Shinzmann/naija-reviewer-8b-v2

NaijaReviewer-8B

Llama 3.1 8B Instruct QLoRA fine-tuned on Nigerian product reviews. This repository contains both the merged HF-format weights (for transformers / Unsloth) and all GGUF quantisations (for llama.cpp / Ollama). It is the model behind the Naija Persona Agent — a Nigerian-context AI system for review simulation (Task A) and persona-aware recommendation (Task B), submitted to the DSN X BCT LLM Agent Challenge.

• Live application: https://switteefranca2-0--naijapersona-web.modal.run/
• Source code: https://github.com/Mystique1337/telcoproject
• Companion artifacts: GGUF-only repo · LoRA adapter · Training corpus

Headline numbers

Metric	NaijaReviewer-8B	Frontier baseline (Claude Sonnet 4)
Task A rating RMSE (lower is better)	1.114	1.319 (15.5% higher)
Task A BERTScore F1	0.858	0.857
Task A Nigerian-rater win-rate, 5 raters / 50 pairs	48.5% (CI [40.2, 56.9])	51.5% (statistical parity)
Task B NDCG@10 vs 4 baselines	0.588 (best in field)	0.430
Parameters	8B	70B-120B+
Per-call API cost	Zero (open weights)	$/1k tokens

What's in this repo

File set	Use
model.safetensors, config.json, tokenizer files	HF format. Load with transformers / Unsloth / vLLM / TGI.
naija-reviewer-8b-v2-Q4_K_M.gguf	Recommended GGUF (~5 GB). Load with llama.cpp / Ollama.
naija-reviewer-8b-v2-Q5_K_M.gguf	Higher-quality GGUF (~5.7 GB).
naija-reviewer-8b-v2-Q8_0.gguf	Near-lossless GGUF (~8.5 GB).
Modelfile	Ollama configuration with the correct Alpaca template and stop sequences.

The Q4_K_M GGUF is also available standalone in its dedicated repo, which is the file the production serverless endpoint pulls.

Quick start

transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("Shinzmann/naija-reviewer-8b-v2")
model = AutoModelForCausalLM.from_pretrained(
    "Shinzmann/naija-reviewer-8b-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = (
    "### Instruction\n"
    "Write a star rating and a short product review.\n\n"
    "### Input\n"
    "Persona: Tunde, 28, Lagos Bolt driver, code-mixed speaker, value-priority.\n"
    "Product: Tecno Spark 10 mobile phone, 4 GB RAM, 5000 mAh battery, NGN 145,000.\n\n"
    "### Response\n"
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=220, eos_token_id=tok.eos_token_id, do_sample=True, temperature=0.7)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

llama-cpp-python (GGUF)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Shinzmann/naija-reviewer-8b-v2",
    filename="naija-reviewer-8b-v2-Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,
    chat_format="llama-3",
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Write a Pidgin review of an Oraimo wireless earbud."}],
    max_tokens=256, temperature=0.7,
)
print(out["choices"][0]["message"]["content"])

Ollama

huggingface-cli download Shinzmann/naija-reviewer-8b-v2 \
  naija-reviewer-8b-v2-Q4_K_M.gguf Modelfile --local-dir .
ollama create naija-reviewer-8b -f Modelfile
ollama run naija-reviewer-8b

Prompt format

Alpaca template, as used in training:

### Instruction
{instruction}

### Input
{input}

### Response
{response}

Stop sequences: ### Instruction, ### Input, ### Response. The included Ollama Modelfile encodes these.

For Nigerian-context use, pass the structured persona JSON (cognitive dimensions + register tier + aspect priorities) as the ### Input. The production prompt templates are in the project repo at app/prompts/.

Training

Base model	meta-llama/Meta-Llama-3.1-8B-Instruct
Method	QLoRA via Unsloth
Adapter	LoRA r=16, alpha=32, dropout=0.1, targets q/k/v/o/up/gate/down (0.52% trainable params)
Loss	Response-only loss (gradients on the answer tokens only)
Tokenisation	EOS-terminated training examples
Schedule	2 epochs, effective batch size 16, learning rate 2e-4 with cosine decay, sequence length 4096
Quantisation	Three GGUF builds: Q4_K_M, Q5_K_M, Q8_0

Training and validation loss curves are published in the project's notebooks/03_training_results.ipynb and reproduced in the Task A solution paper.

Training data

Trained on Shinzmann/npa-corpus-v1 (~20,000 Alpaca-style instruction/response pairs), built from two real public sources plus synthetic expansion:

• Real Jumia reviews (~76k) from the aymane-maghouti/Sentiment-Analysis-for-Jumia-Reviews public GitHub repository, used as Pipeline 1 seed.
• Real Jumia product catalogue (~18k) from the Idowenst/jumia_dataset HuggingFace dataset, used as Pipeline 2 seed.
• Synthetic expansion by NVIDIA Nemotron and OpenAI models, persona-conditioned and stratified 90/5/5 by register tier.

Intended use and limitations

Intended use. Generation of Nigerian-context product reviews and ratings (Task A); persona-aware re-ranking of product recommendations (Task B); research on register-aware text generation in low-resource African contexts.

Limitations. Trained primarily on Nigerian English and Nigerian Pidgin product reviews. The training corpus is partly synthetic; two independent generator pipelines and a held-out evaluation generated by a different model family were used to mitigate confounds, but residual bias remains possible. On a 3-arbiter LLM-as-Judge evaluation, frontier LLM judges showed a systematic preference for Claude Sonnet 4's prose register; Nigerian human raters scored the two systems at parity on the same pairs, which we read as evidence that single-judge LLM evaluation is insufficient on culturally-localised content.

Citation

@misc{naijareviewer8b2026,
  title  = {NaijaReviewer-8B: A Nigerian-Context Open-Weight Fine-Tune for Persona-Aware Review Generation},
  author = {Ashinze, Emmanuel and Uvere, Franca and Oyenekan, Esther},
  year   = {2026},
  url    = {https://huggingface.co/Shinzmann/naija-reviewer-8b-v2}
}

License

Subject to the Llama 3.1 Community License. Released for research and non-commercial use; commercial use must comply with the upstream Llama 3.1 terms.