LFM2.5-1.2B-Saiga-It-v3-GGUF/README.md

---
language:
- ru
- en
license: other
base_model: NickupAI/LFM2.5-1.2B-Saiga-It-v3
tags:
- russian
- saiga
- continued-pretraining
- sft
- lfm
- liquid-ai
datasets:
- IlyaGusev/saiga_scored
- d0rj/alpaca-cleaned-ru
- IlyaGusev/ru_sharegpt_cleaned
- IlyaGusev/ru_turbo_saiga
- lksy/ru_instruct_gpt4
- wikimedia/wikipedia
- uonlp/CulturaX
- allenai/c4
- IlyaGusev/habr
---

# LFM2.5-1.2B-Saiga-It-v3

Continued attempt to make a Russian-language Saiga out of [LFM2.5 1.2B](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) by Liquid AI. This is v3 — a significant upgrade over [v2](https://huggingface.co/NickupAI/LFM2.5-1.2B-Saiga-It-v2) with much more CPT data.

Inspired by the amazing work of [Ilya Gusev](https://huggingface.co/IlyaGusev) and the [Saiga project](https://huggingface.co/IlyaGusev/saiga_llama3_8b).

## What changed vs v2?

**CPT dataset grew from ~1.25M to ~2.69M examples:**
- Full Russian Wikipedia (~1.9M articles, up from 350k)
- Added Habr (~295k technical articles in Russian)
- Replaced c4 ru with CulturaX ru (200k)
- Kept English c4 (300k) for retention

**SFT dataset expanded:**
- Added `IlyaGusev/ru_turbo_saiga`
- Added `lksy/ru_instruct_gpt4`

## Training

**Stage 1 — Continued Pre-Training (CPT):**
- `wikimedia/wikipedia` (Russian, ~1.9M articles)
- `IlyaGusev/habr` (Russian, ~295k technical articles)
- `uonlp/CulturaX` (Russian, 200k)
- `allenai/c4` (English, 300k for retention)
- 10,000 steps, lr=3e-5, loss 2.26 → 2.13

**Stage 2 — Supervised Fine-Tuning (SFT):**
- `IlyaGusev/saiga_scored` (opus_score ≥ 8, ~27k)
- `d0rj/alpaca-cleaned-ru` (15k)
- `IlyaGusev/ru_sharegpt_cleaned`
- `IlyaGusev/ru_turbo_saiga`
- `lksy/ru_instruct_gpt4`
- 2 epochs, lr=5e-6, final loss ~1.21

## Usage

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

model = AutoModelForCausalLM.from_pretrained(
    "NickupAI/LFM2.5-1.2B-Saiga-It-v3",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "NickupAI/LFM2.5-1.2B-Saiga-It-v3",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Привет! Как дела?"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.1,
    top_k=50,
    top_p=0.1,
    repetition_penalty=1.05,
    do_sample=True,
)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Honest Warning ⚠️

Still hallucinates. Training a model of this size properly requires significantly more compute
and time than was available for this project — the CPT data barely scratches the surface of
what would be needed for reliable factual knowledge. The model knows the *format* of answers
but sometimes invents the content.

**Recommended for:**
- Creative nonsense generation, lol
- Experiments and research
- Russian language generation tasks

**Not recommended for:**
- Factual questions
- Medicine, law or any serious topics
- Astronomy (the dwarf planet situation has not improved)

> — Назови все карликовые планеты Солнечной системы
>
> 1. Марс, 2. Венус, 3. Земля, 4. Юпитер... 8. Эритрея

v2 had Kvass and Gamma-Tit. v3 replaced them with Eritrea. Progress.

## GGUF versions

Quantized versions available at [NickupAI/LFM2.5-1.2B-Saiga-It-v3-GGUF](https://huggingface.co/NickupAI/LFM2.5-1.2B-Saiga-It-v3-GGUF).

## What's next

LoRA experiments on larger base models (7B+). The 1.2B size has fundamental limitations for factual knowledge — time to scale up.


## GGUF Versions

Quantized versions of the model.