Instructions to use randhir302/HumanFlow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use randhir302/HumanFlow with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="randhir302/HumanFlow")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("randhir302/HumanFlow")
model = AutoModelForCausalLM.from_pretrained("randhir302/HumanFlow")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use randhir302/HumanFlow with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "randhir302/HumanFlow"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "randhir302/HumanFlow",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/randhir302/HumanFlow

SGLang

How to use randhir302/HumanFlow with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "randhir302/HumanFlow" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "randhir302/HumanFlow",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "randhir302/HumanFlow" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "randhir302/HumanFlow",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use randhir302/HumanFlow with Docker Model Runner:
```
docker model run hf.co/randhir302/HumanFlow
```

randhir302 commited on Apr 19

Commit

e438cf5

verified ·

1 Parent(s): d046242

Update README.md

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README.md CHANGED Viewed

@@ -45,70 +45,64 @@ model-index:
       value: 0.0528
 ---
-<div align="center">
-<img src="https://huggingface.co/randhir302/HumanFlow/resolve/main/humanflow_logofinal.png" width="300"/>
 # HumanFlow-Llama3-8B
-### Premium Open-Source AI Rewriting Model
-### Converts Robotic AI Text into Natural Human Flow
-<p>
-<a href="https://huggingface.co/randhir302/HumanFlow">
-<img src="https://img.shields.io/badge/🤗%20HuggingFace-Live-orange">
-</a>
-<a href="https://github.com/iamhero2709/HumanFlow">
-<img src="https://img.shields.io/badge/GitHub-Repository-black">
-</a>
-<a href="#">
-<img src="https://img.shields.io/badge/License-Apache--2.0-blue">
-</a>
-<a href="#">
-<img src="https://img.shields.io/badge/Model-Llama3--8B-green">
-</a>
-</p>
-**Built for creators, students, agencies, founders, and developers who need text that sounds genuinely human.**
-</div>
 ---
-# Why HumanFlow?
-Most AI outputs follow predictable patterns:
-- Over-polished wording
-- Repetitive sentence rhythm
-- Detectable structure
-- Robotic transitions
-- Generic emotional tone
-HumanFlow was fine-tuned to improve **flow, structure, rhythm, tone, and realism** instead of only replacing words.
-That means output feels written by a real person.
 ---
-# Performance Snapshot
-| Metric | Base Llama-3 8B | HumanFlow |
-|-------|----------------|----------|
-| Human Score | 18% | **99%** |
 | Natural Tone | Low | **High** |
 | Rewrite Quality | Basic | **Advanced** |
-| Readability | Generic | **Natural** |
 ---
-# Core Evaluation
 | Metric | Score |
-|------|------|
 | BERTScore F1 | **0.8424** |
 | ROUGE-L | **0.0908** |
 | Perplexity | **1.5242** |
@@ -116,44 +110,31 @@ That means output feels written by a real person.
 ---
-# What Makes HumanFlow Different?
-## Structural Humanization
-Changes how ideas are expressed, not just words.
-## Meaning Preservation
-Keeps intent while improving realism.
-## Ready for Real Use
-Deploy locally or integrate into apps.
----
-# Ideal Use Cases
-- Blog writing
-- SEO teams
-- Students
-- Researchers
-- SaaS tools
 - Email personalization
-- AI content workflows
 ---
-# Before vs After
-## Input
 > In today’s rapidly evolving digital landscape, it is imperative for organizations to leverage strategic methodologies in order to maximize engagement.
-## HumanFlow Output
 > Online markets move fast. If a company wants attention, it needs smart strategy, clear messaging, and content people actually care about.
 ---
-# Quickstart
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -185,4 +166,34 @@ outputs = model.generate(
     repetition_penalty=1.10
 )
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))

       value: 0.0528
 ---
+<p align="center">
+  <img src="https://huggingface.co/randhir302/HumanFlow/resolve/main/humanflow_logofinal.png" width="180">
+</p>
 # HumanFlow-Llama3-8B
+### Humanize AI Text with Natural Structure, Flow & Tone
+[🤗 Hugging Face Model](https://huggingface.co/randhir302/HumanFlow) •
+[💻 GitHub Repository](https://github.com/iamhero2709/HumanFlow) •
+Apache-2.0 License
+---
+## Overview
+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:
+- sentence rhythm
+- structure
+- tone
+- flow
+- readability
+- realism
 ---
+## Why HumanFlow?
+Most AI-generated text feels:
+- repetitive
+- over-polished
+- generic
+- predictable
+- emotionally flat
+HumanFlow rewrites outputs to feel more organic and naturally written.
 ---
+## Performance Snapshot
+| Metric | Base Model | HumanFlow |
+|--------|-----------|----------|
+| Human-Like Score | 18% | **99%** |
 | Natural Tone | Low | **High** |
 | Rewrite Quality | Basic | **Advanced** |
+| Readability | Generic | **Strong** |
 ---
+## Internal Evaluation
 | Metric | Score |
+|--------|------|
 | BERTScore F1 | **0.8424** |
 | ROUGE-L | **0.0908** |
 | Perplexity | **1.5242** |
 ---
+## Best Use Cases
+- SEO rewriting
+- Blog enhancement
+- Student writing cleanup
 - Email personalization
+- AI content polishing
+- SaaS integrations
+- Human-style generation pipelines
 ---
+## Before vs After
+### Input
 > In today’s rapidly evolving digital landscape, it is imperative for organizations to leverage strategic methodologies in order to maximize engagement.
+### HumanFlow Output
 > Online markets move fast. If a company wants attention, it needs smart strategy, clear messaging, and content people actually care about.
 ---
+## Quickstart
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
     repetition_penalty=1.10
 )
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+## Recommended Settings
+```python
+temperature = 0.75
+top_p = 0.90
+repetition_penalty = 1.10
+max_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