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
Release Pollock 1.4 (r006)
#1
by dawidmajewski - opened
Summary
- Publishes Pollock 1.4 / r006 from the completed
r006-corrected-corpus-v2-s1337-lr0.0004run. - Returns to the corrected V2
SlayerLab/minimal-en-corpus-2.5bdata and the r003-r004 tokenizer while retaining the 12/14/896 architecture. - Updates the bilingual model card, changelog, licensing notice, structured benchmarks, training history, and fixed cross-revision samples.
Validation
- Final native checkpoint SHA-256:
580fbb3c039ea1f349285a9bf7a67731fb6d455de931b6b7601d4ed09fce9437 model.safetensorsSHA-256:8e74aa4d34464229e86a1831fb34d442f91e22aa600e0cf2503891957fdc6a43- Transformers 5.15.1 conversion parity: maximum absolute logit error
0.0on the deterministic[2, 64]probe - Local Transformers load, parameter count, special-token IDs, and forward pass verified
- Seven full zero-shot benchmark splits recorded with no truncated requests
fixed-sampling-v1replayed exactly for r001-r006 against immutable weights commita2e53d9f1b690ebfbea375cf8bd3b5bdf69dece9- All manifest artifact hashes verified; the inherited avatar hash was checked from the remote PR revision
Release state
Merge and version tag v1.4 remain pending owner review.
dawidmajewski changed pull request status to merged
dawidmajewski deleted the
refs/pr/1 ref