Instructions to use vicgalle/Humanish-Roleplay-Llama-3.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vicgalle/Humanish-Roleplay-Llama-3.1-8B with Transformers:

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

pipe = pipeline("text-generation", model="vicgalle/Humanish-Roleplay-Llama-3.1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vicgalle/Humanish-Roleplay-Llama-3.1-8B")
model = AutoModelForCausalLM.from_pretrained("vicgalle/Humanish-Roleplay-Llama-3.1-8B", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vicgalle/Humanish-Roleplay-Llama-3.1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vicgalle/Humanish-Roleplay-Llama-3.1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vicgalle/Humanish-Roleplay-Llama-3.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vicgalle/Humanish-Roleplay-Llama-3.1-8B

SGLang

How to use vicgalle/Humanish-Roleplay-Llama-3.1-8B 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 "vicgalle/Humanish-Roleplay-Llama-3.1-8B" \
    --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": "vicgalle/Humanish-Roleplay-Llama-3.1-8B",
		"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 "vicgalle/Humanish-Roleplay-Llama-3.1-8B" \
        --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": "vicgalle/Humanish-Roleplay-Llama-3.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vicgalle/Humanish-Roleplay-Llama-3.1-8B with Docker Model Runner:
```
docker model run hf.co/vicgalle/Humanish-Roleplay-Llama-3.1-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Humanish-Roleplay-Llama-3.1-8B

A DPO-tuned Llama-3.1 to behave more "humanish", i.e., avoiding all the AI assistant slop. It also works for role-play (RP). To achieve this, the model was fine-tuned over a series of datasets:

General conversations from Claude Opus, from Undi95/Meta-Llama-3.1-8B-Claude
Undi95/Weyaxi-humanish-dpo-project-noemoji, to make the model react as a human, rejecting assistant-like or too neutral responses.
ResplendentAI/NSFW_RP_Format_DPO, to steer the model towards using the *action* format in RP settings. Works best if in the first message you also use this format naturally (see example)

Usage example

conversation = [{'role': 'user', 'content': """*With my face blushing in red* Tell me about your favorite film!"""}]

prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device) 

outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.8)

The response is

*Giggles nervously* Oh my, um, *clears throat*, well... I'm not really sure if I should be sharing my favorite film with you like this! *Blushes even more* But if you insist, *gulps*, I suppose I could tell you about this really romantic movie I saw once... *Leans in close and whispers* It's called "When Harry Met Sally"! *Squeals and covers mouth* You know, it's this super cute story about two friends who try to keep things platonic for a whole year, but end up falling for each other in the end! *Squirms in seat* The chemistry between the leads is just... *Sighs wistfully* There's this famous scene in a diner where... *Waves hand in front of face*... where you know what happens, and it's just so *Blushes bright red*... realistic and relatable! *Giggles* I mean, we've all been there, right? *Winks* Anyway, it's just such a classic rom-com with great dialogue and a perfect soundtrack! *Sways dreamily* I could watch it over and over again. *Tilts head* What about you, do you have a favorite film? *Peeks at you with big, hopeful eyes* *Squeals* I'm dying to know! *Bounces in seat excitedly*

Fine-tuning script

The script I used to train this model is available at this file. It takes less than one hour in a T4 GPU, and requires the following libs:

peft                     0.11.1
transformers             4.44.0.dev0
trl                      0.9.6

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Model size

8B params

Tensor type

F16

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Humanish-Roleplay-Llama-3.1-8B

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Usage example

Fine-tuning script

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