Instructions to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with Ollama:
```
ollama run hf.co/NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M
```

Lemonade

How to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Saiga-It-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LFM2.5-1.2B-Saiga-It-v2

My first LLM — an attempt to make a Russian-language Saiga out of LFM2.5 1.2B by Liquid AI.

Inspired by the amazing work of Ilya Gusev and the Saiga project.

What is this?

LFM2.5 1.2B is a compact but powerful model from Liquid AI with very dense knowledge packing. I tried to teach it Russian through CPT + SFT pipeline.

The result is... interesting.

Training

Stage 1 — Continued Pre-Training (CPT):

wikimedia/wikipedia (Russian, ~350k articles)
uonlp/CulturaX (Russian, 300k)
allenai/c4 (Russian 200k + English 300k for retention)

Stage 2 — Supervised Fine-Tuning (SFT):

IlyaGusev/saiga_scored (opus_score ≥ 8, ~27k examples)
d0rj/alpaca-cleaned-ru (15k examples)
IlyaGusev/ru_sharegpt_cleaned (243 examples, but what examples)
IlyaGusev/ru_turbo_saiga
lksy/ru_instruct_gpt4

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "NickupAI/LFM2.5-1.2B-Saiga-It-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "NickupAI/LFM2.5-1.2B-Saiga-It-v2",
    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=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Honest Warning ⚠️

The model hallucinates badly. It may invent a planet called Gamma-Tit, introduce itself as Yandex or Irina, or confuse dwarf planets with kvass. This is not a bug — this is a feature.

Recommended for:

Creative nonsense generation (lol)
Experiments and research
Inspiration and laughs

Not recommended for:

Factual questions
Medicine, law or any serious topics
Astronomy (especially dwarf planets)

Example outputs

— Назови карликовые планеты солнечной системы

Марс, 2. Юпитер, 3. Сатурн, 4. Квас, 5. Гамма-тит...

Iconic.

What's next

Working on v3 — full Russian Wikipedia + Habr CPT, better SFT mix.

GGUF Versions

Quantized versions of the model.

Downloads last month: 66

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Finetuned

NickupAI/LFM2.5-1.2B-Saiga-It-v2

Quantized

(3)

this model

NickupAI
/

LFM2.5-1.2B-Saiga-It-v2-GGUF

LFM2.5-1.2B-Saiga-It-v2

What is this?

Training

Usage

Honest Warning ⚠️

Example outputs

What's next

GGUF Versions

Model tree for NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF

Datasets used to train NickupAI/LFM2.5-1.2B-Saiga-It-v2-GGUF