Instructions to use RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/Azma-AI_-_bart-large-text-summarizer-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/Azma-AI_-_bart-large-text-summarizer-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits

SGLang

How to use RichardErkhov/Azma-AI_-_bart-large-text-summarizer-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/Azma-AI_-_bart-large-text-summarizer-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/Azma-AI_-_bart-large-text-summarizer-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/Azma-AI_-_bart-large-text-summarizer-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/Azma-AI_-_bart-large-text-summarizer-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Azma-AI_-_bart-large-text-summarizer-8bits
```

Azma-AI_-_bart-large-text-summarizer-8bits

File size: 304 Bytes

195f7c4

{
  "_from_model_config": true,
  "bos_token_id": 0,
  "decoder_start_token_id": 2,
  "early_stopping": true,
  "eos_token_id": 2,
  "forced_eos_token_id": 2,
  "max_length": 62,
  "min_length": 11,
  "no_repeat_ngram_size": 3,
  "num_beams": 6,
  "pad_token_id": 1,
  "transformers_version": "4.40.2"
}