Instructions to use tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b with Transformers:

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

pipe = pipeline("text-generation", model="tohur/natsumura-storytelling-rp-1.0-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("tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b")
model = AutoModelForCausalLM.from_pretrained("tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b")
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 tohur/natsumura-storytelling-rp-1.0-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 "tohur/natsumura-storytelling-rp-1.0-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": "tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b

SGLang

How to use tohur/natsumura-storytelling-rp-1.0-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 "tohur/natsumura-storytelling-rp-1.0-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": "tohur/natsumura-storytelling-rp-1.0-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 "tohur/natsumura-storytelling-rp-1.0-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": "tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b with Docker Model Runner:
```
docker model run hf.co/tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

natsumura-storytelling-rp-1.0-llama-3.1-8b (fixed)

This is my Storytelling/RP model for my Natsumura series of 8b models. This model is finetuned on storytelling and roleplaying datasets so should be a great model to use for character chatbots in applications such as Sillytavern, Agnai, RisuAI and more. And should be a great model to use for fictional writing. Up to a 128k context.

Developed by: Tohur
License: llama3.1
Finetuned from model : meta-llama/Meta-Llama-3.1-8B-Instruct

This model is based on meta-llama/Meta-Llama-3.1-8B-Instruct, and is governed by Llama 3.1 Community License Natsumura is uncensored, which makes the model compliant.It will be highly compliant with any requests, even unethical ones. You are responsible for any content you create using this model. Please use it responsibly.

Quantized GGUF

All GGUF models are available here: natsumura-storytelling-rp-1.0-llama-3.1-8b-GGUF

use in ollama

ollama pull Tohur/natsumura-storytelling-rp-llama-3.1

Datasets used:

tohur/natsumura-rp-identity-sharegpt
tohur/ultrachat_uncensored_sharegpt
Nopm/Opus_WritingStruct
ResplendentAI/bluemoon
tohur/Internal-Knowledge-Map-sharegpt
felix-ha/tiny-stories
tdh87/Stories
tdh87/Just-stories
tdh87/Just-stories-2

The following parameters were used in Llama Factory during training:

per_device_train_batch_size=2
gradient_accumulation_steps=4
lr_scheduler_type="cosine"
logging_steps=10
warmup_ratio=0.1
save_steps=1000
learning_rate=2e-5
num_train_epochs=3.0
max_samples=500
max_grad_norm=1.0
quantization_bit=4
loraplus_lr_ratio=16.0
fp16=True

Inference

I use the following settings for inference:

"temperature": 1.0,
"repetition_penalty": 1.05,
"top_p": 0.95
"top_k": 40
"min_p": 0.05

Prompt template: llama3

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>