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mossez-systems
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Model Details
Model Provider
mossez-systems
Model Tree
mossez-systems/Mossez-100M-Coder-Instruct
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 |
| Objective | Assistant-only balanced calibration SFT |
| Weight format | Safetensors, FP32 |
| License | Apache-2.0 |
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "mossez-systems/Mossez-100M-Nexus"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")messages = [{"role": "user", "content": "Write a Python function and briefly explain it."}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)output = model.generate(**inputs, do_sample=False, max_new_tokens=128)new_tokens = output[0, inputs.input_ids.shape[1]:]print(tokenizer.decode(new_tokens, skip_special_tokens=True))
The calibration corpus contains 5,280 train, 660 validation, and 660 test examples, evenly divided between conversation and coding. The selected checkpoint completed 1,320 optimizer steps and saw every train example exactly once.
On the small immutable project-authored suites, Nexus achieved conversation loss
2.564707 and coding loss 0.027539.
Normalized endpoint retention was 102.0% for conversation
and 100.2% for coding. These are narrow internal measurements,
not a claim of broad benchmark or production quality.
See TRAINING_REPORT.md, EVALUATION.md, and DATASET_ATTRIBUTION.md.
The released model.safetensors SHA-256 is a0ecfd229b238ee4d07019252f3f07385c1a3a9b02e67d17b5eace2a51f9d0bd.
This 100M-parameter research model is not a reliable, safe, or production-ready assistant. It can hallucinate, repeat, mistranslate, mishandle refusal requests, emit insecure code, and return incorrect constants or APIs. The calibration data is narrow and template-heavy. Validate facts, test and sandbox code, and do not use the model as a security or safety classifier.