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Mixtral

Mistral AI's sparse Mixture-of-Experts LLM, ported to pure Keras 3. The attention stack matches Mistral; each MLP is replaced by a router over experts, of which only num_experts_per_tok run per token.

Memory is governed by total parameters, not active ones: every expert stays resident, so plan capacity for the full weight count even though a fraction runs per token.

Links:

See also mistral.md, mistral3.md.

Variants

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

Variant Hub
mixtral-8x7b mistralai/Mixtral-8x7B-v0.1
mixtral-8x7b-instruct mistralai/Mixtral-8x7B-Instruct-v0.1
mixtral-8x22b mistralai/Mixtral-8x22B-v0.1
mixtral-8x22b-instruct mistralai/Mixtral-8x22B-Instruct-v0.1

API

MixtralModel

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

Arg Default Meaning
vocab_size 32000 token vocabulary size
embed_dim 4096 model width
mlp_dim 14336 MLP inner width
num_layers 32 decoder blocks
num_heads 32 query heads
num_kv_heads 8 key/value heads (GQA)
head_dim None per-head width
num_experts 8 expert count
num_experts_per_tok 2 experts routed per token
norm_eps 1e-05 RMSNorm epsilon
rope_theta 1000000.0 rotary base frequency
sliding_window None local attention span
tie_embeddings False reuse the embedding matrix as the LM head

MixtralGenerate

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

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

MixtralTokenizer

Tokenizer on the tokenizers backend.

MixtralTokenizer(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, a list of strings (a batch), or a chat-message list, which is routed through apply_chat_template automatically. 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.mixtral import MixtralGenerate, MixtralTokenizer

model = MixtralGenerate.from_weights("mixtral-8x7b")
tokenizer = MixtralTokenizer.from_weights("mixtral-8x7b")

inputs = tokenizer([{"role": "user", "content": "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.mixtral import MixtralModel

backbone = MixtralModel.from_weights("mixtral-8x7b")
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 = MixtralGenerate.from_weights("hf:mistralai/Mixtral-8x7B-v0.1")

Lower memory

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

model = MixtralGenerate.from_weights(
    "mixtral-8x7b", quantization="int8", low_memory=True, load_dtype="bfloat16"
)