Text Generation
Transformers
Safetensors
English
gpt2
causal-lm
nanogpt
bpe
educational
base-model
Eval Results (legacy)
text-generation-inference
Instructions to use SlayerLab/pollock-mini-lm-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SlayerLab/pollock-mini-lm-125m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SlayerLab/pollock-mini-lm-125m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SlayerLab/pollock-mini-lm-125m") model = AutoModelForCausalLM.from_pretrained("SlayerLab/pollock-mini-lm-125m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SlayerLab/pollock-mini-lm-125m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SlayerLab/pollock-mini-lm-125m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SlayerLab/pollock-mini-lm-125m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SlayerLab/pollock-mini-lm-125m
- SGLang
How to use SlayerLab/pollock-mini-lm-125m 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 "SlayerLab/pollock-mini-lm-125m" \ --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": "SlayerLab/pollock-mini-lm-125m", "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 "SlayerLab/pollock-mini-lm-125m" \ --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": "SlayerLab/pollock-mini-lm-125m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SlayerLab/pollock-mini-lm-125m with Docker Model Runner:
docker model run hf.co/SlayerLab/pollock-mini-lm-125m
| # Changelog | |
| Revision numbers identify published model states independently of release names. | |
| ## r002 — Pollock 1.0 | |
| - Expanded the architecture from 12/12/768 to 12/14/896 while remaining below 128M total parameters. | |
| - Reduced context from 2,048 to 1,024 tokens and increased the effective batch from 262,144 to 491,520 tokens. | |
| - Changed the learning-rate range from 6e-4→6e-5 to 3e-4→3e-5 and warmup from 500 to 440 iterations. | |
| - Trained with BF16 on 2× RTX 4090 instead of 1× RTX 5090. | |
| - Introduced sampled training validation and a seven-task English zero-shot benchmark suite. | |
| Full record: [`training-history/r002.md`](./training-history/r002.md) | |
| ## r001 — experimental predecessor | |
| - Initial experimental release with a 12/12/768 architecture, 2,048-token context, and 95.96M nanoGPT-reported parameters. | |
| - Trained for four dataset passes and selected by deterministic full-validation reevaluation of retained checkpoints. | |
| - No downstream benchmark suite was run. | |
| Full record and generation samples: [`training-history/r001.md`](./training-history/r001.md) | |