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", device_map="auto")

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

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

Atomic Chat

AlterEgo-GGUF

File size: 2,959 Bytes

---
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).