Instructions to use pszemraj/pythia-31m-simplewiki-scratch-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/pythia-31m-simplewiki-scratch-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/pythia-31m-simplewiki-scratch-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-31m-simplewiki-scratch-bf16")
model = AutoModelForCausalLM.from_pretrained("pszemraj/pythia-31m-simplewiki-scratch-bf16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pszemraj/pythia-31m-simplewiki-scratch-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/pythia-31m-simplewiki-scratch-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/pythia-31m-simplewiki-scratch-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/pythia-31m-simplewiki-scratch-bf16

SGLang

How to use pszemraj/pythia-31m-simplewiki-scratch-bf16 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/pythia-31m-simplewiki-scratch-bf16" \
    --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/pythia-31m-simplewiki-scratch-bf16",
		"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/pythia-31m-simplewiki-scratch-bf16" \
        --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/pythia-31m-simplewiki-scratch-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/pythia-31m-simplewiki-scratch-bf16 with Docker Model Runner:
```
docker model run hf.co/pszemraj/pythia-31m-simplewiki-scratch-bf16
```

pythia-31m-simplewiki-scratch-bf16

Trained from random initialized config based on EleutherAI/pythia-31m, 3 epochs bf16 It achieves the following results on the evaluation set:

Loss: 4.1763
Accuracy: 0.3676

Model description

tuned with bf16 (previous was fp32)

Intended uses & limitations

More information needed

Training and evaluation data

***** eval metrics *****                                              
  epoch                   =       2.99                   
  eval_accuracy           =     0.3723                                  eval_loss               =     4.1155                                
  eval_runtime            = 0:00:14.44                                
  eval_samples            =        500                                  eval_samples_per_second =     34.602                                  eval_steps_per_second   =     17.301                              
  perplexity              =    61.2811

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 2
eval_batch_size: 2
seed: 80085
gradient_accumulation_steps: 64
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
lr_scheduler_type: inverse_sqrt
lr_scheduler_warmup_ratio: 0.05
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.8617	0.45	100	5.5276	0.2451
5.2782	0.9	200	4.9596	0.2965
4.9996	1.35	300	4.6412	0.3310
4.6292	1.8	400	4.4344	0.3485
4.5339	2.25	500	4.2875	0.3600
4.5214	2.7	600	4.1763	0.3676

Framework versions

Transformers 4.33.1
Pytorch 2.2.0.dev20230907+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	24.63
ARC (25-shot)	22.78
HellaSwag (10-shot)	25.61
MMLU (5-shot)	23.12
TruthfulQA (0-shot)	49.65
Winogrande (5-shot)	50.51
GSM8K (5-shot)	0.0
DROP (3-shot)	0.72

Downloads last month: 53

Safetensors

Model size

30.5M params

Tensor type

F32

pszemraj
/

pythia-31m-simplewiki-scratch-bf16