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TwiGen MiniCPM4

LoRA adapter for MiniCPM4-8B fine-tuned on Twi (Akan) instruction data. Part of the TwiGen project.

Generates coherent Twi text from English instructions.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "openbmb/MiniCPM4-8B"
lora_id = "ghananlpcommunity/twi-gen-minicpm4"

tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    base_id, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda:0",
)
model = PeftModel.from_pretrained(model, lora_id)
model.eval()

messages = [{"role": "user", "content": "Write a story about a farmer in Ghana."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False) + "<|im_start|>assistant\n"

inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Training

Base model: MiniCPM4-8B
Data: ~495k Twi instruction samples (translated + synthetic)
Method: LoRA (rank 16, alpha 32)
Hardware: NVIDIA H200 (140 GB VRAM)
Framework: LLaMA-Factory

Model

Model	Adapter
twi-gen-qwen	Qwen3.5-0.8B version (lighter, faster)

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