market-analysis-mixtral-8x7b

Market-Analysis Mixtral-8x7B — Financial News Q&A

A LoRA fine-tune of Mixtral-8x7B-Instruct-v0.1 adapted for market analysis and financial news interpretation: classifying news by market area, reading sentiment, identifying who a story matters to, and answering forward-looking questions from earnings calls.

Built with Adaption Labs' AutoScientist for the AutoScientist Challenge (Market-Analysis & News category).

Results

Head-to-head win rate against the base model:

Both numbers are wins in a paired comparison, not accuracy percentages. The two figures differ because the category evaluation spans a wider range of tasks than the training data covers — the model is strongest on the task types it was trained on and holds a smaller edge across the category as a whole.

Worth stating plainly: an earlier checkpoint trained on shorter, more templated answers scored 64–36 on the category evaluation while scoring only 45–55 on its own training distribution. Concise answers generalised better; richer answers won more decisively in-distribution. This release is the latter.

Usage

Extract the adapter — the archive unpacks flat, so give it its own directory:

mkdir -p market-adapter
tar --zstd -xf adaption_mixtral_8x7b_instruc_financial_news_classifie_45d894da.tgz -C market-adapter

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

BASE = "mistralai/Mixtral-8x7B-Instruct-v0.1"
ADAPTER = "./market-adapter"

tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
model = AutoModelForCausalLM.from_pretrained(
    BASE, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

messages = [{
    "role": "user",
    "content": "What area of the market does this headline concern, and why does "
               "it matter? Citi cuts Microsoft price target, citing foreign "
               "exchange headwinds",
}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

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

Hardware: Mixtral-8x7B is a 46.7B-parameter mixture-of-experts model — roughly 94 GB in bf16, or about 25 GB with 4-bit quantisation. The adapter itself is 54 MB.

Task types it was trained on: topic classification, sentiment assessment, market implication, subject identification, who-it-matters-to, and earnings-call analysis.

Training

• Base: mistralai/Mixtral-8x7B-Instruct-v0.1
• Method: LoRA supervised fine-tuning via AutoScientist (recipe auto-selected)
• Data: 5,929 adapted rows from flamiinngo/market-news-qa

Training curve

Training loss fell from 1.414 to 0.876. Evaluation loss was still declining at the end of epoch 3, though the last epoch gained only 0.001 — close to converged, with no sign of overfitting.

Dataset

flamiinngo/market-news-qa — 10,011 market-analysis Q&A pairs across five task types, median answer 16 words, 85% of answers distinct. Derived from zeroshot/twitter-financial-news-topic (MIT), zeroshot/twitter-financial-news-sentiment (MIT), and lamini/earnings-calls-qa (CC-BY-4.0).

Also on Kaggle: dataset · adapted training data

Limitations

• Not investment advice. This model classifies and comments on financial news. It does not predict prices and must not be used to make investment decisions.
• It can state figures confidently and be wrong. The training data includes specific financial numbers. For any company or period outside that data, treat numeric claims as unverified.
• Training headlines skew US markets, 2020–2022. Coverage of other regions and later periods is thinner.
• Category labels carry upstream annotation noise, so some classifications reflect debatable source labels.
• Reasoning clauses in the training data are category-level, not per-headline human judgment. The model learned correct categories with generally apt explanations, not independent analysis of each story.
• Evaluated by automated head-to-head comparison against one base model. Win rate is not a measure of factual accuracy in absolute terms.
• English only.

License

The adapter is released under Apache 2.0, matching the base model's licence. Note that the training data is CC-BY-4.0 and requires attribution — see the dataset card.

Acknowledgements

• Adaption Labs — AutoScientist platform and the AutoScientist Challenge
• zeroshot and Lamini — upstream open datasets
• Mistral AI — Mixtral-8x7B base model