Instructions to use FarmifAI/FarmifAI_1.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FarmifAI/FarmifAI_1.3 with Transformers:

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

pipe = pipeline("text-generation", model="FarmifAI/FarmifAI_1.3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FarmifAI/FarmifAI_1.3")
model = AutoModelForMultimodalLM.from_pretrained("FarmifAI/FarmifAI_1.3", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FarmifAI/FarmifAI_1.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FarmifAI/FarmifAI_1.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FarmifAI/FarmifAI_1.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FarmifAI/FarmifAI_1.3

SGLang

How to use FarmifAI/FarmifAI_1.3 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 "FarmifAI/FarmifAI_1.3" \
    --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": "FarmifAI/FarmifAI_1.3",
		"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 "FarmifAI/FarmifAI_1.3" \
        --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": "FarmifAI/FarmifAI_1.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use FarmifAI/FarmifAI_1.3 with Docker Model Runner:
```
docker model run hf.co/FarmifAI/FarmifAI_1.3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FarmifAI 1.3

FarmifAI 1.3 is a small language model (fine-tuned from Qwen3.5-0.8B) that answers agricultural questions in Spanish from a context you provide. It is built for Colombian agriculture and powers FarmifAI, an offline assistant app for farmers: given a question and technical passages retrieved from a knowledge base, it writes a short reasoning and a clear final answer based only on that context.

This repository contains the full-precision weights. For llama.cpp and on-device use, see FarmifAI_1.3_GGUF.

FarmifAI is not a general-purpose chatbot. It is trained to answer from the context it receives, so it should always be used together with a retrieval step.

Model details


Developed by	FarmifAI, Universidad del Cauca (Colombia)
Base model	Qwen3.5-0.8B
Language	Spanish
Input	System prompt + retrieved context in `<knowledge>` tags + question
Output	`<reasoning>…</reasoning>` followed by `<answer>…</answer>`
License	Apache 2.0

Intended use

Answering farmers' questions in Spanish inside a RAG pipeline, using passages from agricultural technical documents.
Not meant to be used without retrieved context, in other languages, or as the only basis for decisions such as agrochemical selection or dosing.

Prompt format

The model was trained with a fixed Spanish system prompt. Use it as is:

Eres un asistente agrícola. Responde únicamente con la información dentro de <knowledge>. Si la respuesta no está en el contexto, declara que no tienes información; no inventes datos.

Instrucciones de formato:
- En <reasoning>, analiza paso a paso el contexto frente a la ...

<<< PASTE THE REST OF THE EXACT SYSTEM PROMPT FROM THE DATASET HERE >>>

The user message contains the retrieved context followed by the question:

<knowledge>
{retrieved context}
</knowledge>

{question}

The model replies with a step-by-step analysis and a final answer:

<reasoning>
Step-by-step analysis of the context against the question.
</reasoning>
<answer>
Final answer in Spanish, based only on the provided context.
</answer>

Applications typically show only the <answer> block. Parse it defensively in case the tags are missing.

Quickstart

import re
from transformers import AutoProcessor, AutoModelForMultimodalLM

model_id = "FarmifAI/FarmifAI_1.3"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(model_id, device_map="auto", dtype="auto")

SYSTEM_PROMPT = "..."  # the system prompt from "Prompt format" above

context = "..."  # passages retrieved from your knowledge base
question = "¿Cómo puedo controlar la broca en mi cultivo de café?"
user_message = f"<knowledge>\n{context}\n</knowledge>\n\n{question}"

messages = [
    {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
    {"role": "user", "content": [{"type": "text", "text": user_message}]},
]

inputs = processor.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=512, do_sample=True, temperature=0.1, top_p=0.9)
text = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)

match = re.search(r"<answer>(.*?)</answer>", text, re.DOTALL)
print(match.group(1).strip() if match else text)

Recommended settings: temperature=0.1, top_p=0.9, max_new_tokens=512.

Training

Fine-tuned with LoRA (Unsloth + TRL) on FarmifAI_dataset_1.3, about 8.8k synthetic Spanish conversations built from technical documents on Colombian agriculture. Each example pairs a context passage and a question with a reasoning + answer response.

Results

Compared with the base Qwen3.5-0.8B on 250 held-out examples, using the same prompt and settings for both models:

Metric	Base model	FarmifAI 1.3
Format adherence ↑	24.4%	98.0%
Answer relevancy (LLM judge, 0–5) ↑	2.16	4.70
Faithfulness to context (LLM judge, 0–5) ↑	2.13	3.48
Contradictions with context (NLI) ↓	30.4%	17.2%

Limitations

The model can still make mistakes or add details that are not in the context. Check its recommendations, especially anything about agrochemicals, doses or safety periods.
Answer quality depends on the quality of the retrieved context.
It was evaluated with automatic metrics and LLM judges, not by agronomists or in the field, and it is not a substitute for professional advice. Its training material may reflect outdated practices, so do not use it for current regulations or registered products.

Model tree for FarmifAI/FarmifAI_1.3

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

unsloth/Qwen3.5-0.8B

Adapter

(43)

this model

Quantizations

1 model

FarmifAI
/

FarmifAI_1.3