Instructions to use jtatman/pythia-delphi-micromachine-funnel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jtatman/pythia-delphi-micromachine-funnel with Transformers:

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

pipe = pipeline("text-generation", model="jtatman/pythia-delphi-micromachine-funnel")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jtatman/pythia-delphi-micromachine-funnel")
model = AutoModelForCausalLM.from_pretrained("jtatman/pythia-delphi-micromachine-funnel")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jtatman/pythia-delphi-micromachine-funnel with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jtatman/pythia-delphi-micromachine-funnel

SGLang

How to use jtatman/pythia-delphi-micromachine-funnel 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 "jtatman/pythia-delphi-micromachine-funnel" \
    --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": "jtatman/pythia-delphi-micromachine-funnel",
		"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 "jtatman/pythia-delphi-micromachine-funnel" \
        --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": "jtatman/pythia-delphi-micromachine-funnel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jtatman/pythia-delphi-micromachine-funnel with Docker Model Runner:
```
docker model run hf.co/jtatman/pythia-delphi-micromachine-funnel
```

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Tatman Electric
Funded by [optional]: Spare Pocket Lint
Shared by [optional]: TRL
Model type: Sliced Layered
Language(s) (NLP): Mixed
License: Pythia @ EleutherAI
Finetuned from model [optional]: EleutherAI/pythia-2.8b-deduped

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Before there were merged models, there were slices of shards of... stuff. Those slices have meaning. Those slices are real slices too.

Direct Use

Part of a series of slice and dice mods.

Single Hidden Layer Pythia

What does a single hidden layer preserve from a 12 layer base model?

[More Information Needed]

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Groups	Version	Filter	n-shot	Metric	Value		Stderr
Open LLM Leaderboard	N/A	none	5	rouge1_max	36.3550	±	0.9462
		flexible-extract	5	exact_match	0.0220	±	0.0066
- arc_challenge	1	none	25	acc	0.1760	±	0.0170
		none	25	acc_norm	0.2320	±	0.0189
- gsm8k	3	strict-match	5	exact_match	0.0060	±	0.0035
		flexible-extract	5	exact_match	0.0220	±	0.0066
- hellaswag	1	none	10	acc	0.3520	±	0.0214
		none	10	acc_norm	0.4040	±	0.0220
- winogrande	1	none	5	acc	0.5120	±	0.0224
		none	5	bleu_diff	-0.6500	±	0.6421
		none	5	rouge1_acc	0.3700	±	0.0216
		none	5	rouge1_diff	-1.5564	±	1.0223
		none	5	acc	0.2664	±	0.0036
		none	5	rougeL_max	33.8798	±	0.9367
		none	5	rouge2_diff	-3.3178	±	0.9477
		none	5	bleu_max	15.2292	±	0.6714
		none	5	bleu_acc	0.4360	±	0.0222
		none	5	rouge2_max	16.4873	±	1.0172
		none	5	acc_norm	0.3180	±	0.0145
		strict-match	5	exact_match	0.0060	±	0.0035
		none	5	rougeL_diff	-0.7765	±	1.0034
		none	5	rougeL_acc	0.3860	±	0.0218
		none	5	rouge2_acc	0.1920	±	0.0176
- mmlu	N/A	none	0	acc	0.2533	±	0.0039
- humanities	N/A	none	5	acc	0.2408	±	0.0075
- other	N/A	none	5	acc	0.2443	±	0.0080
- social_sciences	N/A	none	5	acc	0.2538	±	0.0081
- stem	N/A	none	5	acc	0.2740	±	0.0079
- truthfulqa	N/A	none	0	rouge1_max	36.3550	±	0.9462
		none	0	bleu_diff	-0.6500	±	0.6421
		none	0	rouge1_acc	0.3700	±	0.0216
		none	0	rouge1_diff	-1.5564	±	1.0223
		none	0	acc	0.3435	±	0.0137
		none	0	rougeL_max	33.8798	±	0.9367
		none	0	bleu_max	15.2292	±	0.6714
		none	0	bleu_acc	0.4360	±	0.0222
		none	0	rouge2_max	16.4873	±	1.0172
		none	0	rougeL_acc	0.3860	±	0.0218
		none	0	rougeL_diff	-0.7765	±	1.0034
		none	0	rouge2_acc	0.1920	±	0.0176
		none	0	rouge2_diff	-3.3178	±	0.9477

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: OldAsDirt
Hours used: 5
Cloud Provider: YourMomsBasement
Compute Region: Siberia
Carbon Emitted: 8ppm

No yaks were harmed in the making of this model.

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Safetensors

Model size

0.7B params

Tensor type

F32

Paper for jtatman/pythia-delphi-micromachine-funnel

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 47