Instructions to use OpenAssistant/pythia-12b-pre-v8-12.5k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenAssistant/pythia-12b-pre-v8-12.5k-steps with Transformers:

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

pipe = pipeline("text-generation", model="OpenAssistant/pythia-12b-pre-v8-12.5k-steps")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/pythia-12b-pre-v8-12.5k-steps")
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/pythia-12b-pre-v8-12.5k-steps")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenAssistant/pythia-12b-pre-v8-12.5k-steps with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenAssistant/pythia-12b-pre-v8-12.5k-steps"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenAssistant/pythia-12b-pre-v8-12.5k-steps",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenAssistant/pythia-12b-pre-v8-12.5k-steps

SGLang

How to use OpenAssistant/pythia-12b-pre-v8-12.5k-steps 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 "OpenAssistant/pythia-12b-pre-v8-12.5k-steps" \
    --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": "OpenAssistant/pythia-12b-pre-v8-12.5k-steps",
		"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 "OpenAssistant/pythia-12b-pre-v8-12.5k-steps" \
        --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": "OpenAssistant/pythia-12b-pre-v8-12.5k-steps",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenAssistant/pythia-12b-pre-v8-12.5k-steps with Docker Model Runner:
```
docker model run hf.co/OpenAssistant/pythia-12b-pre-v8-12.5k-steps
```

Note: internal model, not ready for use

This is an intermediate model used as base-model for further pythia 12b SFT-8 experiments. It was trained on a wider set of instruction-tuning datasets for >12.5k steps with batch-size 128 and a context size of 2048. The gpt4all dataset had "as a language model" contamination (>1.8k entries). We added filtering later, but this model (pre-v8) was trained on the raw unfildered gpt4all dataset.

Datasets:

pretrain:
  num_train_epochs: 1
  weight_decay: 0.0
  use_custom_sampler: true
  sort_by_length: false
  datasets:
    - gpteacher_roleplay:
        val_split: 0.05
    - red_pajama:
        fraction: 0.25
        max_val_set: 1000
    - wizardlm_70k:
        val_split: 0.05
        max_val_set: 500
    - joke:
        val_split: 0.05
    - poem_instructions:
        val_split: 0.025
    - oa_stackexchange:
        val_split: 0.05
        fraction: 0.1
        max_val_set: 1000
    - tell_a_joke:
        val_split: 0.05
        max_val_set: 250
    - webgpt:
        val_split: 0.05
        max_val_set: 250
    - gpt4all:
        val_split: 0.01
        max_val_set: 1000
    - alpaca_gpt4:
        val_split: 0.025
        max_val_set: 250
    - code_alpaca:
        val_split: 0.05
        max_val_set: 250
    - vicuna:
        max_val_set: 250
    - oig_file:
        source_url: https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl
        max_count: 10000
        min_length: 250
        val_split: 0.05
        max_val_set: 250
    - minimath:
        val_split: 0.05
    - humaneval_mbpp_codegen_qa:
        val_split: 0.05
    - humaneval_mbpp_testgen_qa:
        val_split: 0.05
    - grade_school_math_instructions:
        val_split: 0.05
    - recipes:
        val_split: 0.05
    - cmu_wiki_qa:
        val_split: 0.05
 - oa_wiki_qa_bart_10000row:
        val_split: 0.05
        max_val_set: 250
    - prosocial_dialogue:
        fraction: 0.1
        max_val_set: 250
    - explain_prosocial:
        fraction: 0.075
        max_val_set: 250
    - soda:
        fraction: 0.25
        max_val_set: 1000
    - oa_leet10k:
        val_split: 0.05
        max_val_set: 250
    - dolly15k:
        val_split: 0.05
        max_val_set: 300

Pythia:

pythia-12b-pretrain:
  dtype: fp16
  log_dir: "pythia_log_12b"
  learning_rate: 6e-6
  model_name: EleutherAI/pythia-12b-deduped
  output_dir: pythia_model_12b
  weight_decay: 0.0
  max_length: 2048
  warmup_steps: 100
  gradient_checkpointing: true
  gradient_accumulation_steps: 4
  per_device_train_batch_size: 4
  per_device_eval_batch_size: 4
  eval_steps: 251
  save_steps: 500
  num_train_epochs: 1
  save_total_limit: 2
  deepspeed_config: configs/zero_config_pretrain.json

Command used: deepspeed trainer_sft.py --show_dataset_stats --configs defaults pythia-12b-pretrain pretrain --cache_dir .cache/ --output_dir .saved/pythia-12b-super-pretrain2 --deepspeed

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