Instructions to use jbomdev/AlterEgo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jbomdev/AlterEgo-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="jbomdev/AlterEgo-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jbomdev/AlterEgo-GGUF", dtype="auto")

llama-cpp-python

How to use jbomdev/AlterEgo-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jbomdev/AlterEgo-GGUF",
	filename="alterego-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jbomdev/AlterEgo-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 jbomdev/AlterEgo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jbomdev/AlterEgo-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jbomdev/AlterEgo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jbomdev/AlterEgo-GGUF:Q4_K_M

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 jbomdev/AlterEgo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jbomdev/AlterEgo-GGUF:Q4_K_M

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 jbomdev/AlterEgo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jbomdev/AlterEgo-GGUF:Q4_K_M

Use Docker

docker model run hf.co/jbomdev/AlterEgo-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use jbomdev/AlterEgo-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jbomdev/AlterEgo-GGUF:Q4_K_M

SGLang

How to use jbomdev/AlterEgo-GGUF 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 "jbomdev/AlterEgo-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jbomdev/AlterEgo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jbomdev/AlterEgo-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jbomdev/AlterEgo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jbomdev/AlterEgo-GGUF with Ollama:
```
ollama run hf.co/jbomdev/AlterEgo-GGUF:Q4_K_M
```

Unsloth Studio

How to use jbomdev/AlterEgo-GGUF 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 jbomdev/AlterEgo-GGUF 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 jbomdev/AlterEgo-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jbomdev/AlterEgo-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use jbomdev/AlterEgo-GGUF with Docker Model Runner:
```
docker model run hf.co/jbomdev/AlterEgo-GGUF:Q4_K_M
```

Lemonade

How to use jbomdev/AlterEgo-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jbomdev/AlterEgo-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AlterEgo-GGUF-Q4_K_M

List all available models

lemonade list

AlterEgo-GGUF / README.md

jbomdev

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	---
	license: apache-2.0
	base_model: jbomdev/AlterEgo
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- gguf
	- llama.cpp
	- ollama
	- text-generation
	- from-scratch
	- chatml
	---

	<div align="center">

	# 🧠 AlterEgo-373M - GGUF

	GGUF builds of a 373M language model designed, trained, and served entirely from scratch.

	[![Model](https://img.shields.io/badge/🤗-Original%20model-yellow)](https://huggingface.co/jbomdev/AlterEgo)
	[![Code](https://img.shields.io/badge/GitHub-AlterEgo%20(training)-181717?logo=github)](https://github.com/J-bom/AlterEgo)
	[![Platform](https://img.shields.io/badge/GitHub-LLME%20(platform)-181717?logo=github)](https://github.com/J-bom/LLME)
	[![Params](https://img.shields.io/badge/params-373M-green)]()

	</div>

	---

	GGUF quantizations of [jbomdev/AlterEgo](https://huggingface.co/jbomdev/AlterEgo), a 373M-parameter decoder-only model built from the ground up: architecture, training, tokenizer, and inference all written from scratch. For the full story, including architecture, training curves, hyperparameters, and benchmarks, see the [main model card](https://huggingface.co/jbomdev/AlterEgo).

	## Run it with Ollama (one command)

	```bash
	ollama run hf.co/jbomdev/AlterEgo-GGUF:Q8_0
	```

	Swap the tag for any quant in the table (`:Q4_K_M`, `:F16`). The ChatML template, stop tokens, and sampling defaults are applied automatically from the GGUF metadata and the `params` file in this repo.

	## Run it with llama.cpp

	```bash
	llama-cli -hf jbomdev/AlterEgo-GGUF:Q8_0 -p "Tell me about the ocean."
	```

	## Quantizations

	\| File \| Quant \| Size \| Notes \|
	\|---\|---\|---\|---\|
	\| `alterego-Q8_0.gguf` \| Q8_0 \| ~0.4 GB \| Recommended. Near-lossless, still tiny. \|
	\| `alterego-Q4_K_M.gguf` \| Q4_K_M \| ~0.25 GB \| Smallest. Some quality loss, more noticeable on a model this small. \|
	\| `alterego-F16.gguf` \| F16 \| ~0.75 GB \| Full precision, max quality. \|

	AlterEgo is small enough that Q8_0 (or even F16) runs comfortably on any laptop, and at this scale those preserve quality better than aggressive 4-bit quantization. Reach for Q4_K_M only if you want the smallest possible download.

	## Recommended generation settings

	These are the defaults AlterEgo was tuned and served with in LLME:

	\| Parameter \| Value \|
	\|---\|---\|
	\| `temperature` \| 0.7 \|
	\| `top_k` \| 50 \|
	\| `top_p` \| 1.0 \|
	\| `repeat_penalty` \| 1.1 \|

	## Chat format

	AlterEgo uses ChatML, and stops on `<\|im_end\|>` or `<\|endoftext\|>`:

	```
	<\|im_start\|>system
	{system prompt}<\|im_end\|>
	<\|im_start\|>user
	{message}<\|im_end\|>
	<\|im_start\|>assistant
	```

	## Limitations

	A 373M model on a modest token budget behaves like one: it can be factually wrong, repeat itself, and lose coherence on long prompts. English only. Not safety- or preference-tuned. See the [main model card](https://huggingface.co/jbomdev/AlterEgo#limitations) for details.

	## License

	Apache 2.0, same as the [base model](https://huggingface.co/jbomdev/AlterEgo).