Mlx llm package (#301)

* fix converter

* add recursive files

* remove gitignore

* remove gitignore

* add packages properly

* read me update

* remove dup readme

* relative

* fix convert

* fix community name

* fix url

* version

llms/mlx_lm/README.md

@@ -0,0 +1,7 @@
+## Generate Text with MLX and :hugs: Hugging Face
+
+This an example of large language model text generation that can pull models from
+the Hugging Face Hub.
+
+For more information on this example, see the
+[README](../README.md) in the parent directory.

llms/mlx_lm/UPLOAD.md

@@ -0,0 +1,37 @@
+### Packaging for PyPI
+
+Install `build` and `twine`:
+
+```
+pip install --user --upgrade build
+pip install --user --upgrade twine
+```
+
+Generate the source distribution and wheel:
+
+```
+python -m build
+```
+
+> [!warning]
+> Use a test server first
+
+#### Test Upload
+
+Upload to test server:
+
+```
+python -m twine upload --repository testpypi dist/*
+```
+
+Install from test server and check that it works:
+
+```
+python -m pip install --index-url https://test.pypi.org/simple/ --no-deps mlx-lm
+```
+
+#### Upload
+
+```
+python -m twine upload dist/*
+```

llms/mlx_lm/__init__.py

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+from .convert import convert
+from .utils import generate, load

llms/mlx_lm/convert.py

@@ -0,0 +1,205 @@
+import argparse
+import copy
+import glob
+import json
+from pathlib import Path
+from typing import Dict, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+import transformers
+from mlx.utils import tree_flatten
+
+from .utils import get_model_path, load
+
+MAX_FILE_SIZE_GB = 15
+
+
+def configure_parser() -> argparse.ArgumentParser:
+    """
+    Configures and returns the argument parser for the script.
+
+    Returns:
+        argparse.ArgumentParser: Configured argument parser.
+    """
+    parser = argparse.ArgumentParser(
+        description="Convert Hugging Face model to MLX format"
+    )
+
+    parser.add_argument("--hf-path", type=str, help="Path to the Hugging Face model.")
+    parser.add_argument(
+        "--mlx-path", type=str, default="mlx_model", help="Path to save the MLX model."
+    )
+    parser.add_argument(
+        "-q", "--quantize", help="Generate a quantized model.", action="store_true"
+    )
+    parser.add_argument(
+        "--q-group-size", help="Group size for quantization.", type=int, default=64
+    )
+    parser.add_argument(
+        "--q-bits", help="Bits per weight for quantization.", type=int, default=4
+    )
+    parser.add_argument(
+        "--dtype",
+        help="Type to save the parameters, ignored if -q is given.",
+        type=str,
+        choices=["float16", "bfloat16", "float32"],
+        default="float16",
+    )
+    parser.add_argument(
+        "--upload-repo",
+        help="The Hugging Face repo to upload the model to.",
+        type=str,
+        default=None,
+    )
+    return parser
+
+
+def fetch_from_hub(
+    model_path: str,
+) -> Tuple[Dict, dict, transformers.PreTrainedTokenizer]:
+    model_path = get_model_path(model_path)
+
+    weight_files = glob.glob(f"{model_path}/*.safetensors")
+    if not weight_files:
+        raise FileNotFoundError(f"No safetensors found in {model_path}")
+
+    weights = {}
+    for wf in weight_files:
+        weights.update(mx.load(wf).items())
+
+    config = transformers.AutoConfig.from_pretrained(model_path)
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_path)
+
+    return weights, config.to_dict(), tokenizer
+
+
+def quantize_model(
+    weights: dict, config: dict, hf_path: str, q_group_size: int, q_bits: int
+) -> tuple:
+    """
+    Applies quantization to the model weights.
+
+    Args:
+        weights (dict): Model weights.
+        config (dict): Model configuration.
+        hf_path (str): HF model path..
+        q_group_size (int): Group size for quantization.
+        q_bits (int): Bits per weight for quantization.
+
+    Returns:
+        tuple: Tuple containing quantized weights and config.
+    """
+    quantized_config = copy.deepcopy(config)
+    model, _ = load(hf_path)
+    model.load_weights(list(weights.items()))
+
+    nn.QuantizedLinear.quantize_module(model, q_group_size, q_bits)
+    quantized_config["quantization"] = {
+        "group_size": q_group_size,
+        "bits": q_bits,
+    }
+    quantized_weights = dict(tree_flatten(model.parameters()))
+
+    return quantized_weights, quantized_config
+
+
+def make_shards(weights: dict, max_file_size_gb: int = MAX_FILE_SIZE_GB) -> list:
+    """
+    Splits the weights into smaller shards.
+
+    Args:
+        weights (dict): Model weights.
+        max_file_size_gb (int): Maximum size of each shard in gigabytes.
+
+    Returns:
+        list: List of weight shards.
+    """
+    max_file_size_bytes = max_file_size_gb << 30
+    shards = []
+    shard, shard_size = {}, 0
+    for k, v in weights.items():
+        estimated_size = v.size * v.dtype.size
+        if shard_size + estimated_size > max_file_size_bytes:
+            shards.append(shard)
+            shard, shard_size = {}, 0
+        shard[k] = v
+        shard_size += estimated_size
+    shards.append(shard)
+    return shards
+
+
+def upload_to_hub(path: str, upload_repo: str, hf_path: str):
+    """
+    Uploads the model to Hugging Face hub.
+
+    Args:
+        path (str): Local path to the model.
+        upload_repo (str): Name of the HF repo to upload to.
+        hf_path (str): Path to the original Hugging Face model.
+    """
+    import os
+
+    from huggingface_hub import HfApi, ModelCard, logging
+
+    card = ModelCard.load(hf_path)
+    card.data.tags = ["mlx"] if card.data.tags is None else card.data.tags + ["mlx"]
+    card.text = f"""
+# {upload_repo}
+This model was converted to MLX format from [`{hf_path}`]().
+Refer to the [original model card](https://huggingface.co/{hf_path}) for more details on the model.
+## Use with mlx
+```bash
+pip install mlx
+git clone https://github.com/ml-explore/mlx-examples.git
+cd mlx-examples/llms/hf_llm
+python generate.py --model {upload_repo} --prompt "My name is"
+```
+"""
+    card.save(os.path.join(path, "README.md"))
+
+    logging.set_verbosity_info()
+
+    api = HfApi()
+    api.create_repo(repo_id=upload_repo, exist_ok=True)
+    api.upload_folder(
+        folder_path=path,
+        repo_id=upload_repo,
+        repo_type="model",
+    )
+
+
+def convert(
+    hf_path: str,
+    mlx_path: str = "mlx_model",
+    quantize: bool = False,
+    q_group_size: int = 64,
+    q_bits: int = 4,
+    dtype: str = "float16",
+    upload_repo: str = None,
+):
+    print("[INFO] Loading")
+    weights, config, tokenizer = fetch_from_hub(hf_path)
+    dtype = mx.float16 if quantize else getattr(mx, dtype)
+    weights = {k: v.astype(dtype) for k, v in weights.items()}
+    if quantize:
+        print("[INFO] Quantizing")
+        weights, config = quantize_model(weights, config, hf_path, q_group_size, q_bits)
+
+    mlx_path = Path(mlx_path)
+    mlx_path.mkdir(parents=True, exist_ok=True)
+    shards = make_shards(weights)
+    for i, shard in enumerate(shards):
+        mx.save_safetensors(str(mlx_path / f"weights.{i:02d}.safetensors"), shard)
+    tokenizer.save_pretrained(mlx_path)
+    with open(mlx_path / "config.json", "w") as fid:
+        json.dump(config, fid, indent=4)
+
+    if upload_repo is not None:
+        upload_to_hub(mlx_path, upload_repo, hf_path)
+
+
+if __name__ == "__main__":
+    parser = configure_parser()
+    args = parser.parse_args()
+    convert(**vars(args))

llms/mlx_lm/generate.py

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+import argparse
+import time
+
+import mlx.core as mx
+
+from .utils import generate_step, load
+
+DEFAULT_MODEL_PATH = "mlx_model"
+DEFAULT_PROMPT = "hello"
+DEFAULT_MAX_TOKENS = 100
+DEFAULT_TEMP = 0.6
+DEFAULT_SEED = 0
+
+
+def setup_arg_parser():
+    """Set up and return the argument parser."""
+    parser = argparse.ArgumentParser(description="LLM inference script")
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="mlx_model",
+        help="The path to the local model directory or Hugging Face repo.",
+    )
+    parser.add_argument(
+        "--prompt", default=DEFAULT_PROMPT, help="Message to be processed by the model"
+    )
+    parser.add_argument(
+        "--max-tokens",
+        "-m",
+        type=int,
+        default=DEFAULT_MAX_TOKENS,
+        help="Maximum number of tokens to generate",
+    )
+    parser.add_argument(
+        "--temp", type=float, default=DEFAULT_TEMP, help="Sampling temperature"
+    )
+    parser.add_argument("--seed", type=int, default=DEFAULT_SEED, help="PRNG seed")
+    return parser
+
+
+def main(args):
+    mx.random.seed(args.seed)
+    model, tokenizer = load(args.model)
+    print("=" * 10)
+    print("Prompt:", args.prompt)
+    prompt = tokenizer.encode(args.prompt)
+    prompt = mx.array(prompt)
+    tic = time.time()
+    tokens = []
+    skip = 0
+    for token, n in zip(
+        generate_step(prompt, model, args.temp), range(args.max_tokens)
+    ):
+        if token == tokenizer.eos_token_id:
+            break
+        if n == 0:
+            prompt_time = time.time() - tic
+            tic = time.time()
+        tokens.append(token.item())
+        s = tokenizer.decode(tokens)
+        print(s[skip:], end="", flush=True)
+        skip = len(s)
+    print(tokenizer.decode(tokens)[skip:], flush=True)
+    gen_time = time.time() - tic
+    print("=" * 10)
+    if len(tokens) == 0:
+        print("No tokens generated for this prompt")
+        return
+    prompt_tps = prompt.size / prompt_time
+    gen_tps = (len(tokens) - 1) / gen_time
+    print(f"Prompt: {prompt_tps:.3f} tokens-per-sec")
+    print(f"Generation: {gen_tps:.3f} tokens-per-sec")
+
+
+if __name__ == "__main__":
+    parser = setup_arg_parser()
+    args = parser.parse_args()
+    main(args)

llms/mlx_lm/models/base.py

@@ -0,0 +1,15 @@
+import inspect
+from dataclasses import dataclass
+
+
+@dataclass
+class BaseModelArgs:
+    @classmethod
+    def from_dict(cls, params):
+        return cls(
+            **{
+                k: v
+                for k, v in params.items()
+                if k in inspect.signature(cls).parameters
+            }
+        )

llms/mlx_lm/models/llama.py

@@ -0,0 +1,202 @@
+from dataclasses import dataclass
+from typing import Dict, Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    hidden_size: int
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    rms_norm_eps: float
+    vocab_size: int
+    num_key_value_heads: int = None
+    rope_theta: float = 10000
+    rope_traditional: bool = False
+    model_type: str = None
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        if self.rope_scaling:
+            required_keys = {"factor", "type"}
+            if not all(key in self.rope_scaling for key in required_keys):
+                raise ValueError(f"rope_scaling must contain keys {required_keys}")
+
+            if self.rope_scaling["type"] != "linear":
+                raise ValueError("rope_scaling 'type' currently only supports 'linear'")
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dims: int, eps: float = 1e-5):
+        super().__init__()
+        self.weight = mx.ones((dims,))
+        self.eps = eps
+
+    def _norm(self, x):
+        return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
+
+    def __call__(self, x):
+        output = self._norm(x.astype(mx.float32)).astype(x.dtype)
+        return self.weight * output
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+
+        self.repeats = n_heads // n_kv_heads
+
+        head_dim = args.hidden_size // n_heads
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
+
+        rope_scale = (
+            1 / args.rope_scaling["factor"]
+            if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
+            else 1
+        )
+        self.rope = nn.RoPE(
+            head_dim,
+            traditional=args.rope_traditional,
+            base=args.rope_theta,
+            scale=rope_scale,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+
+        def repeat(a):
+            a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
+            return a.reshape([B, self.n_heads, L, -1])
+
+        if self.repeats > 1:
+            keys, values = map(repeat, (keys, values))
+
+        if cache is not None:
+            key_cache, value_cache = cache
+            queries = self.rope(queries, offset=key_cache.shape[2])
+            keys = self.rope(keys, offset=key_cache.shape[2])
+            keys = mx.concatenate([key_cache, keys], axis=2)
+            values = mx.concatenate([value_cache, values], axis=2)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
+        if mask is not None:
+            scores += mask
+        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
+        output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output), (keys, values)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(args)
+        self.mlp = MLP(args.hidden_size, args.intermediate_size)
+        self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        r, cache = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out, cache
+
+
+class LlamaModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.vocab_size = args.vocab_size
+        self.num_hidden_layers = args.num_hidden_layers
+        assert self.vocab_size > 0
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
+        ]
+        self.norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        h = self.embed_tokens(inputs)
+
+        mask = None
+        if h.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
+            mask = mask.astype(h.dtype)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        for e, layer in enumerate(self.layers):
+            h, cache[e] = layer(h, mask, cache[e])
+
+        return self.norm(h), cache
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.model = LlamaModel(args)
+        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out, cache = self.model(inputs, cache)
+        return self.lm_head(out), cache

llms/mlx_lm/models/phi2.py

@@ -0,0 +1,138 @@
+import math
+from dataclasses import dataclass
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    n_positions: int = 2048
+    vocab_size: int = 51200
+    n_embd: int = 2560
+    n_head: int = 32
+    n_layer: int = 32
+    rotary_dim: int = 32
+
+
+class LayerNorm(nn.LayerNorm):
+    def __call__(self, x: mx.array) -> mx.array:
+        return super().__call__(x.astype(mx.float32)).astype(x.dtype)
+
+
+class RoPEAttention(nn.Module):
+    def __init__(self, dims: int, n_head: int, rotary_dim: int):
+        super().__init__()
+
+        self.n_head = n_head
+
+        self.q_proj = nn.Linear(dims, dims)
+        self.k_proj = nn.Linear(dims, dims)
+        self.v_proj = nn.Linear(dims, dims)
+        self.dense = nn.Linear(dims, dims)
+
+        self.rope = nn.RoPE(rotary_dim, traditional=False)
+
+    def __call__(self, x, mask=None, cache=None):
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+
+        # Extract some shapes
+        n_head = self.n_head
+        B, L, D = queries.shape
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, n_head, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, n_head, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, n_head, -1).transpose(0, 2, 1, 3)
+
+        # Add RoPE to the queries and keys and combine them with the cache
+        if cache is not None:
+            key_cache, value_cache = cache
+            queries = self.rope(queries, offset=key_cache.shape[2])
+            keys = self.rope(keys, offset=key_cache.shape[2])
+            keys = mx.concatenate([key_cache, keys], axis=2)
+            values = mx.concatenate([value_cache, values], axis=2)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        queries = queries.astype(mx.float32)
+        keys = keys.astype(mx.float32)
+
+        # Finally perform the attention computation
+        scale = math.sqrt(1 / queries.shape[-1])
+        scores = (queries * scale) @ keys.transpose(0, 1, 3, 2)
+        if mask is not None:
+            scores = scores + mask
+
+        scores = mx.softmax(scores, axis=-1).astype(values.dtype)
+        values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.dense(values_hat), (keys, values)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.fc1 = nn.Linear(dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, dim)
+        self.act = nn.GELU(approx="precise")
+
+    def __call__(self, x) -> mx.array:
+        return self.fc2(self.act(self.fc1(x)))
+
+
+class ParallelBlock(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        dims = config.n_embd
+        mlp_dims = dims * 4
+        self.self_attn = RoPEAttention(dims, config.n_head, config.rotary_dim)
+        self.input_layernorm = LayerNorm(dims)
+        self.mlp = MLP(dims, mlp_dims)
+
+    def __call__(self, x, mask, cache):
+        h = self.input_layernorm(x)
+        attn_h, cache = self.self_attn(h, mask, cache)
+        ff_h = self.mlp(h)
+        return attn_h + ff_h + x, cache
+
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.n_embd)
+        self.layers = [ParallelBlock(config) for i in range(config.n_layer)]
+        self.final_layernorm = LayerNorm(config.n_embd)
+
+    def __call__(self, x, mask, cache):
+        x = self.embed_tokens(x)
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        for e, layer in enumerate(self.layers):
+            x, cache[e] = layer(x, mask, cache[e])
+        return self.final_layernorm(x), cache
+
+
+class Model(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.model = Transformer(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: mx.array = None,
+        cache: mx.array = None,
+    ) -> tuple[mx.array, mx.array]:
+        mask = None
+        if x.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
+            mask = mask.astype(x.dtype)
+
+        y, cache = self.model(x, mask, cache)
+        return self.lm_head(y), cache

llms/mlx_lm/requirements.txt

@@ -0,0 +1,4 @@
+mlx
+numpy
+transformers
+protobuf

llms/mlx_lm/utils.py

@@ -0,0 +1,180 @@
+import glob
+import json
+import logging
+from pathlib import Path
+from typing import Generator, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer, PreTrainedTokenizer
+
+# Local imports
+from .models import llama, phi2
+from .models.base import BaseModelArgs
+
+# Constants
+MODEL_MAPPING = {
+    "llama": llama,
+    "mistral": llama,  # mistral is compatible with llama
+    "phi": phi2,
+}
+
+
+def _get_classes(config: dict):
+    """
+    Retrieve the model and model args classes based on the configuration.
+
+    Args:
+        config (dict): The model configuration.
+
+    Returns:
+        A tuple containing the Model class and the ModelArgs class.
+    """
+    model_type = config["model_type"]
+    if model_type not in MODEL_MAPPING:
+        msg = f"Model type {model_type} not supported."
+        logging.error(msg)
+        raise ValueError(msg)
+
+    arch = MODEL_MAPPING[model_type]
+    return arch.Model, arch.ModelArgs
+
+
+def get_model_path(path_or_hf_repo: str) -> Path:
+    """
+    Ensures the model is available locally. If the path does not exist locally,
+    it is downloaded from the Hugging Face Hub.
+
+    Args:
+        path_or_hf_repo (str): The local path or Hugging Face repository ID of the model.
+
+    Returns:
+        Path: The path to the model.
+    """
+    model_path = Path(path_or_hf_repo)
+    if not model_path.exists():
+        model_path = Path(
+            snapshot_download(
+                repo_id=path_or_hf_repo,
+                allow_patterns=["*.json", "*.safetensors", "*.py", "tokenizer.model"],
+            )
+        )
+    return model_path
+
+
+def generate_step(
+    prompt: mx.array, model: nn.Module, temp: float = 0.0
+) -> Generator[mx.array, None, None]:
+    """
+    A generator producing text based on the given prompt from the model.
+
+    Args:
+        prompt (mx.array): The input prompt.
+        model (nn.Module): The model to use for generation.
+        temp (float): The temperature for sampling. If temp is 0, use max sampling.
+
+    Yields:
+        Generator[mx.array]: A generator producing one token per call.
+    """
+
+    def sample(logits: mx.array) -> mx.array:
+        return (
+            mx.argmax(logits, axis=-1)
+            if temp == 0
+            else mx.random.categorical(logits * (1 / temp))
+        )
+
+    y = prompt
+    cache = None
+    while True:
+        logits, cache = model(y[None], cache=cache)
+        logits = logits[:, -1, :]
+        y = sample(logits)
+        yield y
+
+
+def generate(
+    model: nn.Module,
+    tokenizer: PreTrainedTokenizer,
+    prompt: str,
+    temp: float = 0.0,
+    max_tokens: int = 100,
+    verbose: bool = False,
+) -> str:
+    """
+    Generate text from the model.
+
+    Args:
+       model (nn.Module): The language model.
+       tokenizer (PreTrainedTokenizer): The tokenizer.
+       prompt (str): The string prompt.
+       temp (float): The temperature for sampling (default 0).
+       max_tokens (int): The maximum number of tokens (default 100).
+    """
+
+    prompt = mx.array(tokenizer.encode(prompt))
+
+    tokens = []
+    skip = 0
+    for token, _ in zip(generate_step(prompt, model, temp), range(max_tokens)):
+        if token == tokenizer.eos_token_id:
+            break
+
+        tokens.append(token.item())
+
+        if verbose:
+            s = tokenizer.decode(tokens)
+            print(s[skip:], end="", flush=True)
+            skip = len(s)
+
+    tokens = tokenizer.decode(tokens)[skip:]
+    if verbose:
+        print(tokens, flush=True)
+    return tokens
+
+
+def load(path_or_hf_repo: str) -> Tuple[nn.Module, PreTrainedTokenizer]:
+    """
+    Load the model from a given path or a huggingface repository.
+
+    Args:
+        path_or_hf_repo (str): The path or the huggingface repository to load the model from.
+
+    Returns:
+        Tuple[nn.Module, PreTrainedTokenizer]: The loaded model and tokenizer.
+
+    Raises:
+        FileNotFoundError: If config file or safetensors are not found.
+        ValueError: If model class or args class are not found.
+    """
+    model_path = get_model_path(path_or_hf_repo)
+
+    try:
+        with open(model_path / "config.json", "r") as f:
+            config = json.load(f)
+            quantization = config.get("quantization", None)
+    except FileNotFoundError:
+        logging.error(f"Config file not found in {model_path}")
+        raise
+    weight_files = glob.glob(str(model_path / "*.safetensors"))
+    if not weight_files:
+        logging.error(f"No safetensors found in {model_path}")
+        raise FileNotFoundError(f"No safetensors found in {model_path}")
+    weights = {}
+    for wf in weight_files:
+        weights.update(mx.load(wf))
+
+    model_class, model_args_class = _get_classes(config=config)
+
+    model_args = model_args_class.from_dict(config)
+    model = model_class(model_args)
+
+    if quantization is not None:
+        nn.QuantizedLinear.quantize_module(model, **quantization)
+
+    model.load_weights(list(weights.items()))
+
+    mx.eval(model.parameters())
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    return model, tokenizer