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mossez-systems
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Model Details
Model Provider
mossez-systems
Model Tree
mossez-systems/Mossez-100M-Base
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Parameters | 100,098,048 |
| Architecture | Llama-compatible decoder-only Transformer |
| Layers / hidden size | 12 / 768 |
| Query / KV heads | 12 / 4 |
| Context length | 1,024 tokens |
| Vocabulary | 32,007 |
| Weight format | Safetensors, FP32 |
| License | Apache-2.0 |
The tokenizer extends the Mossez-100M-Base vocabulary with seven single-token chat/FIM markers.
Existing token IDs were not changed. The FIM markers are <|fim_prefix|> (32004),
<|fim_middle|> (32005), and <|fim_suffix|> (32006).
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "mossez-systems/Mossez-100M-Coder-Base"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")prompt = "def fibonacci(n: int) -> list[int]:"inputs = tokenizer(prompt, return_tensors="pt").to(model.device)output = model.generate(**inputs, do_sample=False, max_new_tokens=96)print(tokenizer.decode(output[0], skip_special_tokens=True))
For fill-in-the-middle, render the prompt as
<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>.
The model consumed 39,997,440 tokens in 9,765 finite optimizer steps without corpus wraparound. Packed validation loss decreased monotonically from 2.572834 to 1.488147. See TRAINING_REPORT.md, EVALUATION.md, and DATASET_ATTRIBUTION.md.
The released model.safetensors SHA-256 is
aba529bf10ad9f3acb5294c8bc2b4c93d20d25c6cff3a235a8659503b9ac1837.
This small research model is not production-ready. It can emit malformed or insecure code, wrong constants, hallucinated APIs, repetition, and early EOS. Its 1,024-token context is short, and the evaluation suite is narrow. Validate, test, and sandbox every output. Do not use generated code without review.