EYEDOL/adtc-agri-sft-qwen2.5-1.5b

adtc-agri-sft-qwen2.5-1.5b

This model is a fine-tune of `Qwen/Qwen2.5-1.5B-Instruct`, adapted via QLoRA (4-bit base + LoRA adapters, merged back into full weights) for agricultural advisory Q&A aimed at smallholder farmers and extension officers in Nigeria.

Part of the ADTC 2026 project. This is a baseline SFT run — direct supervised fine-tuning with no distillation — used to establish how much headroom a distillation stage could still add over the base model.

Model description

• —Base model: Qwen/Qwen2.5-1.5B-Instruct
• —Fine-tuning method: QLoRA (4-bit NF4 quantized base, bf16 compute dtype, double quantization), adapters merged into the base weights for the published checkpoint
• —LoRA config: r=16, alpha=32, dropout=0.05, target modules: vproj, downproj, kproj, gateproj, upproj, oproj, q_proj
• —Task: single-turn instruction following — given a farming scenario or question, produce a concise assessment, a practical recommendation, and (if relevant) a note on when to escalate to in-person expert help
• —System prompt used at train/inference time:

You are an offline agricultural advisory assistant for smallholder farmers and extension officers in Nigeria. Given the situation described, provide a clear assessment, a practical recommendation, and, where relevant, a note on when to seek in-person expert help. Be concise and avoid unnecessary hedging.

• —W&B run: https://wandb.ai/federal-university-of-technology-minna/adtc-2026-agriculture-sft/runs/gwj5d9qe

Training data

Combined and deduplicated (on normalized question text) from:

• —kaggle_csv: 36,489 examples
• —synthetic_market: 593 examples
• —synthetic_fertilizer: 598 examples

• —Total training examples: 35,796
• —Held-out eval examples: 1,884 (5% split)
• —Primary source: the Kaggle agriculture_qa_final_cleaned.csv dataset (query/response columns), supplemented with synthetically generated fertilizer-dosage and market/storage economics word problems to cover numeric-reasoning cases underrepresented in the base dataset.

Training procedure

Hyperparameters

Training loss

Intended use & limitations

• —Intended for offline agricultural advisory use cases (smallholder farmers, extension officers) in a Nigerian context; not validated for general-purpose assistant use or for regions/crops outside its training distribution.
• —This is a baseline checkpoint for internal comparison against the un-tuned base model and, later, a distilled variant — treat outputs as advisory, not authoritative agronomic or financial guidance, and always recommend in-person expert follow-up for high-stakes decisions.
• —Evaluated informally via qualitative spot-checks on held-out prompts; formal accuracy benchmarking (ARC-Easy / domain-quality via the ADTC profiler) is tracked separately from this card.

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "EYEDOL/adtc-agri-sft-qwen2.5-1.5b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

messages = [
    {"role": "system", "content": "You are an offline agricultural advisory assistant for smallholder farmers and extension officers in Nigeria. Given the situation described, provide a clear assessment, a practical recommendation, and, where relevant, a note on when to seek in-person expert help. Be concise and avoid unnecessary hedging."},
    {"role": "user", "content": "Your farming question here"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Card generated automatically at training time — 2026-09-16 01:25 UTC.