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Model: NickupAI/LFM2.5-1.2B-Saiga-It-v3-GGUF
Source: Original Platform

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README.md

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+---
+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.