Instructions to use jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01 with Transformers:

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

pipe = pipeline("text-generation", model="jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01")
model = AutoModelForCausalLM.from_pretrained("jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01

SGLang

How to use jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01 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 "jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01" \
    --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": "jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01",
		"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 "jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01" \
        --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": "jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01 with Docker Model Runner:
```
docker model run hf.co/jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01
```

bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01

This model is a fine-tuned version of bigscience/bloomz-560m on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 220.0167

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: 0.05
train_batch_size: 10
eval_batch_size: 10
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	63	200.9191
155.6973	2.0	126	311.3371
155.6973	3.0	189	256.3193
269.4904	4.0	252	291.8558
273.0994	5.0	315	220.0167

Framework versions

Transformers 4.36.2
Pytorch 2.0.1
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 3

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01

Base model

bigscience/bloomz-560m

Finetuned

(33)

this model