Instructions to use mrm8488/bloom-560m-finetuned-common_gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/bloom-560m-finetuned-common_gen with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/bloom-560m-finetuned-common_gen")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/bloom-560m-finetuned-common_gen")
model = AutoModelForCausalLM.from_pretrained("mrm8488/bloom-560m-finetuned-common_gen")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mrm8488/bloom-560m-finetuned-common_gen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/bloom-560m-finetuned-common_gen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/bloom-560m-finetuned-common_gen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/bloom-560m-finetuned-common_gen

SGLang

How to use mrm8488/bloom-560m-finetuned-common_gen 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 "mrm8488/bloom-560m-finetuned-common_gen" \
    --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": "mrm8488/bloom-560m-finetuned-common_gen",
		"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 "mrm8488/bloom-560m-finetuned-common_gen" \
        --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": "mrm8488/bloom-560m-finetuned-common_gen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/bloom-560m-finetuned-common_gen with Docker Model Runner:
```
docker model run hf.co/mrm8488/bloom-560m-finetuned-common_gen
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

bloom-560m-finetuned-common_gen

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

Loss: 1.3937

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: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.4819	0.38	100	1.4582
1.2451	0.76	200	1.4247
1.1016	1.14	300	1.4382
0.9724	1.53	400	1.4207
0.9396	1.91	500	1.3949

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1

Downloads last month: 9

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for mrm8488/bloom-560m-finetuned-common_gen

Quantizations

2 models

mrm8488
/

bloom-560m-finetuned-common_gen

bloom-560m-finetuned-common_gen

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for mrm8488/bloom-560m-finetuned-common_gen

Dataset used to train mrm8488/bloom-560m-finetuned-common_gen