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 / training-history /r003.md

dawidmajewski

Release Pollock 1.4 (r006) (#1)

9d9f729 5 days ago

preview code

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r003 - Pollock 1.1 training record

Identity and provenance

Field	Value
Revision / release	`r003` / Pollock 1.1
Model ID	`SlayerLab/pollock-mini-lm-125m`
Published weights	`698984b1d1b96c9b6ffaff00c7fc2e78e140e842`
Source run	`runs/r003-lr4e-4-v4`
Source checkpoint	`checkpoints/ckpt.pt`
Checkpoint iteration / SHA-256	22,004 / `912483b44bb177c7a62fe19a53fc6db7b333cf881c57de15daf9dd7209d90a6b`
`model.safetensors` SHA-256	`3a9964a0ba5f6d20ef6061d2a7e4442c1b001209ebe80f7b7148a25573decaa9`
Benchmark adapter SHA-256	`0f53aa6b8454e856f8c4c489c7e6cbf207b8e124fe06bbb239315c017fe16fc0`
nanoGPT commit	`3adf61e154c3fe3fca428ad6bc3818b27a3b8291` (dirty workspace recorded)
Training environment	DMPod GPT on RunPod
Run status	completed, exit code 0

The native checkpoint reports iter_num=22004. The wrapper's pre-run estimate in config.json used 22,003 target updates and is one effective batch lower because the legacy nanoGPT loop is inclusive. This record follows the native checkpoint and r002 release convention: 22,004 updates and 10,815,406,080 token presentations.

Architecture and parameter accounting

Setting	Value
Type	GPT-2-style decoder-only Transformer
Layers / heads / width	12 / 14 / 896
Head dimension / MLP width	64 / 3,584
Context / vocabulary	1,024 / 12,288
Activation / dropout / bias	GELU / 0.0 / false
Position representation	learned absolute embeddings
Input/output token weights	tied
nanoGPT reported count	126,637,952
Native unique trainable total	127,555,456

The architecture and parameter counts are unchanged from r002. The Transformers artifact has 127,674,624 unique serialized parameters, including 119,168 zero-valued compatibility bias parameters.

Tokenizer and dataset

Training used Minimal EN Corpus 2.5B.

Split	BPE tokens	Binary SHA-256
Train	2,703,768,815	`5396834c25664080c14793a4880f86b86ae94074a6036f8f54a58686b02d30dc`
Validation	5,267,742	`bf07bd1fb1f956a92ee7788f2a8110e1837f5b880ce432f6cd2a22d05f8ac135`

Dataset and tokenizer identities match r002.

Complete training configuration

Setting	Value
Initialization	scratch
Final checkpoint iteration	22,004
Sequence / micro-batch per GPU	1,024 / 12
Global / per-GPU accumulation	40 / 20 micro-steps
DDP world size / effective batch	2 / 491,520 tokens
Token presentations / passes	10,815,406,080 / approximately 4.0
Optimizer / betas	fused AdamW / 0.9, 0.95
Maximum / minimum LR	4e-4 / 4e-5
Schedule / warmup / decay	cosine / 440 / 22,003 iterations
Weight decay / grad clip	0.1 / 1.0
Evaluation interval / batches	1,000 / 100 per split
Evaluation subset	fixed 1,228,800 tokens per split
Training / data / evaluation seeds	1,337 / 1,337 / 4,242
Precision / compilation / backend	BF16 / `torch.compile` / NCCL
Hardware	2x NVIDIA L40S
PyTorch / CUDA	2.8.0+cu128 / 12.8
Runtime	approximately 11 h 40 min

The controlled change from r002 was the maximum/minimum learning rate. Hardware and the evaluation sampling implementation also differed operationally.

Training-time validation

Loss estimates used fixed offset files containing 1,200 sequence starts per split. They are internally comparable within r003 but not directly comparable with r002's randomly sampled batches or r004's differently seeded offsets.

Step	Train loss	Validation loss
0	9.5797	9.5732
1,000	3.3896	3.4346
2,000	3.0458	3.0779
3,000	2.8829	2.9362
4,000	2.8210	2.8694
5,000	2.7688	2.8231
6,000	2.6991	2.7486
7,000	2.6873	2.7129
8,000	2.6536	2.6828
9,000	2.6451	2.6794
10,000	2.5838	2.6674
11,000	2.5810	2.6154
12,000	2.5351	2.6060
13,000	2.5606	2.5888
14,000	2.5332	2.5935
15,000	2.5207	2.5801
16,000	2.4850	2.5726
17,000	2.4773	2.5945
18,000	2.4851	2.5548
19,000	2.4698	2.5561
20,000	2.4437	2.5362
21,000	2.4602	2.5417
22,000	2.4282	2.5529

The final recorded value is 2.5528755188 with perplexity 12.8439838496. The released source checkpoint is the final checkpoint, not the step-20,000 sampled minimum.

English zero-shot benchmarks

Tasks used complete splits with lm-evaluation-harness 0.4.12, zero few-shot examples, batch size 8, BF16, and 1,024 maximum context. The adapter recorded 80 requests truncated to the model context.

Benchmark	Primary	Additional	Samples	Delta vs r002
BLiMP	acc 0.7773880597	-	67,000	+0.010418
LAMBADA OpenAI	acc 0.2899281972	perplexity 47.7011402302	5,153	+0.011838 acc
HellaSwag	acc_norm 0.3034256124	acc 0.2850029875	10,042	+0.004680
PIQA	acc_norm 0.6109902067	acc 0.6147986942	1,838	+0.007617
SciQ	acc_norm 0.6590000000	acc 0.7660000000	1,000	+0.001000
ARC-Easy	acc_norm 0.4280303030	acc 0.4861111111	2,376	+0.005051
ARC-Challenge	acc_norm 0.2534129693	acc 0.2201365188	1,172	+0.011945

All seven primary scores improved over r002. LAMBADA perplexity decreased by 5.966382. Because the r002 artifact reported zero truncated requests, the cross-revision comparison is informative but not a byte-for-byte identical evaluation protocol.

Structured English results: ../benchmarks/english.json.

Fixed inference reference

The shared fixed-sampling-v1 suite loads r003 from its immutable published commit, verifies the weight hash above, and records the exact output token IDs for the same four prompts used across r001-r006.

Release preparation status

Native training checkpoint verified against the backup archive.
Benchmark adapter hash verified against the benchmark result record.
Dataset, tokenizer, source snapshots, and fixed evaluation offsets are present.
Transformers 5.15.1 conversion is complete; the [2, 64] deterministic parity probe produced a maximum absolute logit error of 0.0, and artifact hashes are recorded in release_manifest.json.
Published and remotely verified at 698984b1d1b96c9b6ffaff00c7fc2e78e140e842.

Licensing and limitations

This is an English, unaligned base model, not an instruction-following assistant. It may hallucinate, generate harmful or biased content, and reproduce training patterns. It is unsuitable for high-risk or production decisions without independent safeguards. The dataset combines sources with different terms; see the dataset card and the model repository's LICENSE.md.