mondk/Msh-Tiny-47M-GGUF overview
msh tiny GGUF A tiny ~47M parameter GPT 2 style chat model, trained completely from scratch — no pretrained base model, custom BPE tokenizer trained from zero,…
Runs locally from ~48.9 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | mondk/Msh-Tiny-47M-GGUF |
|---|---|
| Author | mondk |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | mondk/Msh-Tiny-47M |
| Last modified | 2026-08-25T14:01:44.000Z |
Model README
---
license: apache-2.0
language:
- en
base_model:
- mondk/Msh-Tiny-47M
pipeline_tag: text-generation
tags:
- gguf
- from-scratch
- msh
- f16
- q2k
- q4km
- ty
datasets:
- mondk/Greetings-hi-for-train-Msh-v2
- tatsu-lab/alpaca
- databricks/databricks-dolly-15k
- teknium/OpenHermes-2.5
- m-a-p/Code-Feedback
- OpenAssistant/oasst1
- FreedomIntelligence/medical-o1-reasoning-SFT
- glaiveai/glaive-function-calling-v2
- openai/openai_humaneval
- HuggingFaceH4/no_robots
- open-thoughts/OpenThoughts-114k
- HuggingFaceH4/ultrachat_200k
- google-research-datasets/poem_sentiment
- CohereLabs/aya_dataset
- sentence-transformers/natural-questions
---
msh-tiny (GGUF)
A tiny (~47M parameter) GPT-2-style chat model, trained completely from scratch — no pretrained base model, custom BPE tokenizer trained from zero, custom PyTorch transformer architecture. This repo contains GGUF builds for use with llama.cpp, Ollama, and LM Studio.
The .safetensors source model is at mondk/Msh-Tiny-47M.
Limitations
This model was trained from random initialization on a modest amount of data with limited compute — it is a small educational project, not a production-quality assistant. Expect it to follow the chat format reliably but produce limited/inconsistent knowledge and occasional incoherent answers.
Prompt format
<|user|>
{your message}
<|assistant|>
The model was trained to stop generating at <|end|>.
Usage
Ollama
FROM ./model-f16.gguf
ollama create msh-tiny -f Modelfile
ollama run msh-tiny
LM Studio: drop the .gguf file into your models folder and load it directly.
llama.cpp
./llama-cli -m model-f16.gguf -p "<|user|>\nhi\n<|assistant|>\n"
Training data
Combining 15 well-known open instruction/chat datasets plus a small hand-written set of everyday chit-chat (greetings, thanks, small talk):
- mondk/Greetings-hi-for-train-Msh-v2
- tatsu-lab/alpaca
- databricks/databricks-dolly-15k
- teknium/OpenHermes-2.5
- m-a-p/Code-Feedback
- OpenAssistant/oasst1
- FreedomIntelligence/medical-o1-reasoning-SFT
- glaiveai/glaive-function-calling-v2
- openai/openai_humaneval
- HuggingFaceH4/no_robots
- open-thoughts/OpenThoughts-114k
- HuggingFaceH4/ultrachat_200k
- google-research-datasets/poem_sentiment
- CohereLabs/aya_dataset
- sentence-transformers/natural-questions
Run mondk/Msh-Tiny-47M-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models