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akhaliq
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Model Details
Model Provider
akhaliq
Model Tree
Qwen/Qwen3-4B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
Before: It is worth noting that regular exercise offers a multitude of benefits for both physical and mental well-being. Furthermore, engaging in consistent physical activity has been shown to improve cardiovascular health, enhance mood through the release of endorphins, and contribute to better sleep quality. Additionally, it is important to recognize that even moderate exercise can yield significant positive outcomes over time.
After: Regular exercise offers a multitude of benefits for both physical and mental well-being. Engaging in consistent physical activity has been shown to improve cardiovascular health, enhance mood through the release of endorphins, and contribute to better sleep quality. It is important to recognize that even moderate exercise can yield significant positive outcomes over time.
Before: The implementation of renewable energy sources plays a pivotal role in the mitigation of climate change. Furthermore, solar and wind power have emerged as viable alternatives to fossil fuels, offering sustainable solutions for future energy needs. It is imperative that governments and corporations prioritize investment in these technologies to ensure a greener tomorrow.
After: The use of renewable energy sources is crucial in combating climate change. Solar and wind power are alternatives to fossil fuels that can meet future energy demands. Governments and corporations must invest in these technologies to ensure a greener future.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("akhaliq/open-humanizer-4b", subfolder="merged")model = AutoModelForCausalLM.from_pretrained( "akhaliq/open-humanizer-4b", subfolder="merged", dtype=torch.bfloat16, device_map="auto") SYSTEM = ("You are an expert editor. Rewrite the user's text so it reads like natural " "human writing. Keep every fact, name, date and number exactly as given. " "Remove AI-sounding habits: hedging, filler connectives, over-formal phrasing, " "and uniform sentence rhythm. Do not add information. Return only the rewritten text.") msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "YOUR AI-WRITTEN TEXT"}]enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)out = model.generate(**enc, max_new_tokens=1024, do_sample=False)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))| File | Size | Use |
|---|---|---|
gguf/open-humanizer-4b-q4_k_m.gguf | 2.5 GB | best size/quality tradeoff |
gguf/open-humanizer-4b-q5_k_m.gguf | 2.9 GB | slightly better quality |
gguf/open-humanizer-4b-q8_0.gguf | 4.3 GB | near-lossless |
gguf/open-humanizer-4b-f16.gguf | 8.1 GB | reference / re-quantizing |
bash
llama-cli -m open-humanizer-4b-q4_k_m.gguf --conversationSFTTrainer)rewrite_sft/redistributable — AI drafts → human originals (CRS reports, eLife peer reviews, PeerRead, peS2o; per-row open licenses)merged/: standalone merged bf16 model (use this)gguf/: f16 / Q8_0 / Q5_K_M / Q4_K_M quantsimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("akhaliq/open-humanizer-4b", subfolder="merged")model = AutoModelForCausalLM.from_pretrained( "akhaliq/open-humanizer-4b", subfolder="merged", dtype=torch.bfloat16, device_map="auto") SYSTEM = ("You are an expert editor. Rewrite the user's text so it reads like natural " "human writing. Keep every fact, name, date and number exactly as given. " "Remove AI-sounding habits: hedging, filler connectives, over-formal phrasing, " "and uniform sentence rhythm. Do not add information. Return only the rewritten text.") msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "YOUR AI-WRITTEN TEXT"}]enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)out = model.generate(**enc, max_new_tokens=1024, do_sample=False)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))