This is a custom-quantized version of TinyLlama-1.1B, specifically optimized to attempt to revive the spirit and utility of the legendary SmarterChild AIM bot. While not quite as snarky as the OG, this model maintains a fun, snappy, and positive coherence that is random/uplifting in its nature. While the model is not 100% factually correct with dates or names, it is good for things like: introspection, simple advice, fake inspirational quotes, idea generation, simple coding examples, recipes, lyric generation, storytelling, brainstorming, and all other manner of silly musings (It may hallucinate, talk to itself, or "slop out" on prompts with low context like "hey!").

🧠 Why this model is different

Unlike a standard 1.1B quant, this model was processed using a custom Importance Matrix (imatrix). The training data for the imatrix was hand-curated to preserve:

Classic AIM Dialect: High retention of 2000s-era slang (rofl, lmao, brb, s/l/r).
Logical Flow: Inclusion of wllama.js source code and logic puzzles in the imatrix training to ensure the model stays coherent at low bitrates.
Modern Awareness: Contextual data for 2026, including local-first AI and edge computing concepts.

🛠 Quantization Details

Base Model: TinyLlama-1.1B-Chat-v1.0
Quantization: Q4_K_M
Format: GGUF
Size: ~668 MB
Context Length: 2048 tokens

📈 Perplexity Benchmarks

The following results were generated using llama-perplexity on the wikitext-2-raw/wiki.test.raw dataset.

Model	Precision	Perplexity (PPL)	Δ PPL
TinyLlama-1.1B (Baseline)	F16	19.5532	-
TinyLlama-1.1B (Quant)	Q4_K_M	19.9509	+0.3977

⚖️ Evaluation Verdict

For a model as small as TinyLlama (1.1B), this is a highly successful quantization. Smaller models are inherently "fragile"—they have fewer parameters to represent complex information, so reducing bit-depth usually results in a significant accuracy drop. A Delta of +0.3977 indicates that the Q4_K_M method has preserved the vast majority of the model's reasoning capabilities while reducing the memory footprint by approximately 85%.

🚀 Hardware Performance (Apple M2)

Throughput: 100 tokens/sec (Prompt Eval)
Memory Usage: ~636 MiB RAM for model weights.

🚀 Hardware Performance (Apple Intel i7)

Throughput: 3 tokens/sec (Prompt Eval)
Usage: I have included a custom built, non-AVX llama-server C++ executable in the files! Use this server in combination with my model to turn old consumer computers into cpu-only inference machines!! Runs AI on basicaly any computer from the last 16 years!