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

Libraries

How to use TouchNight/HumanlikeRP with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TouchNight/HumanlikeRP")
model = AutoModelForCausalLM.from_pretrained("TouchNight/HumanlikeRP")
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

vLLM

How to use TouchNight/HumanlikeRP with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TouchNight/HumanlikeRP

SGLang

How to use TouchNight/HumanlikeRP 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 "TouchNight/HumanlikeRP" \
    --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": "TouchNight/HumanlikeRP",
		"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 "TouchNight/HumanlikeRP" \
        --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": "TouchNight/HumanlikeRP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use TouchNight/HumanlikeRP with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TouchNight/HumanlikeRP to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TouchNight/HumanlikeRP to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TouchNight/HumanlikeRP to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="TouchNight/HumanlikeRP",
    max_seq_length=2048,
)

Docker Model Runner
How to use TouchNight/HumanlikeRP with Docker Model Runner:
```
docker model run hf.co/TouchNight/HumanlikeRP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

HumanlikeRP

It is an attempt to build a Humanlike chatbot.
Designed to make it give short reply like a real human.

It is a failure, the dataset used to train it has weak context relevancy. So it often generates irrelevant answer. And it is also overfitting.

Chat Format

This model has been trained to use ChatML format.

<|im_start|>system
{{system}}<|im_end|>
<|im_start|>{{char}}
{{message}}<|im_end|>
<|im_start|>{{user}}
{{message}}<|im_end|>

Uploaded model

Developed by: TouchNight
License: apache-2.0
Finetuned from model : cognitivecomputations/dolphin-2.9.1-yi-1.5-9b

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 8

Model tree for TouchNight/HumanlikeRP

Base model

01-ai/Yi-1.5-9B

Finetuned

dphn/dolphin-2.9.1-yi-1.5-9b

Finetuned

(2)

this model

Quantizations

3 models

TouchNight
/

HumanlikeRP

HumanlikeRP

Chat Format

Uploaded model

Model tree for TouchNight/HumanlikeRP

Dataset used to train TouchNight/HumanlikeRP

Space using TouchNight/HumanlikeRP 1