Getting Started with llamafile

The easiest way to try it for yourself is to download our example llamafile for the Qwen3.5 model (license: Apache 2.0). Qwen3.5 is a recent LLM that can do more than just chat; you can also upload images and ask it questions about them. With llamafile, this all happens locally: no data ever leaves your computer.

NOTE: we chose this model because that's the smallest one we have built a llamafile for, so most likely to work out-of-the-box for you. Please let us know if you are still having issues with that! If, on the other hand, you have powerful hardware and/or GPUs, feel free to choose larger and more expressive models which should provide more accurate responses.

1. Download Qwen3.5-0.8B-Q8_0.llamafile (1.77 GB).

2. Open your computer's terminal.

   • 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.)

     chmod +x Qwen3.5-0.8B-Q8_0.llamafile
     

   • If you're on Windows, rename the file by adding ".exe" on the end.

3. Run the llamafile. e.g.:

   ./Qwen3.5-0.8B-Q8_0.llamafile
   

4. A chat interface will open in the terminal window. That's it: you can immediately start writing. You can also upload an image by using the /upload command and specifying the path to the image, or write /help to see the available commands).

5. Note that when llamafile is running, you can also chat with it using llama.cpp's Web UI: just open a browser window and connect to http://localhost:8080/.

6. When you're done chatting, Control-C to shut down llamafile.

Having trouble? See the Troubleshooting page.

JSON API Quickstart

As llamafile relies on llama.cpp for serving models, it comes with all its features. When it is started, in addition to hosting a web UI chat server at http://127.0.0.1:8080/, it also exposes an endpoint compatible with OpenAI API and Anthropic's Messages API. See the API server guide for llamafile endpoints, authentication, tool calling, and examples. For the exhaustive upstream reference, see the llama.cpp server README.

<details> <summary>Curl API Client Example</summary>

The simplest way to get started using the API is to copy and paste the following curl command into your terminal.

curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d '{
  "model": "LLaMA_CPP",
  "messages": [
      {
          "role": "system",
          "content": "You are LLAMAfile, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
      },
      {
          "role": "user",
          "content": "Write a limerick about python exceptions"
      }
    ]
}' | python3 -c '
import json
import sys
json.dump(json.load(sys.stdin), sys.stdout, indent=2)
print()
'

The response that's printed should look like the following:

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "In the world of Python, where magic breaks and errors occur,\nA script fails when it should not have failed.\nWith a `KeyError`, I can't access the key,\nSo I tell you to use the `except` clause!"
      }
    }
  ],
  "created": 1773659260,
  "model": "Qwen3.5-0.8B-Q8_0.gguf",
  "system_fingerprint": "b1773565177-7f5ee5496",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 52,
    "prompt_tokens": 49,
    "total_tokens": 101
  },
  "id": "chatcmpl-KOqwN6C0oRzINGZuFqZ95bU1iPfc6RFO",
  "timings": {
    "cache_n": 0,
    "prompt_n": 49,
    "prompt_ms": 54.944,
    "prompt_per_token_ms": 1.1213061224489795,
    "prompt_per_second": 891.8171228887594,
    "predicted_n": 52,
    "predicted_ms": 405.856,
    "predicted_per_token_ms": 7.804923076923076,
    "predicted_per_second": 128.1242608215722
  }
}

</details> <details> <summary>Python API Client example</summary>

If you've already developed your software using the openai Python package (that's published by OpenAI) then you should be able to port your app to talk to llamafile instead, by making a few changes to base_url and api_key. This example assumes you've run pip3 install openai to install OpenAI's client software, which is required by this example. Their package is just a simple Python wrapper around the OpenAI API interface, which can be implemented by any server.

#!/usr/bin/env python3
from openai import OpenAI
client = OpenAI(
    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
    api_key = "sk-no-key-required"
)
completion = client.chat.completions.create(
    model="LLaMA_CPP",
    messages=[
        {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
        {"role": "user", "content": "Write a limerick about python exceptions"}
    ]
)
print(completion.choices[0].message)

The above code will return a Python object like this:

ChatCompletionMessage(content="A script that crashes like a ghost,\nWhen it tries to solve the problem deep and fast.\nThe error message pops up in a bright light,\nAnd tells us what's wrong when we try to fix it.", refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None)

</details>

Using llamafile with external weights

Even though our pre-built llamafiles have the weights built-in, you don't have to use llamafile that way. Instead, you can download just the llamafile software (without any weights included) from our releases page. You can then use it alongside any external weights you may have on hand. External weights are particularly useful for Windows users because they enable you to work around Windows' 4GB executable file size limit.

For Windows users, here's an example for the gpt-oss LLM (whose size is >12GB):

curl -L -o llamafile.exe https://huggingface.co/mozilla-ai/llamafile_0.10/resolve/main/llamafile_0.10.1
curl -L -o gpt-oss.gguf https://huggingface.co/unsloth/gpt-oss-20b-GGUF/resolve/main/gpt-oss-20b-Q5_K_S.gguf
./llamafile.exe -m gpt-oss.gguf

Windows users may need to change ./llamafile.exe to .\llamafile.exe when running the above command.

Which release file do I download?

On the releases page each release ships several independent single-file programs. They are not bundled together inside the llamafile executable: if you want to download all of them at once, choose the zip download; otherwise, pick the one that matches the task you want to perform:

| File | What it is | | --- | --- | | llamafile | Runs LLMs (chat/completions). This is the program referenced throughout these docs. | | llamafile-thin | The same program, without the pre-built GPU libraries appended (see below). | | whisperfile | Speech-to-text, built on whisper.cpp. See the whisperfile docs. | | transcribefile | Speech-to-text, built on transcribe.cpp. | | diffusionfile | Image generation (Stable Diffusion). | | zipalign | A small utility for embedding weights or other assets into a llamafile. |

llamafile vs llamafile-thin

Both files contain the same executable code. The difference is whether pre-built GPU backend libraries are bundled together with it:

• llamafile-thin is the base binary, so it is the smaller download. It seamlessly runs on different operating systems and CPU architectures. To run on GPU, it requires a matching backend library to be available on your system. On Apple Silicon this is fully transparent: if the system does not have the required library, llamafile compiles one on the fly. On the other systems, you can build GPU libraries yourself with llamafile/cuda.sh (NVIDIA), llamafile/rocm.sh (AMD), or llamafile/vulkan.sh (NVIDIA + AMD + others). Each library requires the respective GPU toolchain to be installed before you run a build. As an alternative, you can find libraries for the latest release on our HuggingFace repo.
• llamafile is that same binary with ggml-cuda and ggml-vulkan bundled, as x86_64 Linux (.so) and Windows (.dll) artifacts (a single set of libraries, with no separate ARM build). Those backends work with no build step of your own — you only need a driver that provides the corresponding runtime API. The bundled libraries are what makes this file larger.

Note that ROCm support is still experimental and thus not automatically bundled in either file. AMD users who do not want to use the Vulkan backend have to build ggml-rocm with llamafile/rocm.sh, or download the pre-built ggml-rocm dylibs from llamafile's HuggingFace repo, regardless of which llamafile executable they are running.

The bundled libraries are x86_64 Linux and Windows artifacts only. On any other platform (macOS, or a non-x86_64 architecture such as an aarch64/ARM64 Linux host, even one with an NVIDIA GPU) neither file carries an applicable pre-built backend, so the two downloads behave the same there and GPU support comes from the usual runtime path instead.

Use llamafile unless you want the smallest download and only need CPU inference, you have an Apple Silicon Mac, or have already set up your own GPU backend libraries.

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 stores downloaded models in ~/.cache/lm-studio/models/lmstudio-community, in subdirectories with the same name of the models, minus their quantization level. So if you have downloaded e.g. the gpt-oss-20b-MXFP4.gguf file, it will be stored in ~/.cache/lm-studio/models/lmstudio-community/gpt-oss-20b-GGUF/ and you can run llamafile as follows:

llamafile -m ~/.cache/lm-studio/models/lmstudio-community/gpt-oss-20b-GGUF/gpt-oss-20b-MXFP4.gguf

Ollama

When you download a new model with ollama, 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

Note that Ollama's GGUF weights do not always work with llama.cpp (see e.g. here), and as llamafile relies on llama.cpp this trick might not always work for you.