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randhir302
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Model Details
Model Provider
randhir302
Model Tree
unsloth/llama-3-8b-Instruct-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
HumanFlow-Llama3-8B is a fine-tuned Llama 3 model designed to transform robotic AI-generated writing into content that feels natural, human, readable, and authentic.
Instead of replacing words only, HumanFlow improves:
Most AI-generated text feels:
HumanFlow rewrites outputs to feel more organic and naturally written.
| Metric | Base Model | HumanFlow |
|---|---|---|
| Human-Like Score | 18% | 99% |
| Natural Tone | Low | High |
| Rewrite Quality | Basic | Advanced |
| Readability | Generic | Strong |
| Metric | Score |
|---|---|
| BERTScore F1 | 0.8424 |
| ROUGE-L | 0.0908 |
| Perplexity | 1.5242 |
| Text Overlap | 0.0528 |
In today’s rapidly evolving digital landscape, it is imperative for organizations to leverage strategic methodologies in order to maximize engagement.
Online markets move fast. If a company wants attention, it needs smart strategy, clear messaging, and content people actually care about.
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "randhir302/HumanFlow" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") prompt = """Rewrite this in a more human tone: Artificial intelligence is transforming industries worldwide.""" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=220, temperature=0.75, top_p=0.90, repetition_penalty=1.10) print(tokenizer.decode(outputs[0], skip_special_tokens=True))python
temperature = 0.75top_p = 0.90repetition_penalty = 1.10max_new_tokens = 700 ## Roadmap - [x] Public Launch - [x] Hugging Face Release - [x] Fine-Tuned Base Model - [ ] GGUF Quantized Release - [ ] HumanFlow Pro API - [ ] Browser Editor - [ ] Multilingual Version --- ## Community If HumanFlow helps you: ⭐ Like the model ⭐ Share outputs ⭐ Benchmark it ⭐ Build products with itfrom transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "randhir302/HumanFlow" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") prompt = """Rewrite this in a more human tone: Artificial intelligence is transforming industries worldwide.""" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=220, temperature=0.75, top_p=0.90, repetition_penalty=1.10) print(tokenizer.decode(outputs[0], skip_special_tokens=True))temperature = 0.75top_p = 0.90repetition_penalty = 1.10max_new_tokens = 700 ## Roadmap - [x] Public Launch - [x] Hugging Face Release - [x] Fine-Tuned Base Model - [ ] GGUF Quantized Release - [ ] HumanFlow Pro API - [ ] Browser Editor - [ ] Multilingual Version --- ## Community If HumanFlow helps you: ⭐ Like the model ⭐ Share outputs ⭐ Benchmark it ⭐ Build products with it