Instructions to use mondk/Msh-Tiny-47M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mondk/Msh-Tiny-47M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mondk/Msh-Tiny-47M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mondk/Msh-Tiny-47M")
model = AutoModelForCausalLM.from_pretrained("mondk/Msh-Tiny-47M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mondk/Msh-Tiny-47M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mondk/Msh-Tiny-47M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mondk/Msh-Tiny-47M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mondk/Msh-Tiny-47M

SGLang

How to use mondk/Msh-Tiny-47M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mondk/Msh-Tiny-47M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mondk/Msh-Tiny-47M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mondk/Msh-Tiny-47M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mondk/Msh-Tiny-47M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mondk/Msh-Tiny-47M with Docker Model Runner:
```
docker model run hf.co/mondk/Msh-Tiny-47M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gguf: https://huggingface.co/mondk/Msh-Tiny-47M-GGUF

msh-tiny

A tiny (~47M parameter) GPT-2-architecture chat model, trained completely from scratch — no pretrained base model. Custom BPE tokenizer trained from zero, custom transformer trained from random initialization, then converted into a standard GPT2LMHeadModel for compatibility with the wider ecosystem.

Limitations

Trained from random initialization on a modest amount of data with limited compute — a small educational project, not a production-quality assistant. Expect reliable chat formatting but limited/inconsistent knowledge and occasional incoherent answers.

Prompt format

<|user|>
{your message}
<|assistant|>

The model stops generating at <|end|>.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("mondk/Msh-Tiny-47M")
tokenizer = AutoTokenizer.from_pretrained("mondk/Msh-Tiny-47M")

prompt = "<|user|>\nhi\n<|assistant|>\n"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(input_ids, max_new_tokens=150, do_sample=True, temperature=0.7, top_k=40)
print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True))

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

Downloads last month: 251

Safetensors

Model size

47.2M params

Tensor type

F32

Model tree for mondk/Msh-Tiny-47M

Quantizations

1 model

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/

Msh-Tiny-47M