AgroVeritas-Scout-17B

AgroVeritas-Scout-17B

Agricultural intelligence you can audit.

AgroVeritas-Scout-17B is a bilingual English–Spanish agricultural reasoning adapter for Llama 4 Scout, trained with Adaption AutoScientist. It is designed to answer regional crop-calendar and historical climate questions while making the evidence, reasoning, limitations, and need for local confirmation explicit.

The result that matters

On Adaption's held-out Agriculture category tasks, the fine-tuned model achieved a 78% win rate versus 23% for its frozen base model.

That is a +55 percentage-point improvement, a +239% relative lift, and 3.39× the baseline win rate under the platform's head-to-head evaluation.

The training dataset also reached A / 10.0 out of 10 on Adaption quality evaluation. The final same-run data score improved from 9.0 to 10.0 (+11.1% relative); the project as a whole progressed from an early C / 5.0 prototype to the final A / 10.0 release.

The exact exported training artifact contains 28,220 rows: 17,094 agriculture-core examples and 11,126 AutoScientist expansion examples, a 39.4% general-purpose diversity buffer.

Why AgroVeritas exists

Agricultural answers fail when models blur three different things: a published crop calendar, historical climate, and current field reality. AgroVeritas teaches a strict evidence contract:

1. Give the answer that is supported by the record.
2. Name the evidence used.
3. Show the reasoning link.
4. State what the evidence cannot establish.
5. Direct the user to current local confirmation before operational decisions.

This turns a generic assistant into an auditable agricultural evidence layer rather than an unqualified recommendation engine.

Model details

• AutoScientist model ID: adaption_llama_4_scout_17b_16_agri_evidence_qa_158a7fd8
• Architecture: Llama 4 Scout 17B active / 16 experts, 109B total parameters.
• Exported base reference: togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit
• Adaptation: PEFT LoRA supervised fine-tuning.
• Adapter rank / alpha: 64 / 128.
• Languages: English and Latin American Spanish.
• Domain: Agriculture.
• Framework: PEFT 0.15.1.
• License: Llama 4 Community License. Base-model access and acceptable-use terms apply.

Training recipe

LoRA was injected into q_proj, k_proj, v_proj, o_proj, and the shared-expert and feed-forward gate/up/down projections. Training completed 159 global steps across 3 epochs. Exported trainer metrics show validation loss moving from 0.7566 at the first recorded evaluation to 0.6824 at completion.

Evaluation notes

• The 78% value is the Adaption Agriculture category win rate shown for the selected 158a7fd8 run.
• It is a held-out platform evaluation; organizer prompts are not disclosed.
• The weaker retry ending in 2329bca3 is not this release.
• The C→A claim describes successive project dataset versions; it is not substituted for the final same-run A→A evaluation.
• No private AgroVeritas holdout or private evaluation fact group entered adaptation or training.

Intended use

• Evidence-grounded crop-calendar questions.
• Regional and historical climate explanation.
• Bilingual agricultural assistants and research prototypes.
• Workflows that need visible provenance, limitations, and calibrated uncertainty.

Out-of-scope use and safety

Do not treat model output as live weather, field scouting, diagnosis, pesticide instructions, financial advice, or a guarantee of yield. Crop calendars and climatology can be outdated or locally incomplete. Verify material decisions with current authoritative forecasts, label instructions, local regulations, agronomists, and extension services.

Load the adapter

The repository contains a PEFT adapter, not a standalone copy of the Llama 4 base model. You must separately obtain access to a compatible Llama 4 Scout checkpoint and comply with its license.

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit"
adapter_id = "MarianaCodebase/AgroVeritas-Scout-17B"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, adapter_id)

messages = [{
    "role": "user",
    "content": "Using cited crop-calendar and historical climate evidence, explain the planting window for maize in my region. State limitations and what I should verify locally."
}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)
output = model.generate(inputs, max_new_tokens=600, do_sample=False)
print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))

Open release

The exact adapted dataset and this adapter are released publicly on both Hugging Face and Kaggle. The public AgroVeritas Evidence Assistant demonstrates bilingual evidence-bounded responses, and the submission film shows the problem, method, and verified platform results.

Citation

@software{agroveritas_scout_2026,
  title        = {AgroVeritas-Scout-17B: Evidence-Bounded Bilingual Agriculture},
  author       = {Sinisterra, Mariana},
  year         = {2026},
  note         = {Fine-tuned with AutoScientist by Adaption}
}

Built with Adaptive Data and AutoScientist by Adaption.