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SpectraSuite/TriLM_190M_Unpacked model for the ATLAS Engine ecosystem, designed for native, ultra-low-latency CPU inference without any GPU requirement.Packed using the unifiedpack_to_atlas.pytoolchain (v2.10.0) with BF16 weight scale correction.
| Property | Value |
|---|---|
| Format | ATLAS Binary (.atlas), format_version=2 |
| Quantization | TQ1.0 — Ternary Weight Packing (Base-3, ~1.58 bits/weight) |
| Target | Native CPU — Intel AVX2 (Haswell 2013+), no GPU needed |
| File Size | 0.17 GB |
| Inference Speed | ~25 tok/s (f32 bypass) |
| Description | 16 layers, 768 hidden, 2048 intermediate |
| Component | Detail |
|---|---|
| Base Model | SpectraSuite/TriLM_190M_Unpacked |
| Architecture | trilm-99m |
| Layers | 16 |
| Hidden Size | 768 |
| Intermediate Size | 2048 |
| Attention Heads | 12 (GQA, 12 KV heads) |
| Head Dim | 64 |
| RoPE Theta | 10000 |
| Vocabulary | 50304 |
| Context Window | 4096 |
f32 bypass active: hidden=768 ≤ 2048, auto-enabled
Note: Models below 390M parameters have limited capacity for factual knowledge. Larger TriLM variants (1.5B+) show reliable factual recall.
The capital of France isgit clone https://github.com/xxxn3m3s1sxxx/ATLAS-TQ1_0.git1from atlas_infer import AtlasModel
2
3model = AtlasModel("TriLM-190M-ATLAS.tq1.atlas")
4output = model.generate_c(
5 "The capital of France is",
6 max_new_tokens=50,
7 temperature=0.7,
8 top_k=40,
9)
10print(output)atlas --model TriLM-190M-ATLAS.tq1.atlas --prompt "The capital of France is" --max-tokens 501python atlas_server.py --model TriLM-190M-ATLAS.tq1.atlas --port 8080
2curl http://localhost:8080/v1/chat/completions \
3 -H "Content-Type: application/json" \
4 -d '{"prompt": "The capital of France is", "max_tokens": 50}'| Feature | Description |
|---|---|
| No GPU required | Runs on any x86-64 CPU with AVX2 (Intel Haswell 2013+, AMD Excavator 2015+) |
| Hybrid matmul | FFN tensors in int8, QKV/O in TQ1-packed, per-tensor dispatch |
| int4 FFN mode | Halves FFN memory bandwidth for 18-26% speedup (7B/10B) |
| f32 bypass | Auto-enabled for small models (≤1B) and SubLN architectures |
| Ring buffer KV cache | Extended context via NTK-aware RoPE scaling |
| Standalone C++ CLI | No Python or PyTorch required at runtime |
| SSE web server | FastAPI-based /v1/chat/completions with prompt caching |
| Component | License |
|---|---|
| Base Model (SpectraSuite/TriLM_190M_Unpacked) | Apache 2.0 |
| ATLAS Engine | Apache 2.0 |
| This Quantized Derivative | Apache 2.0 |