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

Libraries

How to use Arivukkarasu/Tiny_Llama_Storyteller with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Arivukkarasu/Tiny_Llama_Storyteller")
model = AutoModelForCausalLM.from_pretrained("Arivukkarasu/Tiny_Llama_Storyteller")
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 Arivukkarasu/Tiny_Llama_Storyteller with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Arivukkarasu/Tiny_Llama_Storyteller

SGLang

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

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

See axolotl config

axolotl version: 0.9.1.post1

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
batch_size: 4
bf16: auto
datasets:
- path: jaydenccc/AI_Storyteller_Dataset
  type:
    field_instruction: synopsis
    field_output: short_story
    field_system: system
    format: <|user|> {instruction} </s> <|assistant|>
    no_input_format: <|user|> {instruction} </s> <|assistant|>
    system_prompt: ''
learning_rate: 0.0002
logging_steps: 1
micro_batch_size: 2
model_type: LlamaForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: ./models/Tiny_Llama_Storyteller
sequence_length: 1024
tf32: false
tokenizer_type: LlamaTokenizer

models/Tiny_Llama_Storyteller

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the jaydenccc/AI_Storyteller_Dataset dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 2
num_epochs: 4.0

Training results

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 1

Safetensors

Model size

1B params

Tensor type

F32

BF16

Model tree for Arivukkarasu/Tiny_Llama_Storyteller

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Finetuned

(557)

this model

Quantizations

1 model

Arivukkarasu
/

Tiny_Llama_Storyteller

models/Tiny_Llama_Storyteller

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Arivukkarasu/Tiny_Llama_Storyteller

Dataset used to train Arivukkarasu/Tiny_Llama_Storyteller