Instructions to use EnDevSols/tinyllama-2.5T-Clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EnDevSols/tinyllama-2.5T-Clinical with Transformers:

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

pipe = pipeline("text-generation", model="EnDevSols/tinyllama-2.5T-Clinical")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EnDevSols/tinyllama-2.5T-Clinical")
model = AutoModelForCausalLM.from_pretrained("EnDevSols/tinyllama-2.5T-Clinical")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EnDevSols/tinyllama-2.5T-Clinical with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EnDevSols/tinyllama-2.5T-Clinical"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EnDevSols/tinyllama-2.5T-Clinical",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EnDevSols/tinyllama-2.5T-Clinical

SGLang

How to use EnDevSols/tinyllama-2.5T-Clinical 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 "EnDevSols/tinyllama-2.5T-Clinical" \
    --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": "EnDevSols/tinyllama-2.5T-Clinical",
		"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 "EnDevSols/tinyllama-2.5T-Clinical" \
        --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": "EnDevSols/tinyllama-2.5T-Clinical",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EnDevSols/tinyllama-2.5T-Clinical with Docker Model Runner:
```
docker model run hf.co/EnDevSols/tinyllama-2.5T-Clinical
```

TinyLlama-1.1B

https://github.com/jzhang38/TinyLlama

Finetuning TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T model on Clinical Dataset.

Eval

Model	Pretrain Tokens	HellaSwag	Obqa	WinoGrande	ARC_c	ARC_e	boolq	piqa	avg
Pythia-1.0B	300B	47.16	31.40	53.43	27.05	48.99	60.83	69.21	48.30
TinyLlama-1.1B-intermediate-step-50K-104b	103B	43.50	29.80	53.28	24.32	44.91	59.66	67.30	46.11
TinyLlama-1.1B-intermediate-step-240k-503b	503B	49.56	31.40	55.80	26.54	48.32	56.91	69.42	48.28
TinyLlama-1.1B-intermediate-step-480k-1007B	1007B	52.54	33.40	55.96	27.82	52.36	59.54	69.91	50.22
TinyLlama-1.1B-intermediate-step-715k-1.5T	1.5T	53.68	35.20	58.33	29.18	51.89	59.08	71.65	51.29
TinyLlama-1.1B-intermediate-step-955k-2T	2T	54.63	33.40	56.83	28.07	54.67	63.21	70.67	51.64
TinyLlama-1.1B-intermediate-step-1195k-token-2.5T	2.5T	58.96	34.40	58.72	31.91	56.78	63.21	73.07	53.86

Downloads last month: 8

Safetensors

Model size

1B params

Tensor type

F32