Skip to content

MiniMax-Text-01

MiniMax's hybrid-attention Mixture-of-Experts LLM, ported to pure Keras 3. Most layers use lightning (linear) attention, with periodic full-softmax layers, in front of a sparse expert bank.

Port note: the lightning-attention normalization uses eps=1e-6; changing it breaks parity. Memory is governed by total parameters, not active ones.

Links:

See also minimax_m2.md.

Variants

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

Variant Hub
minimax-text-01 MiniMaxAI/MiniMax-Text-01-hf

API

MiniMaxModel

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 6144 model width
mlp_dim 9216 MLP inner width
num_layers 80 decoder blocks
num_heads 64 query heads
num_kv_heads 8 key/value heads (GQA)
head_dim 128 per-head width
num_experts 32 expert count
num_experts_per_tok 2 experts routed per token
layer_types None
block_size 256
full_attn_alpha 1.0
full_attn_beta 1.0
linear_attn_alpha 1.0
linear_attn_beta 1.0
mlp_alpha 1.0
mlp_beta 1.0
partial_rotary_factor 1.0 fraction of each head that gets rotated
rope_theta 10000000.0 rotary base frequency
norm_eps 1e-05 RMSNorm epsilon
tie_embeddings False reuse the embedding matrix as the LM head

MiniMaxGenerate

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

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

MiniMaxTokenizer

Tokenizer on the tokenizers backend.

MiniMaxTokenizer(hf_id=None, vocab_file=None, merges_file=None)
Arg Default Meaning
hf_id None Hub repo to pull the tokenizer files from
vocab_file None explicit path to the vocab file
merges_file None explicit path to the BPE merges file

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 kerasformers.models.minimax import MiniMaxGenerate, MiniMaxTokenizer

model = MiniMaxGenerate.from_weights("minimax-text-01")
tokenizer = MiniMaxTokenizer.from_weights("minimax-text-01")

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 kerasformers.models.minimax import MiniMaxModel

backbone = MiniMaxModel.from_weights("minimax-text-01")
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 = MiniMaxGenerate.from_weights("hf:MiniMaxAI/MiniMax-Text-01-hf")

Lower memory

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

model = MiniMaxGenerate.from_weights(
    "minimax-text-01", quantization="int8", low_memory=True, load_dtype="bfloat16"
)