Instructions to use antphb/DS-Chatbox-bigscience-bloom-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use antphb/DS-Chatbox-bigscience-bloom-560m with Transformers:

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

pipe = pipeline("text-generation", model="antphb/DS-Chatbox-bigscience-bloom-560m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("antphb/DS-Chatbox-bigscience-bloom-560m")
model = AutoModelForCausalLM.from_pretrained("antphb/DS-Chatbox-bigscience-bloom-560m")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use antphb/DS-Chatbox-bigscience-bloom-560m with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/antphb/DS-Chatbox-bigscience-bloom-560m

SGLang

How to use antphb/DS-Chatbox-bigscience-bloom-560m 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 "antphb/DS-Chatbox-bigscience-bloom-560m" \
    --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": "antphb/DS-Chatbox-bigscience-bloom-560m",
		"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 "antphb/DS-Chatbox-bigscience-bloom-560m" \
        --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": "antphb/DS-Chatbox-bigscience-bloom-560m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use antphb/DS-Chatbox-bigscience-bloom-560m with Docker Model Runner:
```
docker model run hf.co/antphb/DS-Chatbox-bigscience-bloom-560m
```

DS-Chatbox-bigscience-bloom-560m

This model is a fine-tuned version of bigscience/bloom-560m on the None dataset. It achieves the following results on the evaluation set:

eval_loss: 4.8320
eval_runtime: 175.7948
eval_samples_per_second: 37.402
eval_steps_per_second: 4.676
epoch: 0.03
step: 500

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: 0.0005
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 3.0

Framework versions

Transformers 4.30.1
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

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