suehuynh/Marketing-Mixtral-8x7B-v2

Marketing-Mixtral-8x7B

Fine-tuned Mixtral-8x7B-Instruct-v0.1 on a curated marketing instruction dataset using AutoScientist for the AutoScientist Challenge 2026 — Marketing Category.

Model Details

Model Description

Marketing-Mixtral-8x7B-v2 is a LoRA fine-tuned version of Mixtral-8x7B-Instruct-v0.1, adapted for marketing copy generation across five task types: ad/social copy, email marketing, product descriptions, brand voice rewriting, and campaign data insights.

Key contribution: First open marketing LLM optimized for attribute faithfulness — product description outputs are grounded strictly in provided specifications, reducing hallucinated features and invented claims.

• Developed by: Sue Huynh
• Model type: Causal LM, LoRA fine-tuned
• Language: English
• License: Apache 2.0
• Finetuned from: mistralai/Mixtral-8x7B-Instruct-v0.1
• Training platform: AutoScientist by Adaption Labs

Model Sources

• Dataset: suehuynh/marketing-instruct-8k
• Demo: suehuynh/marketing-mixtral-demo (HuggingFace Spaces)
• Challenge: AutoScientist Challenge 2026 — adaptionlabs.ai

Uses

Direct Use

Generate marketing copy from structured briefs across five task types:

• Ad/Social copy: platform-specific ads with audience and CTA constraints
• Email marketing: campaign, lifecycle, and transactional emails with subject + preview + body format
• Product descriptions: grounded in provided attribute lists only
• Brand voice rewriting: neutral copy rewritten to match a specified voice
• Table-to-insight: campaign performance data summarized with recommendations

Downstream Use

Can be fine-tuned further on domain-specific marketing data (e-commerce, B2B SaaS, local business) for specialized copy generation pipelines.

Out-of-Scope Use

• Not intended for strategy generation, market research, or competitive analysis
• Not suitable for medical, legal, or financial copywriting without additional fine-tuning
• Product descriptions should always be reviewed for factual accuracy before publication

Bias, Risks, and Limitations

• Training data is primarily English-language and Western marketing contexts; performance may degrade on non-English or culturally specific briefs
• Despite faithfulness training, product descriptions should be human-reviewed before publication
• The model may reflect biases present in marketing copy (demographic targeting assumptions, persuasion patterns)
• Generated copy should not be published without editorial review

Recommendations

Always review generated copy before publication. For product descriptions, verify all claims against the original product specification. Do not use generated copy as a substitute for human creative judgment.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "suehuynh/Marketing-Mixtral-8x7B-v2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=True,
)

prompt = """Write a product description for an online store listing.
Use ONLY the information provided below — do not invent features,
specifications, or claims.

Product: Wireless Headphones Model-X

Attributes:
- type: over-ear, ANC
- battery: up to 30 hrs (ANC on)
- charging: USB-C, 10 min = 3 hrs
- weight: 250g
- folds_flat: yes, case included

Target length: 100 words. Tone: practical, benefit-led."""

inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    output = model.generate(
        inputs,
        max_new_tokens=200,
        do_sample=False,
        pad_token_id=tokenizer.pad_token_id,
    )

new_tokens = output[0][inputs.shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Training Details

Training Data

8,000 examples across 5 marketing task types, assembled from:

• AdaptData-adapted public marketing datasets (ad copy, email, product descriptions)
• High-quality synthetic examples (brand voice, table insights, email)
• Domain augmentation and general-purpose diversity data via AutoScientist

See full data documentation: suehuynh/marketing-instruct-8k

Training Procedure

Preprocessing

• Unified instruction schema: {instruction, input, output}
• Faithfulness filtering on product descriptions: outputs verified to contain only claims grounded in provided attributes
• Deduplication on output hash
• Balanced sampling across task types before final merge

Training Hyperparameters

• Training regime: bf16 mixed precision
• Algorithm: LoRA (Supervised Fine-Tuning)
• LoRA rank: 32
• LoRA alpha: 64
• LoRA dropout: 0
• Target modules: all-linear
• Learning rate: 1e-4
• LR scheduler: cosine with warmup ratio 0.1
• Epochs: 1
• Batch size: max
• Gradient clipping: 1
• Train on inputs: false

Speeds, Sizes, Times

• Training platform: AutoScientist by Adaption Labs
• Base model size: 46.7B parameters (Mixtral MoE, ~13B active per token)
• Training duration: approximately 1 hour on AutoScientist infrastructure

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluated on AutoScientist's held-out internal marketing test set (not publicly available) covering diverse marketing copy generation tasks.

Metrics

Win rate: pairwise LLM-as-judge comparison between finetuned model output and base Mixtral-8x7B-Instruct output on identical prompts.

Results

Metric	Base Mixtral	Finetuned	Delta
Win rate (AS eval)	17%	83%	+388% relative

Summary

The finetuned model achieves a 83% win rate against base Mixtral-8x7B-Instruct on AutoScientist's held-out marketing test set, demonstrating meaningful improvement in marketing copy quality across five task types.

Environmental Impact

• Hardware: AutoScientist cloud infrastructure
• Cloud Provider: Adaption Labs
• Training duration: ~1 hour

Technical Specifications

Model Architecture and Objective

• Base: Mixtral-8x7B-Instruct-v0.1 (Mixture of Experts, 8 experts, 2 active per token)
• Adaptation: LoRA adapters on all linear layers
• Objective: Supervised fine-tuning on marketing instruction data
• Effective trainable parameters: ~2% of base model parameters

Compute Infrastructure

• Platform: AutoScientist by Adaption Labs
• Method: LoRA SFT with automatic recipe optimization

Citation

If you use this model, please cite:

BibTeX:

@misc{huynh2026marketingmixtral,
  author = {Huynh, Nguyen},
  title = {Marketing-Mixtral-8x7B: A Fine-tuned Model for Marketing Copy Generation},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/suehuynh/Marketing-Mixtral-8x7B-v2}},
  note = {AutoScientist Challenge 2026 — Marketing Category}
}

Model Card Authors

Sue Huynh — Brown University MSc Data Science

Model Card Contact

HuggingFace: suehuynh