Instructions to use hosseinbv/prog-y-tiny-llama-CDL-13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hosseinbv/prog-y-tiny-llama-CDL-13 with Transformers:

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

pipe = pipeline("text-generation", model="hosseinbv/prog-y-tiny-llama-CDL-13")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("hosseinbv/prog-y-tiny-llama-CDL-13", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hosseinbv/prog-y-tiny-llama-CDL-13 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hosseinbv/prog-y-tiny-llama-CDL-13"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hosseinbv/prog-y-tiny-llama-CDL-13",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hosseinbv/prog-y-tiny-llama-CDL-13

SGLang

How to use hosseinbv/prog-y-tiny-llama-CDL-13 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 "hosseinbv/prog-y-tiny-llama-CDL-13" \
    --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": "hosseinbv/prog-y-tiny-llama-CDL-13",
		"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 "hosseinbv/prog-y-tiny-llama-CDL-13" \
        --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": "hosseinbv/prog-y-tiny-llama-CDL-13",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hosseinbv/prog-y-tiny-llama-CDL-13 with Docker Model Runner:
```
docker model run hf.co/hosseinbv/prog-y-tiny-llama-CDL-13
```

progressive-yoco-tiny-llama-CDL-13

This model is a fine-tuned version of /ephemeral/hossein/output/progressive-yoco-tiny-llama-CDL-14 on the reformatted_ultrachat_200k, the reformatted_MathInstruct and the small_slim_pajama datasets.

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: 42
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 6
total_train_batch_size: 2016
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.005
training_steps: 150

Training results

Framework versions

Transformers 4.45.2
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 3

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for hosseinbv/prog-y-tiny-llama-CDL-13

Base model

TinyLlama/TinyLlama_v1.1

Finetuned

(54)

this model