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tarpous
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tarpous
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The model is intended for research, education, systems experiments, conversion tests, and local inference demonstrations. It is a pretrained base model. It has not been aligned for assistant use and should not be treated as a reliable chatbot, factual reference, or safety-critical system.
sample-10BT, immutable revision
87f09149ef4734204d70ed1d046ddc9ca3f2b8f9The tokenizer, shard manifest, model configuration, checkpoint, evaluation, and converted package are connected by recorded hashes. The native Transformers export has an exact maximum fp32 logit error of 0.0 against the project checkpoint in the recorded parity evaluation.
model.safetensors stores float32 tensors and is 498,738,888 bytes. That
precision is deliberate: it is what makes the exact logit-parity check above
meaningful, because the released file is bit-identical in value to the project
checkpoint rather than a re-quantized copy of it. Load with
dtype=torch.bfloat16 for inference, or take one of the GGUF files below if a
smaller download matters more than parity.
Evaluation uses fixed, non-wrapping FineWeb-Edu windows.
| Split | Mean loss | Perplexity |
|---|---|---|
| Validation | 3.324985 | 27.799 |
| Test | 3.400198 | 29.970 |
These measurements describe next-token likelihood under this tokenizer and window protocol. They are not instruction-following, factuality, or safety scores.
The figures/ directory also carries the 20M architecture study, the dense
ablation screening, and the local judge calibration from the wider project.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "tarpous/zero-125m"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto",) inputs = tokenizer("Once upon a time,", return_tensors="pt").to(model.device)with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=64, do_sample=False, ) print(tokenizer.decode(output[0], skip_special_tokens=True))This is a base model, so continuations may be repetitive, abrupt, or inaccurate. A prompt formatted as a conversation does not turn it into an instruction-following model.
| File | Size | SHA-256 |
|---|---|---|
zero-125m-f16.gguf | 250,567,744 bytes | 4863f80ea315fc0713da86750f71cddc8dfbec09c0d3f8a853cee650220027ab |
zero-125m-q8_0.gguf | 133,708,864 bytes | 401866c0a19e803eea19b00e056915a37b05ebe1146430b618355071680b8692 |
The Q8_0 artifact produced 76.5 decoded tokens per second in the recorded bounded CUDA llama.cpp smoke. Throughput is hardware and build dependent. Conversion details and the pre-tokenizer compatibility patch are documented in the project repository.
The project also trained a 20M dense FineWeb model and separate TinyStories models near 20M parameters using dense attention, Mamba-2, and masked discrete diffusion. It includes dense ablations, supervised fine-tuning, DPO, and local judge calibration. Those results describe the research project and are not additional training stages of this released base checkpoint.
The full report is available in this model repository as
lm-from-zero-technical-report.pdf and in the GitHub release.
Users are responsible for evaluating the model in their own setting and for adding safeguards appropriate to any application.
bibtex
@software{tarpous2026lmfromzero, author = {tarpous}, title = {lm-from-zero: A Local-First, Reproducible Language-Model Pipeline from Tokenization to Serving}, year = {2026}, url = {https://github.com/tarpous/lm-from-zero}}The model weights, tokenizer, GGUF files, and code are released under Apache-2.0. The technical report and its figures are released under CC BY 4.0.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "tarpous/zero-125m"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto",) inputs = tokenizer("Once upon a time,", return_tensors="pt").to(model.device)with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=64, do_sample=False, ) print(tokenizer.decode(output[0], skip_special_tokens=True))@software{tarpous2026lmfromzero, author = {tarpous}, title = {lm-from-zero: A Local-First, Reproducible Language-Model Pipeline from Tokenization to Serving}, year = {2026}, url = {https://github.com/tarpous/lm-from-zero}}