Text Generation
Transformers
Safetensors
PyTorch
English
language-model
from-scratch
lora
educational
gpt
decoder-only
Instructions to use Skywalker1910/BB8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skywalker1910/BB8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Skywalker1910/BB8")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Skywalker1910/BB8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Skywalker1910/BB8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Skywalker1910/BB8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywalker1910/BB8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Skywalker1910/BB8
- SGLang
How to use Skywalker1910/BB8 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 "Skywalker1910/BB8" \ --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": "Skywalker1910/BB8", "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 "Skywalker1910/BB8" \ --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": "Skywalker1910/BB8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Skywalker1910/BB8 with Docker Model Runner:
docker model run hf.co/Skywalker1910/BB8
Upload folder using huggingface_hub
Browse files
outputs/bb8-char-small-v002/results.json
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{
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"experiment_name": "bb8-char-small-v002",
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"config": {
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"dropout": 0.1,
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"activation": "gelu",
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"tie_weights": true
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},
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"tokenizer": {
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"type": "char",
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"vocab_size": null
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},
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"training": {
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"batch_size": 64,
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"num_epochs": 5,
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"lr": 0.001,
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"weight_decay": 0.1,
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"max_grad_norm": 1.0,
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"warmup_steps": 50,
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"mixed_precision": false,
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"eval_interval": 0,
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"save_interval": 5000
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},
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"data": {
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"stride": 1,
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"val_fraction": 0.1
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},
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"generation": {
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"max_new_tokens": 100,
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"temperature": 0.8,
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"top_k": 40,
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"top_p": 0.9,
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"repetition_penalty": 1.1
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}
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},
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"tokenizer": {
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"type": "char",
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"vocab_size": 69
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},
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"model_params": 112320,
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"model_config": {
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"vocab_size": 69,
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"activation": "gelu",
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},
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"training_history": {
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"train_loss": [
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],
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"val_loss": [
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],
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"perplexity": [
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],
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"lr": [
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0.0001
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]
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},
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| 89 |
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"evaluation": {
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| 90 |
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"train": {
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| 91 |
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"loss": 1.5084002385491857,
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"perplexity": 4.5194948942914515,
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| 93 |
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"accuracy": 0.5404959213275322,
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"bits_per_char": 2.1761615438306396
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},
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| 96 |
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"val": {
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| 97 |
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"loss": 1.6863457313698642,
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"perplexity": 5.399712607765923,
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"accuracy": 0.4991981479777762,
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"bits_per_char": 2.432882623871575
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}
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},
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"generated_samples": {
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"greedy": "ROMEO:\nThe shall the come the shall be the shall the prince the shall the shall the shall the some the sha",
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"temperature": "ROMEO:\nHo die.\n\nKING RICHARD III:\nI go that and my brothers, it parts fed!\n\nPRINCE:\nWho, but where's that ",
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| 106 |
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"top_k": "ROMEO:\nMy do live not breads a maving.\n\nDUKE VINCENTIO:\nGrace, not within the suffed that this riffence\nAn",
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| 107 |
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"top_p": "ROMEO:\nThere but done.\n\nROMEO:\nWhat, shall proming, and they have to be better,\nAnd make that and to heart"
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}
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}
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outputs/bb8-char-small-v002/run.json
ADDED
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| 1 |
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{
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| 2 |
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"run_id": "bb8-char-small-v002",
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| 3 |
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"started_at": "2026-09-25T01:50:29.960997+00:00",
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"completed_at": "2026-09-25T02:11:54.360979+00:00",
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"duration_seconds": 1284.4,
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"git_dirty": true,
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"dataset": {
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"path": "data\\tiny_shakespeare.txt",
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},
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| 16 |
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},
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"model": {
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| 27 |
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| 28 |
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},
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"training": {
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| 30 |
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"batch_size": 64,
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| 32 |
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| 33 |
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| 34 |
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"mixed_precision": false,
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| 37 |
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| 39 |
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"save_interval": 5000
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| 40 |
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},
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| 41 |
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"runtime": {
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| 42 |
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"device": "cuda",
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| 43 |
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"pytorch": "2.13.0+cu130",
|
| 44 |
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"cuda": "13.0",
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| 45 |
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"gpu": "NVIDIA GeForce RTX 4070 Laptop GPU",
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| 46 |
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"gpu_memory_gb": 8.0
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| 47 |
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},
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| 48 |
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"evaluation": {
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| 49 |
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"train": {
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| 50 |
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"loss": 1.5084002385491857,
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"perplexity": 4.5194948942914515,
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"accuracy": 0.5404959213275322,
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| 53 |
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"bits_per_char": 2.1761615438306396
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| 54 |
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},
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| 55 |
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"val": {
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| 56 |
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"perplexity": 5.399712607765923,
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"accuracy": 0.4991981479777762,
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| 59 |
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"bits_per_char": 2.432882623871575
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| 60 |
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}
|
| 61 |
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},
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| 62 |
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"artifacts": {
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| 63 |
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"checkpoint": "checkpoints/bb8-char-small-v002/final_model.pt",
|
| 64 |
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"tokenizer": "checkpoints/bb8-char-small-v002/tokenizer.json",
|
| 65 |
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"config": "checkpoints/bb8-char-small-v002/config.yaml",
|
| 66 |
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"results": "outputs/bb8-char-small-v002/results.json",
|
| 67 |
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"output_dir": "outputs\\bb8-char-small-v002"
|
| 68 |
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}
|
| 69 |
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}
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