MiniMax-M2¶

On-the-fly conversion: these weights are not mirrored as preconverted .weights.h5 under zeromodels/. from_weights("<variant>") downloads the original safetensors from the Hub and converts them in process on every load, because checkpoints this large are impractical to re-host. Pass cache_converted=True to keep the converted result and skip the download and conversion next time. See Loading Weights.

MiniMax's M2 Mixture-of-Experts LLM, ported to pure Keras 3. Unlike MiniMax-Text-01, M2 uses full attention throughout rather than the lightning / full hybrid, in front of a sparse expert bank.

The hub checkpoints ship in FP8 and are dequantized during conversion. Memory is governed by total parameters, not active ones.

Variants¶

Load any of these with from_weights("<variant>").

Variant	Hub
`minimax-m2`	`MiniMaxAI/MiniMax-M2`

API¶

`MiniMaxM2Model`¶

The decoder backbone, no LM head. Returns {"last_hidden_state": (batch, seq, embed_dim)}.

Arg	Default	Meaning
`vocab_size`	`200064`	token vocabulary size
`embed_dim`	`3072`	model width
`mlp_dim`	`1536`	MLP inner width
`num_layers`	`62`	decoder blocks
`num_heads`	`48`	query heads
`num_kv_heads`	`8`	key/value heads (GQA)
`head_dim`	`128`	per-head width
`num_experts`	`256`	expert count
`num_experts_per_tok`	`8`	experts routed per token
`partial_rotary_factor`	`1.0`	fraction of each head that gets rotated
`rope_theta`	`5000000.0`	rotary base frequency
`norm_eps`	`1e-06`	RMSNorm epsilon
`tie_embeddings`	`False`	reuse the embedding matrix as the LM head

`MiniMaxM2TextGenerate`¶

MiniMaxM2Model plus a (tied) LM head. Returns {"logits": (batch, seq, vocab_size)} and adds .generate(). Same constructor arguments as MiniMaxM2Model.

generate(
    input_ids,
    attention_mask=None,
    max_new_tokens=None,
    eos_token_id=None,
    sampler=None,
    seed=None,
    **prefill_inputs,
)

Arg	Default	Meaning
`input_ids`	required	`(batch, seq)` token ids
`attention_mask`	`None`	`(batch, seq)` 1 = keep, 0 = padding
`max_new_tokens`	`None`	tokens to generate
`eos_token_id`	`None`	stop token (defaults to the tokenizer's)
`sampler`	`None`	sampling strategy; greedy when unset
`seed`	`None`	seed for stochastic samplers

`MiniMaxM2Tokenizer`¶

Tokenizer on the tokenizers backend.

MiniMaxM2Tokenizer(hf_id=None, tokenizer_file=None)

Arg	Default	Meaning
`hf_id`	`None`	Hub repo to pull the tokenizer files from
`tokenizer_file`	`None`	explicit path to a `tokenizer.json`

Calling it returns {"input_ids", "attention_mask"}, padded across the batch. It accepts a plain string or a list of strings (a batch). This tokenizer has no chat template, so pass a prompt you have already rendered rather than a message list. Decode with .decode(ids) for one sequence or .batch_decode(ids) for a batch.

End-to-end example¶

Single input¶

import os

os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from zeromodels.models.minimax_m2 import MiniMaxM2TextGenerate, MiniMaxM2Tokenizer

model = MiniMaxM2TextGenerate.from_weights("minimax-m2")
tokenizer = MiniMaxM2Tokenizer.from_weights("minimax-m2")

inputs = tokenizer("Explain rotary embeddings in one sentence.")
outputs = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(outputs[0]))

Batch¶

Pass a list of strings. The tokenizer pads them and generate runs the batch together:

prompts = [
    "The capital of France is",
    "In one sentence, what is a transformer?",
    "Write a haiku about GPUs.",
]
inputs = tokenizer(prompts)  # {"input_ids": (3, seq), "attention_mask": (3, seq)}
outputs = model.generate(**inputs, max_new_tokens=64)

for text in tokenizer.batch_decode(outputs):
    print(text)

Backbone only¶

from zeromodels.models.minimax_m2 import MiniMaxM2Model

backbone = MiniMaxM2Model.from_weights("minimax-m2")
hidden = backbone(inputs)["last_hidden_state"]  # (batch, seq, embed_dim)

Loading from the Hub¶

Any Hub repo with this architecture works via the hf: prefix, including community fine-tunes:

model = MiniMaxM2TextGenerate.from_weights("hf:MiniMaxAI/MiniMax-M2")

Lower memory¶

Larger checkpoints load in bf16 or weight-only quantized. See quantization.md:

model = MiniMaxM2TextGenerate.from_weights(
    "minimax-m2", quantization="int8", load_dtype="bfloat16"
)