` and `wpath` access,
+   but network access will remain forbidden.
+
+Therefore your llamafile is able to protect itself against the outside
+world, but that doesn't mean you're protected from llamafile. Sandboxing
+is self-imposed. If you obtained your llamafile from an untrusted source
+then its author could have simply modified it to not do that. In that
+case, you can run the untrusted llamafile inside another sandbox, such
+as a virtual machine, to make sure it behaves how you expect.
+
+## Licensing
+
+While the llamafile project is Apache 2.0-licensed, the changes
+to llama.cpp are licensed under MIT (just like the llama.cpp project
+itself) so as to remain compatible and upstreamable in the future,
+should that be desired.
+
+
+
+[![Star History Chart](https://api.star-history.com/svg?repos=Mozilla-Ocho/llamafile&type=Date)](https://star-history.com/#Mozilla-Ocho/llamafile&Date)

numind.NuExtract-v1.5.Q5_K_M.llamafile

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