Instructions to use MrDevCoder01/TrainedModels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrDevCoder01/TrainedModels with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MrDevCoder01/TrainedModels",
	filename="PTM1-1B-Q8.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use MrDevCoder01/TrainedModels with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MrDevCoder01/TrainedModels
# Run inference directly in the terminal:
llama-cli -hf MrDevCoder01/TrainedModels

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MrDevCoder01/TrainedModels
# Run inference directly in the terminal:
llama-cli -hf MrDevCoder01/TrainedModels

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 MrDevCoder01/TrainedModels
# Run inference directly in the terminal:
./llama-cli -hf MrDevCoder01/TrainedModels

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 MrDevCoder01/TrainedModels
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MrDevCoder01/TrainedModels

Use Docker

docker model run hf.co/MrDevCoder01/TrainedModels

LM Studio
Jan

vLLM

How to use MrDevCoder01/TrainedModels with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MrDevCoder01/TrainedModels"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MrDevCoder01/TrainedModels",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MrDevCoder01/TrainedModels

Ollama
How to use MrDevCoder01/TrainedModels with Ollama:
```
ollama run hf.co/MrDevCoder01/TrainedModels
```

Unsloth Studio new

How to use MrDevCoder01/TrainedModels with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MrDevCoder01/TrainedModels to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MrDevCoder01/TrainedModels to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MrDevCoder01/TrainedModels to start chatting

Docker Model Runner
How to use MrDevCoder01/TrainedModels with Docker Model Runner:
```
docker model run hf.co/MrDevCoder01/TrainedModels
```

Lemonade

How to use MrDevCoder01/TrainedModels with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MrDevCoder01/TrainedModels

Run and chat with the model

lemonade run user.TrainedModels-{{QUANT_TAG}}

List all available models

lemonade list

TrainedModels / README.md

MrDevCoder01

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	---
	license: apache-2.0
	language:
	- en
	base_model: []
	pipeline_tag: text-generation
	datasets:
	- HuggingFaceTB/cosmopedia
	- tiiuae/falcon-refinedweb
	library_name: gguf
	tags:
	- text-generation
	- gguf
	- quantized
	- 1b
	- llama-cpp
	---

	# PT1S-1B-Q8.gguf

	This model is a 1-billion parameter text generation model trained on a high-quality mixture of synthetic and web-crawled data. It is optimized for efficiency and performance in a small footprint.

	## Model Details

	- Model Type: Text Generation
	- Parameters: 1B
	- Quantization: Q8_0 (8-bit quantization for high precision with reduced memory)
	- Training Data:
	- [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
	- [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
	- Language(s): English
	- License: Apache 2.0

	## Training Information

	The model was trained on a curated blend of:
	1. Cosmopedia: A large-scale synthetic dataset designed to provide high-quality educational content across various domains.
	2. Falcon RefinedWeb: A massive, filtered web dataset that provides broad world knowledge and linguistic diversity.

	This combination allows the model to have both structured knowledge from synthetic sources and a natural "web-aware" conversational style.

	## Usage

	### llama.cpp

	You can use this model with [llama.cpp](https://github.com/ggerganov/llama.cpp) by running:

	```bash
	./main -m PT1S-1B-Q8.gguf -p "Once upon a time," -n 128
	```

	### Python (via llama-cpp-python)

	```python
	from llama_cpp import Llama

	llm = Llama(model_path="./PT1S-1B-Q8.gguf")
	output = llm("Q: What is the importance of cosmopedia dataset? A:", max_tokens=100)
	print(output)
	```

	## Intended Use

	This model is ideal for:
	- Lightweight text generation tasks.
	- Educational applications.
	- On-device inference where memory is limited.
	- Research into small language models (SLMs).

	## Limitations and Bias

	While trained on filtered data, small models may still exhibit biases or generate incorrect information (hallucinations). Users should always verify the output of the model for critical applications.