Instructions to use suehuynh/Marketing-Mixtral-8x7B-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suehuynh/Marketing-Mixtral-8x7B-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="suehuynh/Marketing-Mixtral-8x7B-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suehuynh/Marketing-Mixtral-8x7B-v3")
model = AutoModelForCausalLM.from_pretrained("suehuynh/Marketing-Mixtral-8x7B-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use suehuynh/Marketing-Mixtral-8x7B-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suehuynh/Marketing-Mixtral-8x7B-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suehuynh/Marketing-Mixtral-8x7B-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suehuynh/Marketing-Mixtral-8x7B-v3

SGLang

How to use suehuynh/Marketing-Mixtral-8x7B-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "suehuynh/Marketing-Mixtral-8x7B-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suehuynh/Marketing-Mixtral-8x7B-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "suehuynh/Marketing-Mixtral-8x7B-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suehuynh/Marketing-Mixtral-8x7B-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use suehuynh/Marketing-Mixtral-8x7B-v3 with Docker Model Runner:
```
docker model run hf.co/suehuynh/Marketing-Mixtral-8x7B-v3
```

Marketing-Mixtral-8x7B-v3

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

Model Demo

Watch the Model Output Demo to see the generation quality.

Model Details

Model Description

Marketing-Mixtral-8x7B-v3 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.

This is the second iteration of the Marketing-Mixtral series, trained on an expanded 8,000-row dataset (up from the original 4,300 rows used in Marketing-Mixtral-8x7B v1).

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
Related model: Marketing-Llama-3.3-70B (trained on an extended 13K version of this dataset, 88% win rate)
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

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
Despite faithfulness-filtering on product description examples, all generated copy should be human-reviewed before publication
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.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=True,
)
model = PeftModel.from_pretrained(base_model, "suehuynh/Marketing-Mixtral-8x7B-v3")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")

prompt = "Write a product description for an online store listing..."
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

output = model.generate(inputs, max_new_tokens=200, do_sample=False)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Training Details

Training Data

8,000 examples across 5 marketing task types — an expansion of the original 4,300-row dataset with additional sources for table-to-insight, email marketing, and ad copy/brand voice. See suehuynh/marketing-instruct-8k for full documentation.

Training Procedure

Training Hyperparameters

Training regime: bf16 mixed precision
Algorithm: LoRA (Supervised Fine-Tuning)
LoRA rank: 32
LoRA alpha: 64
LoRA dropout: 0.05
Target modules: all-linear
Learning rate: 1.5e-4
LR scheduler: cosine, warmup ratio 0.05
Epochs: 1
Weight decay: 0.01
Gradient clipping: 1.0
Train on inputs: false

Note on epochs: an earlier run on this identical dataset and recipe with n_epochs=3 achieved an 83% win rate. Changing only n_epochs to 1 (holding every other hyperparameter constant) improved the win rate to 87% — a controlled, single-variable result indicating that additional training epochs on this dataset size leads to overfitting and reduced generalization against the base model judge.

Speeds, Sizes, Times

Training platform: AutoScientist by Adaption Labs
Base model size: 46.7B parameters (Mixtral MoE, ~13B active per token)

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluated on AutoScientist's held-out internal marketing test set (not publicly available).

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)	13%	87%	+569% relative

Summary

This model achieves an 87% win rate against base Mixtral-8x7B-Instruct on AutoScientist's held-out marketing test set, up from 80% in v1 (trained on 4,300 rows, 1 epoch).

The most notable finding from this iteration is a controlled, single-variable experiment: on the same 8,000-row dataset with an identical recipe, training for 3 epochs produced 83% win rate, while training for 1 epoch (with no other change) produced 87%. This isolates epoch count as a direct driver of overfitting at this dataset size — more passes over the data degrade generalization against the base model judge rather than improving it.

Environmental Impact

Hardware: AutoScientist cloud infrastructure
Cloud Provider: Adaption Labs

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

Citation

BibTeX:

@misc{huynh2026marketingmixtralv3,
  author = {Huynh, Nguyen},
  title = {Marketing-Mixtral-8x7B-v3: A Fine-tuned Model for Marketing Copy Generation},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/suehuynh/Marketing-Mixtral-8x7B-v3}},
  note = {AutoScientist Challenge 2026 — Marketing Category}
}

Model Card Authors

Sue Huynh — Brown University MSc Data Science

Model Card Contact

HuggingFace: suehuynh

Downloads last month: 96

Model tree for suehuynh/Marketing-Mixtral-8x7B-v3

Base model

mistralai/Mixtral-8x7B-v0.1

Finetuned

mistralai/Mixtral-8x7B-Instruct-v0.1

Adapter

(126)

this model

suehuynh
/

Marketing-Mixtral-8x7B-v3