Instructions to use andreaskoepf/pythia-1.4b-gpt4all-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andreaskoepf/pythia-1.4b-gpt4all-pretrain with Transformers:

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

pipe = pipeline("text-generation", model="andreaskoepf/pythia-1.4b-gpt4all-pretrain")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andreaskoepf/pythia-1.4b-gpt4all-pretrain")
model = AutoModelForCausalLM.from_pretrained("andreaskoepf/pythia-1.4b-gpt4all-pretrain")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use andreaskoepf/pythia-1.4b-gpt4all-pretrain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreaskoepf/pythia-1.4b-gpt4all-pretrain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andreaskoepf/pythia-1.4b-gpt4all-pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andreaskoepf/pythia-1.4b-gpt4all-pretrain

SGLang

How to use andreaskoepf/pythia-1.4b-gpt4all-pretrain 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 "andreaskoepf/pythia-1.4b-gpt4all-pretrain" \
    --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": "andreaskoepf/pythia-1.4b-gpt4all-pretrain",
		"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 "andreaskoepf/pythia-1.4b-gpt4all-pretrain" \
        --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": "andreaskoepf/pythia-1.4b-gpt4all-pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use andreaskoepf/pythia-1.4b-gpt4all-pretrain with Docker Model Runner:
```
docker model run hf.co/andreaskoepf/pythia-1.4b-gpt4all-pretrain
```

wandb: https://wandb.ai/open-assistant/supervised-finetuning/runs/t2adm3wu

checkpoint: 11000 step (2 epochs)

datasets:

pretrain:
  weight_decay: 0.01
  use_custom_sampler: true
  sort_by_length: false
  datasets:
    - joke
    - webgpt:
        val_split: 0.1
    - gpt4all:
        val_split: 0.01
    - alpaca:
        val_split: 0.025
    - code_alpaca:
        val_split: 0.05
    - minimath
    - humaneval_mbpp_codegen_qa
    - humaneval_mbpp_testgen_qa
    - grade_school_math_instructions
    - recipes
    - cmu_wiki_qa
    - oa_wiki_qa_bart_10000row
    - prosocial_dialogue:
        fraction: 0.1
    - explain_prosocial:
        fraction: 0.05

pythia:

pythia-1.4b-pretrain:
  dtype: fp16
  learning_rate: 6e-6
  model_name: EleutherAI/pythia-1.4b-deduped
  deepspeed_config: configs/zero_config_pretrain.json
  weight_decay: 0.0
  max_length: 2048
  use_flash_attention: true
  warmup_steps: 50
  gradient_checkpointing: false
  gradient_accumulation_steps: 1
  per_device_train_batch_size: 16
  per_device_eval_batch_size: 16
  num_train_epochs: 2 
  save_total_limit: 2

command: deepspeed trainer_sft.py --configs defaults pretrain pythia-1.4b-pretrain --cache_dir .cache/ --output_dir .saved_models/pythia-1.4b-pre --residual_dropout 0.0 --deepspeed

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