Instructions to use Corianas/tiny-llama-miniguanaco-1.5T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Corianas/tiny-llama-miniguanaco-1.5T with Transformers:

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

pipe = pipeline("text-generation", model="Corianas/tiny-llama-miniguanaco-1.5T")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Corianas/tiny-llama-miniguanaco-1.5T")
model = AutoModelForCausalLM.from_pretrained("Corianas/tiny-llama-miniguanaco-1.5T")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Corianas/tiny-llama-miniguanaco-1.5T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Corianas/tiny-llama-miniguanaco-1.5T"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Corianas/tiny-llama-miniguanaco-1.5T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Corianas/tiny-llama-miniguanaco-1.5T

SGLang

How to use Corianas/tiny-llama-miniguanaco-1.5T 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 "Corianas/tiny-llama-miniguanaco-1.5T" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Corianas/tiny-llama-miniguanaco-1.5T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Corianas/tiny-llama-miniguanaco-1.5T" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Corianas/tiny-llama-miniguanaco-1.5T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Corianas/tiny-llama-miniguanaco-1.5T with Docker Model Runner:
```
docker model run hf.co/Corianas/tiny-llama-miniguanaco-1.5T
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TinyLLama 1.5T checkpoint trained to answer questions.

f"{'prompt'}\n{'completion'}\n<END>"

No special formatting, just question, then newline to begin the answer.

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

pipe = pipeline("text-generation", model="Corianas/tiny-llama-miniguanaco-1.5T")# Load model directly

tokenizer = AutoTokenizer.from_pretrained("Corianas/tiny-llama-miniguanaco-1.5T")
model = AutoModelForCausalLM.from_pretrained("Corianas/tiny-llama-miniguanaco-1.5T")

# Run text generation pipeline with our next model
prompt = "What is a large language model?"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=500)
result = pipe(f"<s>{prompt}")
print(result[0]['generated_text'])

Result will have the answer, ending with <END> on a new line.

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Safetensors

Model size

1B params

Tensor type

F32

F16

Model tree for Corianas/tiny-llama-miniguanaco-1.5T

Quantizations

2 models