Instructions to use Defalt-404/GPT-6B_Tuned_small_pile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Defalt-404/GPT-6B_Tuned_small_pile with Transformers:

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

pipe = pipeline("text-generation", model="Defalt-404/GPT-6B_Tuned_small_pile")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Defalt-404/GPT-6B_Tuned_small_pile")
model = AutoModelForCausalLM.from_pretrained("Defalt-404/GPT-6B_Tuned_small_pile")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Defalt-404/GPT-6B_Tuned_small_pile with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Defalt-404/GPT-6B_Tuned_small_pile"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Defalt-404/GPT-6B_Tuned_small_pile",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Defalt-404/GPT-6B_Tuned_small_pile

SGLang

How to use Defalt-404/GPT-6B_Tuned_small_pile 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 "Defalt-404/GPT-6B_Tuned_small_pile" \
    --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": "Defalt-404/GPT-6B_Tuned_small_pile",
		"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 "Defalt-404/GPT-6B_Tuned_small_pile" \
        --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": "Defalt-404/GPT-6B_Tuned_small_pile",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Defalt-404/GPT-6B_Tuned_small_pile with Docker Model Runner:
```
docker model run hf.co/Defalt-404/GPT-6B_Tuned_small_pile
```

Model Card for Model ID

GPT-6B_Tuned_small_pile is a GPT-j-6B model trained on 0.1 million example of pile dataset.

n_embd: 4096, n_layer: 28, n_positions: 2048

Tuning Parameters:

val_split_percent: 20,

momentum: 0.9 

train_batch_size (eff) : 32

train_micro_batch: 16

gradient_accumulation_steps: 2

gradient_clipping: 0.5

learning_rate: 0.00001

weight_decay: 0.01

lr_schedular: cosine

lr_warmup_steps: 1000

lr_decay: 0.1

lr_decay_step: 2000

mixed_precision: bf16

## Model Details

Model Description

Developed by: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model: EleutherAI/gpt-j-6b

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Defalt-404
/

GPT-6B_Tuned_small_pile

Model Card for Model ID

Model Description

Dataset used to train Defalt-404/GPT-6B_Tuned_small_pile