Instructions to use mossez-systems/Mossez-100M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mossez-systems/Mossez-100M-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mossez-systems/Mossez-100M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mossez-systems/Mossez-100M-Instruct")
model = AutoModelForCausalLM.from_pretrained("mossez-systems/Mossez-100M-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mossez-systems/Mossez-100M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mossez-systems/Mossez-100M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mossez-systems/Mossez-100M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mossez-systems/Mossez-100M-Instruct

SGLang

How to use mossez-systems/Mossez-100M-Instruct 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 "mossez-systems/Mossez-100M-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mossez-systems/Mossez-100M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "mossez-systems/Mossez-100M-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mossez-systems/Mossez-100M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mossez-systems/Mossez-100M-Instruct with Docker Model Runner:
```
docker model run hf.co/mossez-systems/Mossez-100M-Instruct
```

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.

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:

final CPT-derived Mossez-100M-Base;
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;
mixed assistant-only SFT v3 through checkpoint step 555;
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.