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keras/
mixtral_8_instruct_7b_en

Name: mixtral_8_instruct_7b_en
Availability: InStock

keras-hub

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Model Overview

Model Summary

Mistral is a set of large language models published by the Mistral AI team. The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. Both pre-trained and instruction tuned models are available with 7 billion activated parameters.

Weights are released under the Apache 2 License . Keras model code is released under the Apache 2 License.

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

Preset name	Parameters	Description
mixtral_8_7b_en	7B	32-layer Mixtral MoE model with 7 billion active parameters and 8 experts per MoE layer.
mixtral_8_instruct_7b_en	7B	Instruction fine-tuned 32-layer Mixtral MoE model with 7 billion active parameters and 8 experts per MoE layer.

Example Usage

Python

import keras
import keras_hub
import numpy as np

# Basic text generation
mixtral_lm = keras_hub.models.MixtralCausalLM.from_preset("mixtral_8_instruct_7b_en")
mixtral_lm.generate("[INST] What is Keras? [/INST]", max_length=500)

# Generate with batched prompts
mixtral_lm.generate([
    "[INST] What is Keras? [/INST]",
    "[INST] Give me your best brownie recipe. [/INST]"
], max_length=500)

# Using different sampling strategies
mixtral_lm = keras_hub.models.MixtralCausalLM.from_preset("mixtral_8_instruct_7b_en")
# Greedy sampling
mixtral_lm.compile(sampler="greedy")
mixtral_lm.generate("I want to say", max_length=30)

# Beam search
mixtral_lm.compile(
    sampler=keras_hub.samplers.BeamSampler(
        num_beams=2,
        top_k_experts=2,  # MoE-specific: number of experts to use per token
    )
)
mixtral_lm.generate("I want to say", max_length=30)

# Generate without preprocessing
prompt = {
    "token_ids": np.array([[1, 315, 947, 298, 1315, 0, 0, 0, 0, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]] * 2),
}

mixtral_lm = keras_hub.models.MixtralCausalLM.from_preset(
    "mixtral_8_instruct_7b_en",
    preprocessor=None,
    dtype="bfloat16"
)
mixtral_lm.generate(
    prompt,
    num_experts=8,           # Total number of experts per layer
    top_k_experts=2,         # Number of experts to use per token
    router_aux_loss_coef=0.02  # Router auxiliary loss coefficient
)

# Training on a single batch
features = ["The quick brown fox jumped.", "I forgot my homework."]
mixtral_lm = keras_hub.models.MixtralCausalLM.from_preset(
    "mixtral_8_instruct_7b_en", 
    dtype="bfloat16"
)
mixtral_lm.fit(
    x=features, 
    batch_size=2,
    router_aux_loss_coef=0.02  # MoE-specific: router training loss
)

# Training without preprocessing
x = {
    "token_ids": np.array([[1, 315, 947, 298, 1315, 369, 315, 837, 0, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
y = np.array([[315, 947, 298, 1315, 369, 315, 837, 0, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1, 1, 1, 0, 0, 0]] * 2)

mixtral_lm = keras_hub.models.MixtralCausalLM.from_preset(
    "mixtral_8_instruct_7b_en",
    preprocessor=None,
    dtype="bfloat16"
)
mixtral_lm.fit(
    x=x,
    y=y,
    sample_weight=sw,
    batch_size=2,
    router_aux_loss_coef=0.02
)

Example Usage with Hugging Face URI