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-373M - GGUF

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

GGUF quantizations of 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.

Run it with Ollama (one command)

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

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 for details.

License

Apache 2.0, same as the base model.

Downloads last month: 59

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for jbomdev/AlterEgo-GGUF

Base model

jbomdev/AlterEgo

Quantized

(1)

this model