Instructions to use Nasaawakening/Zoder1.0-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Nasaawakening/Zoder1.0-1B 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 Nasaawakening/Zoder1.0-1B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nasaawakening/Zoder1.0-1B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Nasaawakening/Zoder1.0-1B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nasaawakening/Zoder1.0-1B: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 Nasaawakening/Zoder1.0-1B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Nasaawakening/Zoder1.0-1B: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 Nasaawakening/Zoder1.0-1B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Nasaawakening/Zoder1.0-1B:Q4_K_M
Use Docker
docker model run hf.co/Nasaawakening/Zoder1.0-1B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Nasaawakening/Zoder1.0-1B with Ollama:
ollama run hf.co/Nasaawakening/Zoder1.0-1B:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Nasaawakening/Zoder1.0-1B with Docker Model Runner:
docker model run hf.co/Nasaawakening/Zoder1.0-1B:Q4_K_M
- Lemonade
How to use Nasaawakening/Zoder1.0-1B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Nasaawakening/Zoder1.0-1B:Q4_K_M
Run and chat with the model
lemonade run user.Zoder1.0-1B-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload Full float16 model files
Browse files
README.md
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---
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tags:
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- minicpm
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- zoder
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- gguf
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- ollama
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created_by: Komandan Nasa
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---
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# 🤖 Zoder 1.0-1B
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## Features
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- ✅ GGUF Q4_K_M quantized version (~700MB)
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- ✅ Gradio Web UI included
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- ✅ Ollama compatible with Modelfile
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- ✅ Optimized for Termux deployment
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- ✅ 100% benchmark pass rate (8/8 tests)
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## Merge Details
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| Parameter | Value |
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|-----------|-------|
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| Base Model A | openbmb/MiniCPM5-1B |
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| Base Model B | GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking |
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| Method | SLERP |
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| T-value (self_attn) | [0, 0.5, 0.3, 0.7, 1] |
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| T-value (mlp) | [1, 0.5, 0.7, 0.3, 0] |
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| T-value (default) | 0.5 |
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| Dtype | float16 |
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| Layers | 24 |
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## Files
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| File | Size | Description |
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| `model.safetensors` | ~2GB | Full float16 model |
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| `Zoder1.0-1B-Q4_K_M.gguf` | ~700MB | Quantized for mobile/Termux/Ollama |
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| `zoder_gradio.py` | ~3KB | Web UI script |
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| `Modelfile` | ~1KB | Ollama configuration |
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| `install_zoder_ollama.sh` | ~2KB | Termux installer script |
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## Quick Start with Ollama
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```bash
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# Pull from HuggingFace (after creating model locally)
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ollama create zoder -f Modelfile
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# Or pull directly (if published to Ollama library)
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ollama run zoder
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```
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## Usage
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### Transformers (Full Model)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Nasaawakening/Zoder1.0-1B", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("Nasaawakening/Zoder1.0-1B", trust_remote_code=True, torch_dtype="float16", device_map="auto")
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messages = [
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{"role": "system", "content": "You are Zoder, a helpful AI assistant."},
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{"role": "user", "content": "Hello!"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### GGUF (llama.cpp / Termux)
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```bash
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huggingface-cli download Nasaawakening/Zoder1.0-1B Zoder1.0-1B-Q4_K_M.gguf --local-dir ~/zoder
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llama-server -m ~/zoder/Zoder1.0-1B-Q4_K_M.gguf --port 8080
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```
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### Gradio Web UI
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```bash
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pip install gradio transformers torch
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python zoder_gradio.py
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```
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### Termux Ollama Deployment
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```bash
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# Download and run installer
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bash install_zoder_ollama.sh
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# Run Zoder
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proot-distro login ubuntu -- ollama run zoder
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```
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## Benchmark Results
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| Test | Status |
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|------|--------|
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| Basic Conversation | ✅ PASS |
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| Logical Reasoning | ✅ PASS |
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| Code Generation (Python) | ✅ PASS |
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| Creative Writing | ✅ PASS |
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| Instruction Following | ✅ PASS |
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| Multi-turn Memory | ✅ PASS |
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| Math Problem | ✅ PASS |
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| Termux Knowledge | ✅ PASS |
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| **Pass Rate** | **100% (8/8)** |
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## Built With ❤️ in Kediri, Indonesia
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Created by Komandan Nasa from Bakso Bangi Pak Romdani.
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---
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base_model:
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- openbmb/MiniCPM5-1B
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- GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# zoder-merged
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
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### Models Merged
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The following models were included in the merge:
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* [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)
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* [GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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slices:
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- sources:
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- model: openbmb/MiniCPM5-1B
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layer_range: [0, 24]
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- model: GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
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layer_range: [0, 24]
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merge_method: slerp
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base_model: openbmb/MiniCPM5-1B
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: float16
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```
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