Instructions to use Meridian-MRM/Gast-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Meridian-MRM/Gast-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Meridian-MRM/Gast-Mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Meridian-MRM/Gast-Mini") model = AutoModelForCausalLM.from_pretrained("Meridian-MRM/Gast-Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Meridian-MRM/Gast-Mini 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 Meridian-MRM/Gast-Mini:F16 # Run inference directly in the terminal: llama cli -hf Meridian-MRM/Gast-Mini:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Meridian-MRM/Gast-Mini:F16 # Run inference directly in the terminal: llama cli -hf Meridian-MRM/Gast-Mini:F16
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 Meridian-MRM/Gast-Mini:F16 # Run inference directly in the terminal: ./build/bin/llama cli -hf Meridian-MRM/Gast-Mini:F16
Use Docker
docker model run hf.co/Meridian-MRM/Gast-Mini:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use Meridian-MRM/Gast-Mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Meridian-MRM/Gast-Mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Meridian-MRM/Gast-Mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Meridian-MRM/Gast-Mini:F16
- SGLang
How to use Meridian-MRM/Gast-Mini 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 "Meridian-MRM/Gast-Mini" \ --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": "Meridian-MRM/Gast-Mini", "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 "Meridian-MRM/Gast-Mini" \ --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": "Meridian-MRM/Gast-Mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Meridian-MRM/Gast-Mini with Ollama:
ollama run hf.co/Meridian-MRM/Gast-Mini:F16
- Unsloth Desktop
- Docker Model Runner
How to use Meridian-MRM/Gast-Mini with Docker Model Runner:
docker model run hf.co/Meridian-MRM/Gast-Mini:F16
- Lemonade
How to use Meridian-MRM/Gast-Mini with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Meridian-MRM/Gast-Mini:F16
Run and chat with the model
lemonade run user.Gast-Mini-F16
List all available models
lemonade list
- Atomic Chat
File size: 4,370 Bytes
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---
license: apache-2.0
language:
- en
tags:
- qwen3
- gast
- causal-lm
- base-model
- text-generation
- gguf
- small-language-model
library_name: transformers
pipeline_tag: text-generation
datasets:
- HuggingFaceFW/fineweb-edu
- Salesforce/wikitext
---
<div align="center">
<h1 style="font-size: 3rem; margin: 12px 0 6px 0; font-weight: 800; letter-spacing: 1.5px;">Gast-Mini</h1>
<p>
<img src="https://img.shields.io/badge/Parameters-7.07M-blue.svg" alt="Parameters" />
<img src="https://img.shields.io/badge/Context-512_tokens-orange.svg" alt="Context" />
<img src="https://img.shields.io/badge/Tokens-9B-green.svg" alt="Tokens" />
</p>
<p>Base causal language model with 7.07M parameters, trained from scratch.</p>
</div>
---
## Architecture
| Field | Value |
|---|---|
| Parameters | 7.07M |
| Layers | 16 |
| Hidden size | 192 |
| Attention | GQA (3 Query / 1 KV head, head_dim 64) |
| Intermediate size | 512 SwiGLU |
| Norm | RMSNorm (eps 1e-6) + per-head QK-Norm |
| RoPE theta | 10000.0 |
| Context length | 512 |
| Vocab size | 4000 |
| Tokenizer | Custom BPE (byte-level) |
| Embeddings | Tied (`lm_head` == `embed_tokens`) |
| HF Layout | `Qwen3ForCausalLM` |
## Training & Corpus
| Parameter | Value |
|---|---|
| Optimizer | AdamW (beta1=0.9, beta2=0.95, wd=0.1) |
| LR | 2e-3 (flat, 500-step warmup) |
| Batch | 196,608 tokens/step |
| Precision | fp16 |
| Tokens seen | ~9B |
### Pretraining Data
| Source | Tokens | License |
|---|---:|---|
| FineWeb-Edu (sample/10BT) | ~8.85B | ODC-By 1.0 |
| WikiText-103-raw-v1 | ~0.17B | CC BY-SA 4.0 |
| **Total** | **~9B** | |
## Samples
Generated with `llama.cpp`, `temperature=0.85, top_p=0.92, top_k=50, repeat_penalty=1.15`.
> **Prompt:** Artificial intelligence is
>
> Artificial intelligence is the most efficient, and it is difficult to obtain data from human beings. It has no clearly defined or explicit identity, but only a firm assumption of a common law. The Digital Literature Society, Digital Literature Society, ABA, and Reference Library Society have been compiled to address the data collection needs of the digital literacy system. They provide advanced technical assistance in a variety of applications, including on-line communication with computers, and are now referred to as Dig
> **Prompt:** Photosynthesis is the process by which
>
> Photosynthesis is the process by which DNA can transfer a protein to a cell with the same function of its function. The most common function of a cell is to transfer it from one cell to another, and then to use it for a given cell or genome. The key step in creating a healthy organism is to create a bridge between living organisms that can survive. This process involves the formation of new cells in which the cells are made up of new cells. The cell contains only two cells, each one being formed into two sections, with an additional two cells having a different cell shape
> **Prompt:** The theory of relativity states that
>
> The theory of relativity states that the stars are all uniform in shape. For example, the star can be measured by a gravitational force, which is a small amount of mass that can be measured in terms of grams per kilogram. Similarly, the star is used to calculate its mass: This theory states that the grams of the stars are equal to 1,000,000,000 and their masses are equal to 2,500,000,000 and the masses are equal to 12,000,000.
## Usage
### llama.cpp
```bash
./llama-cli -m gast-7m-f16.gguf -p "The history of the Roman Empire" -n 128 --temp 0.85 --top-k 50 --top-p 0.92 --repeat-penalty 1.15
```
### llama-cpp-python
```python
from llama_cpp import Llama
llm = Llama(model_path="gast-7m-f16.gguf", n_ctx=512)
out = llm(
"The history of the Roman Empire",
max_tokens=120,
temperature=0.85,
top_p=0.92,
top_k=50,
repeat_penalty=1.15,
)
print(out["choices"][0]["text"])
```
## Limitations
- English only.
- Base model, not instruction tuned, no chat template.
- Trained on ~9B tokens; not factual, barely coherent beyond a few words.
## License
Apache 2.0.
## Credits
- FineWeb-Edu - Hugging Face (ODC-By 1.0)
- WikiText-103 - Salesforce (CC BY-SA 4.0)
- llama.cpp - GGUF tooling
- Hugging Face
## Citation
```bibtex
@misc{gastmini,
title = {Gast-Mini},
author = {Meridian-MRM},
howpublished = {https://huggingface.co/Meridian-MRM/Gast-Mini}
}
```
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