## Generate Text with LLMs and MLX

The easiest way to get started is to install the `mlx-lm` package:

**With `pip`**:

```sh
pip install mlx-lm
```

**With `conda`**:

```sh
conda install -c conda-forge mlx-lm
```

The `mlx-lm` package also has:

- [LoRA, QLoRA, and full fine-tuning](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md)
- [Merging models](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/MERGE.md)
- [HTTP model serving](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/SERVER.md)

### Quick Start

To generate text with an LLM use:

```bash
mlx_lm.generate --prompt "Hi!"
```

To chat with an LLM use:

```bash
mlx_lm.chat
```

This will give you a chat REPL that you can use to interact with the LLM. The
chat context is preserved during the lifetime of the REPL.

Commands in `mlx-lm` typically take command line options which let you specify
the model, sampling parameters, and more. Use `-h` to see a list of available
options for a command, e.g.:

```bash
mlx_lm.generate -h
```

### Python API

You can use `mlx-lm` as a module:

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Mistral-7B-Instruct-v0.3-4bit")

prompt = "Write a story about Einstein"

messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```

To see a description of all the arguments you can do:

```
>>> help(generate)
```

Check out the [generation
example](https://github.com/ml-explore/mlx-examples/tree/main/llms/mlx_lm/examples/generate_response.py)
to see how to use the API in more detail.

The `mlx-lm` package also comes with functionality to quantize and optionally
upload models to the Hugging Face Hub.

You can convert models in the Python API with:

```python
from mlx_lm import convert

repo = "mistralai/Mistral-7B-Instruct-v0.3"
upload_repo = "mlx-community/My-Mistral-7B-Instruct-v0.3-4bit"

convert(repo, quantize=True, upload_repo=upload_repo)
```

This will generate a 4-bit quantized Mistral 7B and upload it to the repo
`mlx-community/My-Mistral-7B-Instruct-v0.3-4bit`. It will also save the
converted model in the path `mlx_model` by default.

To see a description of all the arguments you can do:

```
>>> help(convert)
```

#### Streaming

For streaming generation, use the `stream_generate` function. This returns a
generator object which streams the output text. For example,

```python
from mlx_lm import load, stream_generate

repo = "mlx-community/Mistral-7B-Instruct-v0.3-4bit"
model, tokenizer = load(repo)

prompt = "Write a story about Einstein"

messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

for t in stream_generate(model, tokenizer, prompt, max_tokens=512):
    print(t, end="", flush=True)
print()
```

### Command Line

You can also use `mlx-lm` from the command line with:

```
mlx_lm.generate --model mistralai/Mistral-7B-Instruct-v0.3 --prompt "hello"
```

This will download a Mistral 7B model from the Hugging Face Hub and generate
text using the given prompt.

For a full list of options run:

```
mlx_lm.generate --help
```

To quantize a model from the command line run:

```
mlx_lm.convert --hf-path mistralai/Mistral-7B-Instruct-v0.3 -q
```

For more options run:

```
mlx_lm.convert --help
```

You can upload new models to Hugging Face by specifying `--upload-repo` to
`convert`. For example, to upload a quantized Mistral-7B model to the
[MLX Hugging Face community](https://huggingface.co/mlx-community) you can do:

```
mlx_lm.convert \
    --hf-path mistralai/Mistral-7B-Instruct-v0.3 \
    -q \
    --upload-repo mlx-community/my-4bit-mistral
```

### Long Prompts and Generations 

`mlx-lm` has some tools to scale efficiently to long prompts and generations:

- A rotating fixed-size key-value cache.
- Prompt caching

To use the rotating key-value cache pass the argument `--max-kv-size n` where
`n` can be any integer. Smaller values like `512` will use very little RAM but
result in worse quality. Larger values like `4096` or higher will use more RAM
but have better quality.

Caching prompts can substantially speedup reusing the same long context with
different queries. To cache a prompt use `mlx_lm.cache_prompt`. For example:

```bash
cat prompt.txt | mlx_lm.cache_prompt \
  --model mistralai/Mistral-7B-Instruct-v0.3 \
  --prompt - \
  --prompt-cache-file mistral_prompt.safetensors
``` 

Then use the cached prompt with `mlx_lm.generate`:

```
mlx_lm.generate \
    --prompt-cache-file mistral_prompt.safetensors \
    --prompt "\nSummarize the above text."
```

The cached prompt is treated as a prefix to the supplied prompt. Also notice
when using a cached prompt, the model to use is read from the cache and need
not be supplied explicitly.

Prompt caching can also be used in the Python API in order to to avoid
recomputing the prompt. This is useful in multi-turn dialogues or across
requests that use the same context. See the
[example](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/examples/chat.py)
for more usage details.

### Supported Models

`mlx-lm` supports thousands of Hugging Face format LLMs. If the model you want to
run is not supported, file an
[issue](https://github.com/ml-explore/mlx-examples/issues/new) or better yet,
submit a pull request.

Here are a few examples of Hugging Face models that work with this example:

- [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
- [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat)
- [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
- [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B)
- [pfnet/plamo-13b](https://huggingface.co/pfnet/plamo-13b)
- [pfnet/plamo-13b-instruct](https://huggingface.co/pfnet/plamo-13b-instruct)
- [stabilityai/stablelm-2-zephyr-1_6b](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b)
- [internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b)

Most
[Mistral](https://huggingface.co/models?library=transformers,safetensors&other=mistral&sort=trending),
[Llama](https://huggingface.co/models?library=transformers,safetensors&other=llama&sort=trending),
[Phi-2](https://huggingface.co/models?library=transformers,safetensors&other=phi&sort=trending),
and
[Mixtral](https://huggingface.co/models?library=transformers,safetensors&other=mixtral&sort=trending)
style models should work out of the box.

For some models (such as `Qwen` and `plamo`) the tokenizer requires you to
enable the `trust_remote_code` option. You can do this by passing
`--trust-remote-code` in the command line. If you don't specify the flag
explicitly, you will be prompted to trust remote code in the terminal when
running the model. 

For `Qwen` models you must also specify the `eos_token`. You can do this by
passing `--eos-token "<|endoftext|>"` in the command
line. 

These options can also be set in the Python API. For example:

```python
model, tokenizer = load(
    "qwen/Qwen-7B",
    tokenizer_config={"eos_token": "<|endoftext|>", "trust_remote_code": True},
)
```
