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jbomdev/
AlterEgo-GGUF

Name: AlterEgo-GGUF
Availability: InStock

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🧠 AlterEgo-373M - GGUF

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

Code

Platform

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.