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
5.7M 2x2: seed 2 destales the both-vs-std Wiki win
Browse files
README.md
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@@ -200,7 +200,8 @@ before they opened). That is an inference knock-out on this checkpoint,
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not a model trained without MC. The 114M twin also dropped fractal RoPE,
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so it is not an MC-only ablation. A 5.7M 2Γ2 that *does* isolate the
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knobs is in [`5m-ablation/`](5m-ablation/README.md). MC alone did not
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help there. MC+fractal together
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If you write a loader: this branch is a full-sequence op. It uses the whole
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`x` given to `forward`. Naive KV cache feeds it one token, it rebuilds
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### 5.7M matched 2Γ2 (10k steps)
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Same blend as the nomc twin. Four runs.
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| std | off | geometric | 34.82 | 46.75 | **56.57** | **98.51** |
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| mc | on | geometric | 41.23 | 161 | 199 | 267 |
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| fractal | off | Ξ³=1 | 40.49 | 145 | 218 | 303 |
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| both | on | Ξ³=1 | **32.82** | **26.67** | 76.41 | 159 |
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That is a 5.7M / 10k result. It is not a 114M result. 10k may also be
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early for the zero-init mix-in: |tanh(gate)| ~0.25 here vs ~0.60 on the
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114M DPO. The paper's growing memory is with **context length**, not
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steps. Gate-opening over training is this code. A longer 5M run is
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untested. Weights: [`5m-ablation/`](5m-ablation/README.md).
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The 114M nomc twin is still two knobs. Do not read 9.26 Wiki (twin
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40.5k, current blend) vs 9.08 (`base_62k`, Dolma history) as MC.
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below). I have not proven it helps at 114M. Paper recall tests on this
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62k base are chance with the branch on or off. A matched 5.7M 2Γ2 on
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the current blend says **MC alone did not help PPL or long context**;
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**MC+fractal together**
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there is ~0.25 vs ~0.60 on this 114M DPO β the mix-in may still be
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opening. The paper does not say "train longer and MC appears"; it says
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memory grows with **how many segments the sequence has**. The
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**What is still missing at 114M.** A train that changes only MC,
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fractal left on. The nomc twin also dropped fractal RoPE, so it is not
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that experiment. The 5.7M 2Γ2 *is* that experiment at small scale:
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MC alone did not help; MC+fractal together
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Weights and write-up: [`5m-ablation/`](5m-ablation/README.md). Paper
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recall tests (S-NIAH, MQAR) on this 62k base are at chance with MC on
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or off; they do not prove MC. If you copy the branch anyway, copy the
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not a model trained without MC. The 114M twin also dropped fractal RoPE,
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so it is not an MC-only ablation. A 5.7M 2Γ2 that *does* isolate the
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knobs is in [`5m-ablation/`](5m-ablation/README.md). MC alone did not
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help there. MC+fractal together beat std at train length on seed 1
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and did not on seed 2.
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If you write a loader: this branch is a full-sequence op. It uses the whole
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`x` given to `forward`. Naive KV cache feeds it one token, it rebuilds
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### 5.7M matched 2Γ2 (10k steps)
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Same blend as the nomc twin. Four runs on seed 1. Seed 2 retrained
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`std` and `both` only. Only `use_memory_cache` and `use_fractal_rope`
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change. Eval is deterministic (repeat matched to the printed digits).
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The seed gap is training, not measurement.
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WikiText-2 PPL @1024 (20 windows):
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| run | MC | RoPE | seed 1 | seed 2 |
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| std | off | geometric | 34.82 | **30.86** |
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| mc | on | geometric | 41.23 | β |
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| fractal | off | Ξ³=1 | 40.49 | β |
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| both | on | Ξ³=1 | **32.82** | 33.32 |
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Unique-text PPL (native):
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| | 512 | 1024 | 2048 | 4096 |
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| std seed 1 | 45.14 | 46.75 | **56.57** | **98.51** |
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| std seed 2 | **13.66** | **13.35** | **17.07** | **38.19** |
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| mc seed 1 | 143 | 161 | 199 | 267 |
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| fractal seed 1 | 131 | 145 | 218 | 303 |
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| both seed 1 | 26.01 | **26.67** | 76.41 | 159 |
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| both seed 2 | 31.25 | 32.03 | 76.82 | 176 |
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Seed 1 val loss at 10k: both 4.87, std 4.96, fractal 5.00, mc 5.06.
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**MC alone does not help at this size.** Worse PPL, worse stretch
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(seed 1; not retrained).
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**Fractal alone does not help.** Same.
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**Swapping fractal onto finished std weights after training** still
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does not help at 1024: seed 1 unique 46.75 β 54.95; seed 2 13.35 β
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16.20.
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**Both together** beat std at train length on seed 1 (Wiki 32.82 vs
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34.82). On seed 2, std won (30.86 vs 33.32). That 1024 win did not
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replicate. Past 1024, both still falls off faster than std on both
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seeds. Stretching the window is a win for geometric. MQAR is 0/16
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every cell, both seeds.
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That is a 5.7M / 10k result. It is not a 114M result. 10k may also be
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early for the zero-init mix-in: |tanh(gate)| ~0.25 here vs ~0.60 on the
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114M DPO. The paper's growing memory is with **context length**, not
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steps. Gate-opening over training is this code. A longer 5M run is
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untested. Weights: [`5m-ablation/`](5m-ablation/README.md) (seed 1).
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Seed 2 lives with the 5M grid, not in this upload.
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**Why both, if each knob alone is worse.** Seed 1 looked like an
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interaction. Seed 2 did not copy the Wiki win. What still holds:
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- `both` actually uses the branch, both seeds. Unique PPL seed 1
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26.67 β 33.93 when `mc_gate` is zeroed; seed 2 32.03 β 42.45.
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`mc` alone is 161 β 170 on an already-bad net (seed 1).
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- Gate *strength* on seed 1 is similar (`mean |tanh|` ~0.25 vs
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~0.26). The difference is **sign**. `mc` (geometric) mixed +/β;
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layer 0 never left zero. `both` (fractal) layers 1β4 the same
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sign. Hypothesis, not proof, and it does not explain seed 2's
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std unique-PPL crash from 46.75 to 13.35.
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- I would not claim MC+fractal is the winner at 5M. I would claim
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MC-only and fractal-only lost on the seed I ran, both is mixed
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across two seeds, and std's unique-text number is seed-noisy.
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The 114M nomc twin is still two knobs. Do not read 9.26 Wiki (twin
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40.5k, current blend) vs 9.08 (`base_62k`, Dolma history) as MC.
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below). I have not proven it helps at 114M. Paper recall tests on this
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62k base are chance with the branch on or off. A matched 5.7M 2Γ2 on
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the current blend says **MC alone did not help PPL or long context**;
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**MC+fractal together** beat std at train length on seed 1 and lost
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on seed 2. That is 5M / 10k, two seeds on std/both, and |tanh(gate)|
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there is ~0.25 vs ~0.60 on this 114M DPO β the mix-in may still be
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opening. The paper does not say "train longer and MC appears"; it says
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memory grows with **how many segments the sequence has**. The
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**What is still missing at 114M.** A train that changes only MC,
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fractal left on. The nomc twin also dropped fractal RoPE, so it is not
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that experiment. The 5.7M 2Γ2 *is* that experiment at small scale:
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+
MC alone did not help; MC+fractal together beat std at 1024 on seed 1
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and not on seed 2.
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Weights and write-up: [`5m-ablation/`](5m-ablation/README.md). Paper
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recall tests (S-NIAH, MQAR) on this 62k base are at chance with MC on
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or off; they do not prove MC. If you copy the branch anyway, copy the
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