Instructions to use Josephgflowers/tinyllama-730M-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Josephgflowers/tinyllama-730M-test with Transformers:

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

pipe = pipeline("text-generation", model="Josephgflowers/tinyllama-730M-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Josephgflowers/tinyllama-730M-test")
model = AutoModelForCausalLM.from_pretrained("Josephgflowers/tinyllama-730M-test")
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 Josephgflowers/tinyllama-730M-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Josephgflowers/tinyllama-730M-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Josephgflowers/tinyllama-730M-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Josephgflowers/tinyllama-730M-test

SGLang

How to use Josephgflowers/tinyllama-730M-test 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 "Josephgflowers/tinyllama-730M-test" \
    --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": "Josephgflowers/tinyllama-730M-test",
		"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 "Josephgflowers/tinyllama-730M-test" \
        --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": "Josephgflowers/tinyllama-730M-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Josephgflowers/tinyllama-730M-test with Docker Model Runner:
```
docker model run hf.co/Josephgflowers/tinyllama-730M-test
```

I cut my TinyLlama 1.1B cinder v 2 down from 22 layers to 14. At 14 there was no coherent text but there were emerging ideas of a response. 1000 steps on step-by-step dataset. 6000 on Reason-with-cinder. The loss was still over 1 and the learning rate was still over 4. This model needs significat training. I am putting it up as a base model that needs work. If you continue training please let me know on the tinyllama discord, I have some interesting plans for this model.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.55
AI2 Reasoning Challenge (25-Shot)	25.09
HellaSwag (10-Shot)	33.82
MMLU (5-Shot)	24.43
TruthfulQA (0-shot)	42.90
Winogrande (5-shot)	51.07
GSM8k (5-shot)	0.00

Downloads last month: 61

Safetensors

Model size

0.7B params

Tensor type

F32

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

25.090
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

33.820
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

24.430
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

42.900
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

51.070
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.000