Instructions to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora with Transformers:

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

pipe = pipeline("text-generation", model="SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora")
model = AutoModelForCausalLM.from_pretrained("SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora")
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 SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora

SGLang

How to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora 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 "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora" \
    --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": "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora",
		"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 "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora" \
        --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": "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora with Docker Model Runner:
```
docker model run hf.co/SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora
```

tinyllama-1.1B-intermediate-step-715k-1.5T-sft-lora

This model is a fine-tuned version of PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.4638

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 128
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.459	0.7	285	1.4638

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu121
Datasets 2.14.6
Tokenizers 0.14.1

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