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ghananlpcommunity
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Model Details
Model Provider
ghananlpcommunity
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
This is a PEFT LoRA adapter (rank=32, alpha=64) trained on ~495k Twi instruction-response pairs. It takes an instruction (and optional English source text) and generates a response entirely in Twi.
Built as part of the TwiGen project. A MiniCPM4-8B version is also available at twi-gen-minicpm4.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen3.5-0.8B"adapter = "ghananlpcommunity/twi-gen-qwen" tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained( base, torch_dtype=torch.bfloat16, device_map="cuda:0",)model = PeftModel.from_pretrained(model, adapter)model.eval() instruction = "Write a short story about a farmer and his harvest"messages = [ {"role": "user", "content": instruction + "\n\nRespond entirely in Twi. Your response should not exceed 250 words."},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048).to(model.device) outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, do_sample=True)response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)print(response)| Property | Value |
|---|---|
| Base model | Qwen3.5-0.8B |
| Adapter size | ~43 MB |
| LoRA rank | 32 |
| LoRA alpha | 64 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Training precision | bfloat16 |
| VRAM (inference) | ~1.8 GB (with base model) |
Apache 2.0
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen3.5-0.8B"adapter = "ghananlpcommunity/twi-gen-qwen" tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained( base, torch_dtype=torch.bfloat16, device_map="cuda:0",)model = PeftModel.from_pretrained(model, adapter)model.eval() instruction = "Write a short story about a farmer and his harvest"messages = [ {"role": "user", "content": instruction + "\n\nRespond entirely in Twi. Your response should not exceed 250 words."},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048).to(model.device) outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, do_sample=True)response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)print(response)