Instructions to use jeff31415/TinyLlama-1.1B-1T-OpenOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeff31415/TinyLlama-1.1B-1T-OpenOrca with Transformers:

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

pipe = pipeline("text-generation", model="jeff31415/TinyLlama-1.1B-1T-OpenOrca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jeff31415/TinyLlama-1.1B-1T-OpenOrca")
model = AutoModelForCausalLM.from_pretrained("jeff31415/TinyLlama-1.1B-1T-OpenOrca")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jeff31415/TinyLlama-1.1B-1T-OpenOrca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeff31415/TinyLlama-1.1B-1T-OpenOrca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeff31415/TinyLlama-1.1B-1T-OpenOrca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca

SGLang

How to use jeff31415/TinyLlama-1.1B-1T-OpenOrca 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 "jeff31415/TinyLlama-1.1B-1T-OpenOrca" \
    --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": "jeff31415/TinyLlama-1.1B-1T-OpenOrca",
		"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 "jeff31415/TinyLlama-1.1B-1T-OpenOrca" \
        --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": "jeff31415/TinyLlama-1.1B-1T-OpenOrca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jeff31415/TinyLlama-1.1B-1T-OpenOrca with Docker Model Runner:
```
docker model run hf.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Base model:

PY007/TinyLlama-1.1B-intermediate-step-480k-1T

Dataset:

Fine tuned on OpenOrca GPT4 subset for 1 epoch,Using CHATML format

Model License:

Apache 2.0, following the TinyLlama base model.

Quantisation:

Hardware and training details:

Hardware: 1*RTX A5000, ~16 hours to complete 1 epoch. GPU from autodl.com, cost around $3 for this finetuning. https://wandb.ai/jeff200402/TinyLlama-Orca?workspace= for more details.

Downloads last month: 777

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for jeff31415/TinyLlama-1.1B-1T-OpenOrca

Finetunes

4 models

Quantizations

5 models

jeff31415
/

TinyLlama-1.1B-1T-OpenOrca