Instructions to use aloobun/TinyAiroboros-2.2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aloobun/TinyAiroboros-2.2.1 with Transformers:

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

pipe = pipeline("text-generation", model="aloobun/TinyAiroboros-2.2.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aloobun/TinyAiroboros-2.2.1")
model = AutoModelForCausalLM.from_pretrained("aloobun/TinyAiroboros-2.2.1")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aloobun/TinyAiroboros-2.2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aloobun/TinyAiroboros-2.2.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aloobun/TinyAiroboros-2.2.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aloobun/TinyAiroboros-2.2.1

SGLang

How to use aloobun/TinyAiroboros-2.2.1 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 "aloobun/TinyAiroboros-2.2.1" \
    --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": "aloobun/TinyAiroboros-2.2.1",
		"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 "aloobun/TinyAiroboros-2.2.1" \
        --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": "aloobun/TinyAiroboros-2.2.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aloobun/TinyAiroboros-2.2.1 with Docker Model Runner:
```
docker model run hf.co/aloobun/TinyAiroboros-2.2.1
```

This model is a fine-tuned version of PY007/TinyLlama-1.1B-Chat-v0.3 (finetuned on 15k rows of airoboros-2.2.1 dataset)

lm-eval

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.2671|±  |0.0129|
|             |       |acc_norm|0.2850|±  |0.0132|
|arc_easy     |      0|acc     |0.5673|±  |0.0102|
|             |       |acc_norm|0.5109|±  |0.0103|
|boolq        |      1|acc     |0.6040|±  |0.0086|
|hellaswag    |      0|acc     |0.4155|±  |0.0049|
|             |       |acc_norm|0.5420|±  |0.0050|
|openbookqa   |      0|acc     |0.2200|±  |0.0185|
|             |       |acc_norm|0.3420|±  |0.0212|
|piqa         |      0|acc     |0.7057|±  |0.0106|
|             |       |acc_norm|0.6970|±  |0.0107|
|winogrande   |      0|acc     |0.5714|±  |0.0139|

Usage:

from transformers import AutoTokenizer
import transformers
import torch

model = "aloobun/TinyAiroboros-2.2.1"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "Write a short story about a dystopian society."

sequences = pipeline(
    f'[INST] {prompt} [/INST]',
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_length=1024,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Downloads last month: 7

Safetensors

Model size

1B params

Tensor type

F16

F32

aloobun
/

TinyAiroboros-2.2.1

lm-eval

Usage:

Dataset used to train aloobun/TinyAiroboros-2.2.1