Text Generation
Transformers
Safetensors
gpt2
tiny
tiny-lm
small-language-model
subword
bpe
from-scratch
tinystories
charlm
7m-params
text-generation-inference
Instructions to use Compactbot/subword-gpt-7m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Compactbot/subword-gpt-7m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Compactbot/subword-gpt-7m")# Load model directly from transformers import AutoTokenizer, GPT tokenizer = AutoTokenizer.from_pretrained("Compactbot/subword-gpt-7m") model = GPT.from_pretrained("Compactbot/subword-gpt-7m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Compactbot/subword-gpt-7m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Compactbot/subword-gpt-7m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Compactbot/subword-gpt-7m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Compactbot/subword-gpt-7m
- SGLang
How to use Compactbot/subword-gpt-7m 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 "Compactbot/subword-gpt-7m" \ --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": "Compactbot/subword-gpt-7m", "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 "Compactbot/subword-gpt-7m" \ --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": "Compactbot/subword-gpt-7m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Compactbot/subword-gpt-7m with Docker Model Runner:
docker model run hf.co/Compactbot/subword-gpt-7m
v2: 4000-step checkpoint (held-out ppl 55.5, was 268.6) Replaces the 3175-step model with the full 4000-step run of the same architecture/data/schedule. Verified end-to-end: this safetensors artifact reproduces val_loss 4.0163 / held-out ppl 55.50 when loaded back (39 tensors, bf16, 6,950,144 params, tied embeddings). See updated README for the new numbers and the export/eval harness.
#3
by Compactbot - opened
No description provided.
Closing: the binary upload's post-upload verification flagged a hash mismatch (new file 13,903,744 B vs the 13,903,712 B previously on main), so I cannot confirm this PR carries the intended v2 weights. The v2 model itself is trained and verified (held-out ppl 55.50, recorded in the eval store), and the export/eval harness is saved — a clean re-upload is pending a working binary-PR path. Reopening or merging this PR should be verified against the safetensors header (39 tensors, bf16, 6,950,144 params, metadata step=4000) first.
Compactbot changed pull request status to closed