Instructions to use LumiCharles/genshin-starlit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LumiCharles/genshin-starlit with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LumiCharles/genshin-starlit")
model = AutoModelForCausalLM.from_pretrained("LumiCharles/genshin-starlit", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LumiCharles/genshin-starlit with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LumiCharles/genshin-starlit

SGLang

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

Docker Model Runner
How to use LumiCharles/genshin-starlit with Docker Model Runner:
```
docker model run hf.co/LumiCharles/genshin-starlit
```

Genshin Starlit

Genshin Starlit is a LLaMA-based causal language model fine-tuned for
roleplay and character-driven dialogue.

The model prioritizes immersive conversational flow and in-character consistency over strict factual accuracy.

Model Details

Architecture: LlamaForCausalLM
Parameters: ~71B
Precision: bfloat16 (BF16)
Context Length: up to 131k tokens
Format: Safetensors (sharded)

Training and Composition

This model was created by merging two LoRA adapters into a single checkpoint:

Character Dialogue Adapter
Trained on character-specific dialogue transcripts to improve tone, personality, and speech consistency.
Lore Knowledge Adapter
Trained on lore-oriented question–answer style data to improve narrative coherence and lore-aware responses during roleplay.

The merged model balances immersive roleplay with contextual lore awareness during generation.

Intended Use

Recommended uses

Roleplay and character simulation
Creative writing and dialogue
Interactive chat applications
Narrative-driven assistants

Not recommended

Factual or extractive question answering
Retrieval-augmented generation
Safety-critical or professional domains

Chat / Roleplay Usage

This model uses a chat template and is intended to be run in a conversational setting.

Example (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "your-username/genshin-starlit"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="bfloat16",
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a fictional character engaging in immersive roleplay."},
    {"role": "user", "content": "Hello. Who are you?"}
]

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

outputs = model.generate(
    inputs,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.8,
    top_p=0.9
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 10

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for LumiCharles/genshin-starlit

Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.3-70B-Instruct

Finetuned

(658)

this model

LumiCharles
/

genshin-starlit