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-medium-v006b/results.json
ADDED
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| 1 |
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{
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| 2 |
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"experiment_name": "bb8-char-medium-v006b",
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| 3 |
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"config": {
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| 5 |
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"generated_samples": {
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| 152 |
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"greedy": "ROMEO:\nThe that the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the\nthat the the the the the the the the the the the the the the the\nt",
|
| 153 |
+
"temperature": "ROMEO:\nThis sen raucks\nWit morhe he goos sond cand, be nowse that the us light badauns,\nThim: seelesh hear the heifer.\n\nPENIUS:\nTheace.\n\nCAULLIO:\nLe you thou notw not I your, ase bandy fort brarie\nWhis spri",
|
| 154 |
+
"top_k": "ROMEO:\nNor to thou is rube,\nThe to hand is my gone prie I hean the comel bat tearlave\nBas gasof the knound, wom ay at live thee pa scaclince\nAll thou wor of mirt beste\nAnd not to sur the is to sear fry aur ",
|
| 155 |
+
"top_p": "ROMEO:\nBy you thall this be that the alle deake allind fraly\nHerevel me thingh to no sis thatht athould of to iste,\nI is in whis ourd destring send.\n\nPOLAR:\nWhat o by cond the on was apour alt thaver'd.\nMEN"
|
| 156 |
+
}
|
| 157 |
+
}
|
outputs/bb8-char-medium-v006b/run.json
ADDED
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@@ -0,0 +1,80 @@
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| 1 |
+
{
|
| 2 |
+
"run_id": "bb8-char-medium-v006b",
|
| 3 |
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"started_at": "2026-09-27T20:51:16.241359+00:00",
|
| 4 |
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"completed_at": "2026-09-27T20:54:35.125897+00:00",
|
| 5 |
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|
| 6 |
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"git_commit": "5e22799e0b14096994bfe5b2f4432344ab31911b",
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| 9 |
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"config_sha256": "a1cc2d98fab2db2606502f418be3a9a07f302737be8e20c361be972b3ee90f42",
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| 10 |
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"experiment": {
|
| 11 |
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"objective": "character_language_model_depth_ablation",
|
| 12 |
+
"status": "completed",
|
| 13 |
+
"baseline": "bb8-char-medium-v006",
|
| 14 |
+
"change": "num_layers 4->6, keeping d_model=128"
|
| 15 |
+
},
|
| 16 |
+
"dataset": {
|
| 17 |
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"path": "data/tiny_shakespeare.txt",
|
| 18 |
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"sha256": "86c4e6aa9db7c042ec79f339dcb96d42b0075e16b8fc2e86bf0ca57e2dc565ed",
|
| 19 |
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| 20 |
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| 21 |
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},
|
| 22 |
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"tokenizer": {
|
| 23 |
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"type": "char",
|
| 24 |
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"vocab_size": 69
|
| 25 |
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},
|
| 26 |
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"model": {
|
| 27 |
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"parameters": 1229184,
|
| 28 |
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"vocab_size": 69,
|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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"d_ff": 512,
|
| 33 |
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|
| 34 |
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"dropout": 0.1,
|
| 35 |
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"activation": "gelu",
|
| 36 |
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"tie_weights": true
|
| 37 |
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},
|
| 38 |
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"training": {
|
| 39 |
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"batch_size": 32,
|
| 40 |
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|
| 41 |
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"lr": 0.0003,
|
| 42 |
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| 43 |
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|
| 44 |
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"warmup_steps": 100,
|
| 45 |
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"mixed_precision": true,
|
| 46 |
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"log_interval": 20,
|
| 47 |
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"eval_interval": 200,
|
| 48 |
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"save_interval": 1000
|
| 49 |
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},
|
| 50 |
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"runtime": {
|
| 51 |
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"device": "cuda",
|
| 52 |
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"pytorch": "2.13.0+cu130",
|
| 53 |
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"cuda": "13.0",
|
| 54 |
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"gpu": "NVIDIA GeForce RTX 4070 Laptop GPU",
|
| 55 |
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"gpu_memory_gb": 8.0
|
| 56 |
+
},
|
| 57 |
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"evaluation": {
|
| 58 |
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"train": {
|
| 59 |
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"loss": 2.0324661191628905,
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| 60 |
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"perplexity": 7.63288677556304,
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| 61 |
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"accuracy": 0.39493582589285714,
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"bits_per_token": 2.932228790891139,
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"bits_per_char": 2.932228790891139
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| 64 |
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},
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| 65 |
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"val": {
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| 66 |
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"perplexity": 8.240158031217348,
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"accuracy": 0.37666576867816093,
|
| 69 |
+
"bits_per_token": 3.0426720059360832,
|
| 70 |
+
"bits_per_char": 3.0426720059360832
|
| 71 |
+
}
|
| 72 |
+
},
|
| 73 |
+
"artifacts": {
|
| 74 |
+
"checkpoint": "checkpoints/bb8-char-medium-v006b/final_model.pt",
|
| 75 |
+
"tokenizer": "checkpoints/bb8-char-medium-v006b/tokenizer.json",
|
| 76 |
+
"config": "checkpoints/bb8-char-medium-v006b/config.yaml",
|
| 77 |
+
"results": "outputs/bb8-char-medium-v006b/results.json",
|
| 78 |
+
"output_dir": "outputs\\bb8-char-medium-v006b"
|
| 79 |
+
}
|
| 80 |
+
}
|