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Suggested model ID:FatihJimale/gpt2-medium-somali
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4model_id = "FatihJimale/gpt2-medium-somali"
5tok = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
7
8prompt = "qarax xoogan ayaa ka dhacay magaalada"
9inputs = tok(prompt, return_tensors="pt").to(model.device)
10outputs = model.generate(
11 **inputs,
12 max_new_tokens=80,
13 do_sample=True,
14 temperature=0.9,
15 top_p=0.92,
16 repetition_penalty=1.08,
17)
18print(tok.decode(outputs[0], skip_special_tokens=True))repetition_penalty to 1.1–1.2 or lower temperature (0.7–0.9).max_new_tokens and set top_p around 0.9.do_sample=False, tune top_k=None, temperature=1.0.ml.g5.24xlarge — 4× NVIDIA A10 (24 GB each), 96 vCPU, 384 GiB RAM; data-parallel across 4 GPUsNote: Dataset specifics and cleaning steps are intentionally not disclosed here, per the author's request. This card focuses on model size, parameters, and usage.
config.json
pytorch_model.bin (or model.safetensors)
merges.txt
vocab.json
tokenizer.json
tokenizer_config.json
special_tokens_map.json (if any)
README.md (this file)1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4model_id = "FatihJimale/gpt2-medium-somali"
5tok = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
7
8prompt = "qarax xoogan ayaa ka dhacay magaalada"
9inputs = tok(prompt, return_tensors="pt").to(model.device)
10outputs = model.generate(**inputs, max_new_tokens=80, do_sample=True, temperature=0.9, top_p=0.92, repetition_penalty=1.08)
11print(tok.decode(outputs[0], skip_special_tokens=True))1@software{gpt2_medium_somali_2025,
2 title = {GPT-2 Medium Somali},
3 author = {Mohamed Abdirizak Ahmed},
4 year = {2025},
5 url = {https://huggingface.co/FatihJimale/gpt2-medium-somali}
6}