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 16

Commit

eaa1072

verified ·

1 Parent(s): f5e9a00

Upload README.md with huggingface_hub

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README.md +29 -31

README.md CHANGED Viewed

@@ -1,41 +1,26 @@
 ---
 language:
-  - en
 license: apache-2.0
 base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
 library_name: transformers
 pipeline_tag: text-generation
 tags:
-  - llama-3
-  - unsloth
-  - lora
-  - merged
-  - sft
-  - humanizer
-  - conversational
 widget:
-  - text: "Rewrite this in a more human tone: The system is functioning correctly."
-    example_title: "Smooth System"
-  - text: "Rewrite this in a more human tone: The implementation has been completed successfully."
-    example_title: "Successful Setup"
-  - text: "Rewrite this in a more human tone: The user is advised to proceed with caution."
-    example_title: "Friendly Warning"
-model-index:
-  - name: HumanizeAI-LLaMA3
-    results:
-      - task:
-          type: text-generation
-          name: Text Generation
-        metrics:
-          - type: custom
-            value: 8.7
-            name: Fluency
-          - type: custom
-            value: 8.5
-            name: Human-likeness
-          - type: custom
-            value: 9.2
-            name: Meaning Preservation
 ---
 # 🚀HumanFlow — LLaMA3 Humanizer Model
@@ -119,4 +104,17 @@ prompt = "Rewrite this in a more human tone: The system is functioning properly.
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 outputs = model.generate(**inputs, max_new_tokens=120)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))

 ---
 language:
+- en
 license: apache-2.0
 base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
 library_name: transformers
 pipeline_tag: text-generation
 tags:
+- llama-3
+- unsloth
+- lora
+- merged
+- sft
+- humanizer
+- conversational
 widget:
+- text: 'Rewrite this in a more human tone: The system is functioning correctly.'
+  example_title: Smooth System
+- text: 'Rewrite this in a more human tone: The implementation has been completed
+    successfully.'
+  example_title: Successful Setup
+- text: 'Rewrite this in a more human tone: The user is advised to proceed with caution.'
+  example_title: Friendly Warning
 ---
 # 🚀HumanFlow — LLaMA3 Humanizer Model
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 outputs = model.generate(**inputs, max_new_tokens=120)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+## Evaluation Results (Automated)
+The model was evaluated using a professional suite at temperature 0.7.
+| Metric | Value | Interpretation |
+| :--- | :--- | :--- |
+| **BERTScore F1** | 0.8424 | Semantic Similarity to Prompts |
+| **ROUGE-L** | 0.0908 | Low overlap indicates original generation |
+| **Perplexity** | 1.5242 | Confidence/Coherence (Lower is better) |
+| **Text Overlap** | 0.0528 | Lexical similarity to input |
+*Results generated and uploaded via Colab automated pipeline.*