Base model: mistralai/Mistral-7B-Instruct-v0.2
Fine-tuning method: LoRA (r=16, alpha=32) via AutoScientist co-optimization
Dataset: Papajams/orbura-dataviz-augmented (5,000 rows with reasoning traces)
Task: Data visualization — matplotlib code generation, chart QA, code-to-description, style transfer, bug repair, chart-type selection
Competition: AutoScientist Challenge Part 2 — Data Visualization

Training

Train examples: 5,000 (augmented with reasoning traces via Adaption Adaptive Data)
Validation examples: 2,000 (deterministic, held-out)
Iterations: 3 (AutoScientist co-optimization loop)
Best win rate: 53.77%
LoRA rank: 16, alpha: 32
Learning rate: 1e-5, cosine schedule
Epochs: 1
Training type: LoRA
Batch size: max (platform-selected)
Compute: Free via Adaption voucher

Evaluation

Naive baseline: 17.6% on 2k validation set.

Run the eval harness:

bash

cd showcases/autoscientist-dataviz
python3 eval_finetuned.py --baseline-only

Scoring metrics (per-task deterministic evaluation):

Task	Scoring method
`chart_qa`	Exact or numeric-tolerance match
`chart_to_code` / `data_to_code`	Chart function + data value overlap (≥80%)
`code_to_desc`	Chart type + ≥2 numeric mentions
`style_transfer`	Normalized code equivalence
`fix_code`	Normalized code equivalence
`chart_choice`	First-word (chart type) match

Usage

This is a LoRA adapter. Load it with PEFT:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = PeftModel.from_pretrained(base, "Papajams/orbura-dataviz-mistral-7b-autoscientist")
tokenizer = AutoTokenizer.from_pretrained("Papajams/orbura-dataviz-mistral-7b-autoscientist")

messages = [{"role": "user", "content": "Answer the question using the chart data.\n\n- A: 45\n- B: 78"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

Trained on synthetic chart data with 4 chart types (line, bar, scatter, pie). Performance on novel chart types (boxplot, heatmap, violin) is not validated.
The text-only dataset does not test visual chart reading. The multimodal pilot (100 rows) begins to address this but is too small for full training.
Always validate generated matplotlib code before execution.

Reproducibility

bash

# 1. Generate the dataset
cd showcases/autoscientist-dataviz
python3 generate_full.py
python3 prepare_adaption.py

# 2. Run Adaption augmentation
python3 run_adaption.py --input data/adaption_train_5k.jsonl

# 3. Train via AutoScientist (adaptionlabs.ai/auto-scientist)
#    Upload the augmented dataset, select Mistral-7B, run co-optimization

# 4. Evaluate
python3 eval_finetuned.py --baseline-only

# 5. Publish
python3 publish_hf.py --model path/to/model

License

Apache 2.0 (base model license applies). Dataset and model generated for the AutoScientist Challenge by Adaption Labs.