Instructions to use alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline with Transformers:

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

pipe = pipeline("text-generation", model="alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline")
model = AutoModelForCausalLM.from_pretrained("alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline

SGLang

How to use alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline 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 "alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline" \
    --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": "alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline",
		"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 "alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline" \
        --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": "alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline with Docker Model Runner:
```
docker model run hf.co/alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Bloom-1b7-dialogsum-IT

This model is a instruction-tuned version of bigscience/bloom-1b7 on a dialog summation dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Instruction Tuned on the dialog summation task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/dialogsum/train

Training procedure

Given a set of prompts:

prompts = [
    "Provide a concise summary for the following dialogue:",
    "Summarize this conversation in a few sentences:",
    "Here is a dialogue. Can you summarize it briefly?",
    "Read the following dialogue and write a short summary:",
    "Condense the essence of this conversation into a summary:"
]

Each example is concatenated with the prompt, the dialogue, and the summary as so:

    concatenated_texts = [
        random.choice(prompts) + " " + dialogue + "<\s>" + " Summary:" + summary
        for dialogue, summary in zip(examples['dialogue'], examples['summary'])
    ]

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 8
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: 10
mixed_precision_training: Native AMP

Training results

Final epoch results: {'loss': 0.0137, 'grad_norm': 0.6599154472351074, 'learning_rate': 7.000000000000001e-07, 'epoch': 10.0}

Average results: {'train_runtime': 1142.1524, 'train_samples_per_second': 1.751, 'train_steps_per_second': 0.438, 'train_loss': 0.37129621666669843, 'epoch': 10.0}

Framework versions

Transformers 4.38.1
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

Downloads last month: 2

Safetensors

Model size

2B params

Tensor type

F32

Model tree for alonzogarbanzo/Bloom-1b7-dialogsum-IT-baseline

Base model

bigscience/bloom-1b7

Finetuned

(12)

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Quantizations

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