Instructions to use epsil/Tinyllama-1.1B-miniguanaco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use epsil/Tinyllama-1.1B-miniguanaco with Transformers:

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

pipe = pipeline("text-generation", model="epsil/Tinyllama-1.1B-miniguanaco")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("epsil/Tinyllama-1.1B-miniguanaco")
model = AutoModelForMultimodalLM.from_pretrained("epsil/Tinyllama-1.1B-miniguanaco")
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 epsil/Tinyllama-1.1B-miniguanaco with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "epsil/Tinyllama-1.1B-miniguanaco"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epsil/Tinyllama-1.1B-miniguanaco",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/epsil/Tinyllama-1.1B-miniguanaco

SGLang

How to use epsil/Tinyllama-1.1B-miniguanaco 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 "epsil/Tinyllama-1.1B-miniguanaco" \
    --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": "epsil/Tinyllama-1.1B-miniguanaco",
		"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 "epsil/Tinyllama-1.1B-miniguanaco" \
        --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": "epsil/Tinyllama-1.1B-miniguanaco",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use epsil/Tinyllama-1.1B-miniguanaco with Docker Model Runner:
```
docker model run hf.co/epsil/Tinyllama-1.1B-miniguanaco
```

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Check out the documentation for more information.

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

datasets : miniguanaco

To use the model in the colab, use the below steps

!pip install accelerate
import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="epsil/Tinyllama-1.1B-miniguanaco", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "Are llm better than rule based chat?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

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