Instructions to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/flan-t5-xl-summary-map-reduce-1024")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/flan-t5-xl-summary-map-reduce-1024")
model = AutoModelForMultimodalLM.from_pretrained("pszemraj/flan-t5-xl-summary-map-reduce-1024")

llama-cpp-python

How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pszemraj/flan-t5-xl-summary-map-reduce-1024",
	filename="flan-t5-xl-summary-map-reduce-1024-q5_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0
# Run inference directly in the terminal:
llama-cli -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0
# Run inference directly in the terminal:
llama-cli -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0
# Run inference directly in the terminal:
./llama-cli -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0

Use Docker

docker model run hf.co/pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0

LM Studio
Jan

vLLM

How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/flan-t5-xl-summary-map-reduce-1024"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/flan-t5-xl-summary-map-reduce-1024",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0

SGLang

How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 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 "pszemraj/flan-t5-xl-summary-map-reduce-1024" \
    --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": "pszemraj/flan-t5-xl-summary-map-reduce-1024",
		"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 "pszemraj/flan-t5-xl-summary-map-reduce-1024" \
        --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": "pszemraj/flan-t5-xl-summary-map-reduce-1024",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with Ollama:
```
ollama run hf.co/pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0
```

Unsloth Studio

How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pszemraj/flan-t5-xl-summary-map-reduce-1024 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pszemraj/flan-t5-xl-summary-map-reduce-1024 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pszemraj/flan-t5-xl-summary-map-reduce-1024 to start chatting

Atomic Chat new
Docker Model Runner
How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with Docker Model Runner:
```
docker model run hf.co/pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0
```

Lemonade

How to use pszemraj/flan-t5-xl-summary-map-reduce-1024 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pszemraj/flan-t5-xl-summary-map-reduce-1024:Q5_0

Run and chat with the model

lemonade run user.flan-t5-xl-summary-map-reduce-1024-Q5_0

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

flan-t5-xl-summary-map-reduce-1024

A larger t2t model trained to complete the "reduce" step (consolidation step) of map-reduce summarization.

About

Refer to this wiki page or the smaller BART model card for explanations and usage examples.

Comparatively, this model seems to

produce more eloquent final reduced summaries
more "gullible"/sensitive to noise in the input summaries
- i.e. a hallucinated one-off term/name/entity is likely to be mentioned/appear in the reduced summary
agnostic to whitespace in input (by definition, since the t5 tokenizer normalizes whitespace)

Therefore, it's recommended to compare sample outputs of this model and the BART version on your data to see which is better for your use case.

Details

This model is a fine-tuned version of google/flan-t5-xl on the pszemraj/summary-map-reduce-v1 dataset at 1024 context length in/out.

It achieves the following results on the evaluation set:

Loss: 0.6039
Num Input Tokens Seen: 7138765

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 8e-05
train_batch_size: 2
eval_batch_size: 2
seed: 17868
gradient_accumulation_steps: 32
total_train_batch_size: 64
optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 2.0

Downloads last month: 19

Safetensors

Model size

3B params

Tensor type

F32

Model tree for pszemraj/flan-t5-xl-summary-map-reduce-1024

Base model

google/flan-t5-xl

Quantized

(3)

this model

pszemraj
/

flan-t5-xl-summary-map-reduce-1024

flan-t5-xl-summary-map-reduce-1024

About

Details

Training procedure

Training hyperparameters

Model tree for pszemraj/flan-t5-xl-summary-map-reduce-1024

Dataset used to train pszemraj/flan-t5-xl-summary-map-reduce-1024