Instructions to use SlayerLab/pollock-mini-lm-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

pollock-mini-lm-125m / CHANGELOG.md

dawidmajewski

Release Pollock 1.0 (r002)

5e571cd verified 2 days ago

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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

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