Text Generation
PyTorch
Safetensors
GGUF
English
byrne
spikewhale
looped-transformer
memory-cache
mla
small-language-model
conversational
Instructions to use Quazim0t0/Byrne-100M-Ultra-MC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Quazim0t0/Byrne-100M-Ultra-MC 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 Quazim0t0/Byrne-100M-Ultra-MC:F16 # Run inference directly in the terminal: llama cli -hf Quazim0t0/Byrne-100M-Ultra-MC:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Quazim0t0/Byrne-100M-Ultra-MC:F16 # Run inference directly in the terminal: llama cli -hf Quazim0t0/Byrne-100M-Ultra-MC:F16
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 Quazim0t0/Byrne-100M-Ultra-MC:F16 # Run inference directly in the terminal: ./llama-cli -hf Quazim0t0/Byrne-100M-Ultra-MC:F16
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 Quazim0t0/Byrne-100M-Ultra-MC:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Quazim0t0/Byrne-100M-Ultra-MC:F16
Use Docker
docker model run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16
- LM Studio
- Jan
- vLLM
How to use Quazim0t0/Byrne-100M-Ultra-MC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Quazim0t0/Byrne-100M-Ultra-MC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Quazim0t0/Byrne-100M-Ultra-MC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16
- Ollama
How to use Quazim0t0/Byrne-100M-Ultra-MC with Ollama:
ollama run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16
- Unsloth Desktop
- Docker Model Runner
How to use Quazim0t0/Byrne-100M-Ultra-MC with Docker Model Runner:
docker model run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16
- Lemonade
How to use Quazim0t0/Byrne-100M-Ultra-MC with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Quazim0t0/Byrne-100M-Ultra-MC:F16
Run and chat with the model
lemonade run user.Byrne-100M-Ultra-MC-F16
List all available models
lemonade list
- Atomic Chat
5m-ablation card: two seeds on std/both
Browse files- 5m-ablation/README.md +26 -16
5m-ablation/README.md
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@@ -4,12 +4,16 @@ Four small trains. Same FineWeb-Edu blend, same 10k steps, same looped
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stack. Only two flags change. This is **not** Byrne-100M-Ultra-MC. It is
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the isolation run that 114M card could not do.
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| directory | MC | RoPE | params |
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| `std/` | off | geometric | 5.70M | 34.82 |
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| `mc/` | on | geometric | 5.85M | 41.23 |
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| `fractal/` | off | γ=1 | 5.70M | 40.49 |
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| `both/` | on | γ=1 | 5.85M | 32.82 |
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Each folder is `config.json` + `model.safetensors` at step 10k.
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Shared tokenizer is `tokenizer.json` in this directory (same 16,512
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**Putting fractal RoPE on a finished `std` net after training** (nothing
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else on) did not help. Unique-text 1024: 46.75 → 54.95.
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**Both together**
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## Why `both`, maybe
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## 10k steps and “growing” memory
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stack. Only two flags change. This is **not** Byrne-100M-Ultra-MC. It is
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the isolation run that 114M card could not do.
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| directory | MC | RoPE | params | Wiki @1024 seed 1 | Wiki @1024 seed 2 |
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| `std/` | off | geometric | 5.70M | 34.82 | **30.86** |
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| `mc/` | on | geometric | 5.85M | 41.23 | — |
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| `fractal/` | off | γ=1 | 5.70M | 40.49 | — |
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| `both/` | on | γ=1 | 5.85M | **32.82** | 33.32 |
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These folders are **seed 1**. Seed 2 retrained `std` and `both` only
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(not uploaded here). Eval repeat matched to the printed digits. The
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seed gap is training, not measurement.
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Each folder is `config.json` + `model.safetensors` at step 10k.
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Shared tokenizer is `tokenizer.json` in this directory (same 16,512
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**Putting fractal RoPE on a finished `std` net after training** (nothing
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else on) did not help. Unique-text 1024: 46.75 → 54.95.
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**Both together** beat `std` at train length on seed 1 (Wiki 32.82 vs
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34.82; unique 26.67 vs 46.75). On seed 2, `std` won Wiki (30.86 vs
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33.32) and unique (13.35 vs 32.03). That 1024 win did not replicate.
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Past 1024, `both` still fell off faster than `std` on both seeds.
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Stretching the window favoured plain geometric.
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MQAR 0/16 on every cell, both seeds.
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## Why `both`, maybe
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Seed 1 looked like an interaction. Seed 2 did not copy the Wiki win.
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Gate *strength* on seed 1 is similar (`mean |tanh|` ~0.25 on `mc` vs
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~0.26 on `both`). Sign is not: `mc` mixed +/−, layer 0 never left
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zero; `both` layers 1–4 the same sign. Knock-out: `both` seed 1
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26.67 → 33.93 unique PPL when the gates are zeroed; seed 2 32.03 →
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42.45; `mc` 161 → 170 on an already-bad net. Hypothesis from seed 1:
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at this width the branch settled next to Cantor frequencies and
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fought itself next to geometric. It does not explain std unique PPL
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moving 46.75 → 13.35 across seeds. I would not call `both` the
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winner at 5M.
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## 10k steps and “growing” memory
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