Instructions to use MityaLabs/nafanya-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MityaLabs/nafanya-0.8b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B")
model = PeftModel.from_pretrained(base_model, "MityaLabs/nafanya-0.8b")

MLX

How to use MityaLabs/nafanya-0.8b with MLX:

# Download the model from the Hub
pip install huggingface_hub[hf_xet]

hf download MityaLabs/nafanya-0.8b --local-dir nafanya-0.8b

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MityaLabs/nafanya-0.8b 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 MityaLabs/nafanya-0.8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MityaLabs/nafanya-0.8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MityaLabs/nafanya-0.8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MityaLabs/nafanya-0.8b:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf MityaLabs/nafanya-0.8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf MityaLabs/nafanya-0.8b:Q4_K_M

Use Docker

docker model run hf.co/MityaLabs/nafanya-0.8b:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use MityaLabs/nafanya-0.8b with Ollama:
```
ollama run hf.co/MityaLabs/nafanya-0.8b:Q4_K_M
```
Unsloth Desktop

How to use MityaLabs/nafanya-0.8b with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MityaLabs/nafanya-0.8b"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MityaLabs/nafanya-0.8b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MityaLabs/nafanya-0.8b with Docker Model Runner:
```
docker model run hf.co/MityaLabs/nafanya-0.8b:Q4_K_M
```

Lemonade

How to use MityaLabs/nafanya-0.8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MityaLabs/nafanya-0.8b:Q4_K_M

Run and chat with the model

lemonade run user.nafanya-0.8b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use MityaLabs/nafanya-0.8b with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MityaLabs/nafanya-0.8b"

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 MityaLabs/nafanya-0.8b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MityaLabs/nafanya-0.8b with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MityaLabs/nafanya-0.8b"

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 "MityaLabs/nafanya-0.8b" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

nafanya-0.8b

The memory-first variant of nafanya, a Russian tool-calling model for the offline kuzya agent on a phone (Poco F1, skladenOS). Fine-tuned from Qwen/Qwen3.5-0.8B; runs as a 505 MB GGUF Q4_K_M through llama-server.

What it is

Base: Qwen/Qwen3.5-0.8B (Apache-2.0).
Training: MLX-LM BF16 LoRA, 2 last layers, rank 16, scale 16, dropout 0.05, 75 iterations (the run was stopped by the macOS GPU watchdog), batch 1, max sequence 1280.
Data: 831 synthetic Russian requests and refusals (v5 + targeted v7), generated by DeepSeek V4.1 Flash (AI-generated), filtered against the agent's frozen test sets.
Same compact prompt and 13 tools as nafanya-1.7b.

Evaluation

temperature 0, compact prompt, GGUF. The model alone is weak on fresh phrasings; the agent's deterministic rules layer is what makes the combination usable.

Set	Model alone	With kuzya rules
held-out v4 (fresh)	25/40 (62%), args 22/40	39/40 (98%), args 39/40
held-out v3	—	38/40 (95%)
held-out v2	—	40/40 (100%)
first 30 frozen queries, phone	—	26/30 (87%)

Phone (Poco F1): peak RAM 0.92 GB, load 2 s, warm energy 4.1 J per query with rules, 0.6 s per query.

Do not attribute the rules-assisted numbers to the weights: the pre-routing, refusals and argument repair live in the kuzya agent.

Limitations

Weaker than nafanya-1.7b on free-form requests that the rules do not cover.
On its own it often fails negatives and out-of-scope requests.
Same semantic weak spots as the 1.7B: relative volume/brightness, mute/nosleep inversion, service-log arguments.

Data and licence

Weights: Apache-2.0.
Training data is synthetic, generated by DeepSeek V4.1 Flash via its API under the DeepSeek Open Platform Terms of Service (release 2026-04-22, §4.2(3)). It is AI-generated; no phone data, no personal data.
DeepSeek is mentioned only as the data source; no affiliation or endorsement.

Model tree for MityaLabs/nafanya-0.8b

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

(736)

this model

MityaLabs
/

nafanya-0.8b

nafanya-0.8b

What it is

Evaluation

Limitations

Data and licence

Links

Model tree for MityaLabs/nafanya-0.8b