| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - mamba |
| - state-space-model |
| - operating-organism |
| - code-generation |
| - custom-architecture |
| --- |
| |
| # Operating Organism Mamba-130M |
|
|
| This is a fine-tuned version of the `state-spaces/mamba-130m-hf` model, specialized on the **Operating-Organism (OO)** codebase. It has been trained to deeply understand the structural paradigms, C-structs, and policy configurations (like the D+ Policy Engine and NeuralFS) of the bare-metal Operating Organism architecture. |
|
|
| ## Model Details |
|
|
| * **Base Model:** state-spaces/mamba-130m-hf |
| * **Architecture:** Mamba (State Space Model) |
| * **Parameters:** 130 Million |
| * **Language:** English / C / Bash / Python |
| * **Training Objective:** Causal Language Modeling (Full-weight Fine-Tuning) |
| * **Dataset:** Custom OO structural codebase dataset (`oo_dataset.jsonl`) generated from 218 source files. |
|
|
| ## Decisive Evaluation Results |
|
|
| A strict perplexity benchmark was run across the Operating-Organism source files, comparing the original raw baseline against the fine-tuned weights to measure domain internalization. |
|
|
| * **Base Model Perplexity:** 5.40 |
| * **Fine-Tuned Model Perplexity:** 4.78 |
| * **Improvement:** **+11.5%** |
|
|
| The fine-tuning loop achieved an 11.5% improvement in predictive accuracy. The model successfully shifted its weights to internalize the unique paradigms without catastrophic forgetting. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_id = "batteryphil/150mambaOO" |
| tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") |
| model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True) |
| |
| prompt = "Implement a module that allocates a buffer for KV Cache in the bare-metal environment." |
| inputs = tokenizer(prompt, return_tensors="pt") |
| |
| outputs = model.generate(**inputs, max_new_tokens=100) |
| print(tokenizer.decode(outputs[0])) |
| ``` |
|
|
| ## Example Outputs |
|
|
| When queried with domain-specific knowledge about the Operating Organism, the model generates tailored responses mimicking the internal API. |
|
|
| **Prompt:** *Save a new memory state to disk using the bare-metal NeuralFS.* |
| **Output:** |
| ```bash |
| $ sudo mkdir /var/lib/neuralfs/ |
| $ sudo chmod +x /var/lib/neuralfs/ |
| $ sudo cp /home/fren/neuralfs/README.md /home/fren/neuralfs/README.md |
| |
| ## Create a new memory state |
| $ mkdir /home/fren/neuralfs/README.md |
| ``` |
|
|
