---
license: apache-2.0
language:
  - ru
  - en
library_name: transformers
pipeline_tag: text-generation
base_model: mossez-systems/Mossez-100M-Base
datasets:
  - OpenAssistant/oasst2
  - HuggingFaceTB/smol-smoltalk
tags:
  - causal-lm
  - instruct
  - llama
  - gqa
  - russian
  - english
  - research
  - mossez
---

# Mossez-100M-Instruct

**Mossez-100M-Instruct** is an experimental 100M-parameter Russian–English
instruction-tuning research model derived from the final CPT-trained
Mossez-100M-Base.

This is a compact research artifact. It is **not** a strong, reliable, safe,
or production-ready assistant. Its purpose is to study the behavior and
post-training limits of the Mossez-100M family; stronger practical behavior is
expected from later, larger families.

## Model details

| Property | Value |
|---|---:|
| Parameters | 100,098,048 |
| Architecture | Llama-compatible decoder-only Transformer |
| Layers | 12 |
| Hidden size | 768 |
| Query / KV heads | 12 / 4 |
| Intermediate size | 2,048 |
| Context length | 1,024 tokens |
| Vocabulary | 32,007 |
| Tokenizer | Mossez Tokenizer v1.1, byte-level BPE |
| Embeddings | Tied input/output embeddings |
| Primary languages | Russian, English |
| Weight format | Safetensors, clean FP32 master |

Tokenizer v1.1 preserves every original ID from 0 through 31,999 and adds
seven role/FIM tokens at IDs 32,000–32,006. Its `tokenizer.json` SHA-256 is
`e9551d84b9947f741763bf815a2d5f6bfcc47a3b67c73fcbf386223e8ed969be`.

## Intended use

The model is intended for compact-model research, local experiments,
instruction-tuning analysis, reproducibility work, and testing inference
pipelines. It is not intended for factual authority, autonomous action,
safety-critical use, cybersecurity advice, or high-stakes decisions.

## Usage

Install recent versions of `torch`, `transformers`, `safetensors`, and
`jinja2`. The repository contains a clean FP32 master; Transformers can load it
directly at FP16 runtime dtype on CUDA.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mossez-systems/Mossez-100M-Instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=dtype)
model.to(device).eval()

messages = [{"role": "user", "content": "Кратко объясни, что такое RoPE."}]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=96,
        do_sample=False,
        eos_token_id=[tokenizer.eos_token_id, 32003],
        pad_token_id=tokenizer.pad_token_id,
    )

new_tokens = output[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
```

Keep the total prompt plus generated length within 1,024 tokens. The model may
fail to emit `<|end|>` or EOS, so always set a finite `max_new_tokens` limit.

## Training lineage

The selected weights follow this exact path:

1. final CPT-derived Mossez-100M-Base;
2. vocabulary resize from 32,000 to 32,007 with the seven new rows initialized
   to the mean of the original embedding rows, without changing old rows;
3. mixed assistant-only SFT v3 through checkpoint step 555;
4. concise assistant-only calibration v4 for 216 optimizer steps.

The selected lineage exposed the model to 9,973,153 non-padding tokens and
6,030,590 supervised assistant tokens after resize. v4 used FP16 autocast,
fused AdamW, gradient checkpointing, micro-batch 8, accumulation 2, learning
rate `1e-5`, and seed 3407. System/user text, role prefixes, BOS, and padding
were excluded from loss; assistant content, assistant termination, and final
EOS were supervised.

OASST1 v1/v2 and first-turn v5 were completed comparison pilots but are not in
the selected weight lineage. See `TRAINING_REPORT.md` and `EVALUATION.md`.

## Evaluation and selection

Checkpoint selection combined teacher-forced validation/test loss with a fixed
31-case RU/EN diagnostic suite and manual review. v4 step 216 was selected by
relative performance among the bounded v1–v5 experiments, not because it met a
product-quality threshold.

| Metric | v4 step 216 |
|---|---:|
| v4 validation loss | 1.434212 |
| v4 test loss | 1.551534 |
| Automatic diagnostic passes | 2 / 31 |
| Structural completions | 17 / 31 |
| Unterminated outputs | 13 / 31 |
| Repetition failures | 5 / 31 |
| Generated role-marker leakage | 0 / 31 |

The clean FP32 export was independently reloaded and verified with 100,098,048
parameters, tied embeddings, finite weights, and finite logits in a real CUDA
forward pass. A separate FP16 validation export also passed, but is not
duplicated in this repository.

## Limitations

- Instruction following is weak in both Russian and English.
- Factual questions, arithmetic, JSON extraction, classification, translation,
  and exact formatting frequently fail.
- Responses may hallucinate, repeat, terminate early, or fail to terminate.
- Harmful-request refusal tests failed; the model must not be treated as a
  safety layer.
- The model may continue in an irrelevant style or produce superficially fluent
  but incorrect text.
- v3 included a `smol-contraints` subset later found to contain some prompt-to-
  answer leakage. It was removed from v4, but its influence may remain in the
  selected weights.
- Public conversational datasets may contain residual bias, errors, or
  undesirable material despite filtering.
- The context window is only 1,024 tokens and the model has no current-world
  knowledge guarantee.

Do not use this model for medical, legal, financial, security, or other
high-stakes decisions.

## Data and attribution

Selected-lineage SFT data was derived from pinned, filtered subsets of
OpenAssistant OASST2 and HuggingFaceTB SmolTalk. The Base model retains its own
FineWeb/FineWeb2/Wikipedia training history. Source datasets are not
redistributed here. See `DATASET_ATTRIBUTION.md` and `NOTICE.md` for pinned
revisions, licenses, and caveats.

## License

Model weights and original repository materials are released under Apache-2.0.
Third-party datasets and source content remain under their respective licenses
and terms. See `LICENSE` and `NOTICE.md`.