Instructions to use RichardErkhov/Shahm_-_bart-german-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Shahm_-_bart-german-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/Shahm_-_bart-german-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/Shahm_-_bart-german-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/Shahm_-_bart-german-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/Shahm_-_bart-german-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/Shahm_-_bart-german-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/Shahm_-_bart-german-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/Shahm_-_bart-german-8bits

SGLang

How to use RichardErkhov/Shahm_-_bart-german-8bits 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 "RichardErkhov/Shahm_-_bart-german-8bits" \
    --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": "RichardErkhov/Shahm_-_bart-german-8bits",
		"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 "RichardErkhov/Shahm_-_bart-german-8bits" \
        --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": "RichardErkhov/Shahm_-_bart-german-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/Shahm_-_bart-german-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Shahm_-_bart-german-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

bart-german - bnb 8bits

Model creator: https://huggingface.co/Shahm/
Original model: https://huggingface.co/Shahm/bart-german/

Original model description:

license: apache-2.0 tags: - generated_from_trainer - summarization datasets: - mlsum language: de metrics: - rouge model-index: - name: mode-bart-deutsch results: - task: name: Summarization type: summarization dataset: name: mlsum de type: mlsum args: de metrics: - name: Rouge1 type: rouge value: 41.698

mode-bart-deutsch

This model is a fine-tuned version of facebook/bart-base on the mlsum de dataset. It achieves the following results on the evaluation set:

Loss: 1.2152
Rouge1: 41.698
Rouge2: 31.3548
Rougel: 38.2817
Rougelsum: 39.6349
Gen Len: 63.1723

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: 6
eval_batch_size: 6
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.0+cu111
Datasets 1.17.0
Tokenizers 0.10.3

Downloads last month: 4

Safetensors

Model size

96.2M params

Tensor type

F32

F16