Instructions to use Mandotosh/NedoLM-0.8B-SFT-GGUF 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 Mandotosh/NedoLM-0.8B-SFT-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 Mandotosh/NedoLM-0.8B-SFT-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Mandotosh/NedoLM-0.8B-SFT-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
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 Mandotosh/NedoLM-0.8B-SFT-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
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 Mandotosh/NedoLM-0.8B-SFT-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
Use Docker
docker model run hf.co/Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use Mandotosh/NedoLM-0.8B-SFT-GGUF with Ollama:
ollama run hf.co/Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
- Unsloth Desktop
- Docker Model Runner
How to use Mandotosh/NedoLM-0.8B-SFT-GGUF with Docker Model Runner:
docker model run hf.co/Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
- Lemonade
How to use Mandotosh/NedoLM-0.8B-SFT-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Mandotosh/NedoLM-0.8B-SFT-GGUF:F16
Run and chat with the model
lemonade run user.NedoLM-0.8B-SFT-GGUF-F16
List all available models
lemonade list
- Atomic Chat
NedoLM-0.8B-SFT-GGUF
Public GGUF snapshot of NedoLM 0.8B after Turkish full-parameter SFT.
- SFT checkpoint:
step_00005300.pt - Base pretraining checkpoint:
/arf/scratch/egitimg16/nedolm_0p8b/runs/nedolm-0p8b-v1/checkpoints/step_00064000.pt - Parameters: 823,076,352
- Context length: 4096
- Attention: 12 heads / 4 KV heads, head dim 128
- Sliding window: 2048
- FFN width: 5632
- MorphFFN layers: 18 / 24
- GGUF storage: F16
- GGUF SHA256:
9c830384e5434035e77c55ae40c5940b386f42156b63c7d4132af5b2ec986aa1 - NedoTokenizer vocab SHA256:
72412d981dac65a29d1767bc98821fc2bcffc2de53c534e7c719598515bfb600
Important runtime note
This file uses the custom GGUF architecture name nedolm. NedoLM contains token-prior MorphFFN routing (OTHER/ROOT/SUFFIX channels). Stock llama.cpp does not understand this architecture unless a NedoLM/MorphFFN runtime implementation is added. The GGUF is a valid v3 container and preserves the model tensors and architecture metadata; surface-vocab.bin is included for the exact tokenizer used by training.
SFT data
The SFT loader trains only on assistant targets and excludes conversations containing tool-role messages. During the corpus audit, 3,753 tool-role documents were detected and filtered from the readable training corpus.
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