gabrielgts

ministral3-3b-ec-magento

README

License: apache-2.0

What it does

Trained on 56,253 instruction samples: 70% ECInstruct (generic e-commerce) + 30% synthetic Magento-schema data generated from the Magento Luma sample catalog (products fully disjoint between train and eval). Four task shapes:

Attribute extraction — product text (or a raw Magento custom_attributes payload) → JSON:

markdown
target attribute: size
product title: Puma Suede green sneakers size 43
→ [{"attribute": "size", "value": "43"}]

Absent attributes are reported as "None" rather than hallucinated.

Product QA — a question answered strictly from given product data. Relevance classification — query + product → graded relevance option (ESCI-style A–D). Relevance ranking — query + lettered product list → ranked letters (B,A,C).

Usage — adapter (unsloth / peft)

python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "gabrielgts/ministral3-3b-ec-magento", max_seq_length=2048, load_in_4bit=True)
FastLanguageModel.for_inference(model)

# An EXPLICIT EMPTY system message is REQUIRED: without one, the Ministral 3
# chat template injects Mistral's ~520-token default system prompt, which the
# adapter was not trained with. The multimodal Processor also requires
# tokenizer(text=...), never a positional string.
messages = [
    {"role": "system", "content": ""},
    {"role": "user", "content":
        "Extract the value of the target attribute from the given product information "
        "and output it as JSON. If the attribute is not present, output None as the value.\n\n"
        "target attribute: size\nproduct title: Puma Suede green sneakers size 43"},
]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text=text, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=64, do_sample=False)  # greedy recommended
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
# [{"attribute": "size", "value": "43"}]

Plain peft also works: PeftModel.from_pretrained(base_model, "gabrielgts/ministral3-3b-ec-magento").

Usage — GGUF / Ollama

bash
hf download gabrielgts/ministral3-3b-ec-magento --include 'gguf/*' --local-dir .
cd gguf && ollama create ministral3-3b-ec-magento -f Modelfile
ollama run ministral3-3b-ec-magento "target attribute: color ..."

The bundled Modelfile renders the trained prompt format ([SYSTEM_PROMPT][/SYSTEM_PROMPT][INST]…[/INST]) with temperature 0. If your Ollama version ships a built-in Ministral 3 renderer, pass an explicit empty system prompt per request so Mistral's default one is not injected.

Training recipe

Table with columns: Parameter, Value
Parameter	Value
Method	QLoRA (4-bit NF4 base, bf16 compute) via Unsloth
LoRA	r=8, alpha=16, targets q/k/v/o/gate/up/down_proj (language model only; vision tower frozen)
Trainable params	12.4M of 3,861M (0.32%)
Batch	1 × grad_accum 16 (effective 16), max_seq_length 2048
Optimizer / LR	paged_adamw_8bit, 2e-4 cosine, 1 epoch, seed 42
Data	56,253 samples: 39,377 ECInstruct + 16,876 Magento-synthetic
System prompt	explicit empty system message at train and eval (suppresses the template default)

Evaluation

Greedy decoding, identical prompts and chat template across all models; base model evaluated zero-shot with the same harness. The Qwen column is the same-data, same-recipe sibling qwen3.5-4b-ec-magento (4.55B params vs 3.85B text params here — mind the ~15% size gap when comparing).

Magento held-out set (2,969 samples, 475 products never seen in training):

Table with columns: Task · metric, Base, this model, qwen3.5-4b-ec-magento
Task · metric	Base	this model	qwen3.5-4b-ec-magento
Attribute extraction · F1	0.000	0.945	0.938
Attribute extraction · parse failures	100%	0.1%	0%
Product QA · token-F1	0.039	0.954	0.943
Relevance classification · accuracy	0.132

ECInstruct held-out set (2,000 samples):

Table with columns: Task · metric, Base, this model, qwen3.5-4b-ec-magento
Task · metric	Base	this model	qwen3.5-4b-ec-magento
Attribute extraction · F1	0.000	0.654	0.646
Query→product rank · top-1	0.000	0.632	0.650
Relevance classification · accuracy	0.013	0.655	0.685
Answerability · accuracy	0.507	0.735

Limitations — read before relying on the numbers

The Magento eval is synthetic-on-synthetic. Eval tasks were generated with the same templates as training data (products fully disjoint). It validly measures schema adherence — JSON format, Magento attribute vocabularies, the None-when-absent rule — but overstates production quality on real catalogs and real user queries.
The base model's near-zero extraction scores are dominated by format non-adherence (it answers in prose); they understate its underlying capability, though prose output is itself a blocker for programmatic use.
Fine-tuned on English-only structured data: expect degraded multilingual and general-chat ability versus the base model (drop the adapter to recover it). The vision tower is untouched, but this repo's GGUF is text-only.
Free-form generation is weak (trained r=8, extraction-focused); use it for structured tasks, not copywriting.
Use greedy decoding (do_sample=False / temperature 0) — that's how it was evaluated.

Provenance

Table

Training run	`ministral3-3b-r8-mix56k-e1` (2026-07-09)
Adapter sha256	`c20e11c73705797541191bbda9287f31514f22a4a4c19164709e1695c0d3d9d1`
Train set sha256	`afb7e664cda4490597c1914db6aff94933991a56d2175435666f8f6b7a726532` (mixture_train.jsonl, 56,253 rows)
Eval set sha256	`8feafdb2…` (magento_eval.jsonl) · `2583a61e…` (ecinstruct_eval.jsonl)
GGUF	adapter merged into `mistralai/Ministral-3-3B-Instruct-2512-BF16`, Q4_K_M, sha256

References

Ministral 3: arXiv:2601.08584
ECInstruct / eCeLLM: arXiv:2402.08831
QLoRA: arXiv:2305.14314

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Model Details

Model Provider

gabrielgts

Model Tree

Base

unsloth/Ministral-3-3B-Instruct-2512-bnb-4bit

Adapter

this model

Input Modalities

Text

Output Modalities