Text Generation
Transformers
Safetensors
English
pollock
causal-lm
gpt2
nanogpt
swiglu
bpe
educational
base-model
custom_code
Eval Results (legacy)
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", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SlayerLab/pollock-mini-lm-125m", trust_remote_code=True, 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
Commit History
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eval: niezalezna ewaluacja r003(v1.1) vs r006 (Arkadiusz Slota) - pliki, bez zmian karty
Verified signatureZero-shot lm-eval 0.4.12, obie rewizje na jednym harnessie. Karta/README nietkniete. Szczegoly w evaluations/independent-eval-slota-2026-08-30.mdb9abe7b -
Release Pollock 1.4 (r006) (#1)
- Release Pollock 1.4 (r006) (a2e53d9f1b690ebfbea375cf8bd3b5bdf69dece9) - Finalize Pollock 1.4 release metadata (b9abe2fbda7b21e1a78f4f826b99590a1b407e2d)9d9f729
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Finalize Pollock 1.3 release metadata
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Release Pollock 1.3 (r005)
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Add fixed cross-revision inference samples
Verified signaturePublish deterministic r001-r004 inference references, reproduction tooling, and updated model-card and training-history links.8bda8c7 -
Release Pollock 1.2 (r004)
Verified signaturePublish the verified r004 Transformers artifact, tokenizer, English benchmark results, model card, changelog, training history, and release manifest.30feb81 -
Release Pollock 1.1 (r003)
Verified signaturePublish the verified r003 Transformers artifact, tokenizer, English benchmark results, model card, changelog, training history, and release manifest.698984b
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Release Pollock 1.0 (r002)
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Popraw opis projektu i tokeny specjalne
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Publikacja pollock-mini-lm
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initial commit
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