2.9 KiB
license, base_model, language, pipeline_tag, library_name, tags
| license | base_model | language | pipeline_tag | library_name | tags | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 | jbomdev/AlterEgo |
|
text-generation | transformers |
|
🧠 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.