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README.md

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+---
+license: gemma
+base_model: google/gemma-4-E2B-it
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+datasets:
+  - build-small-hackathon/slipstream-evm-sft
+tags:
+  - project-controls
+  - earned-value-management
+  - forecasting
+  - lora
+  - distillation
+  - code-action
+  - agent
+  - slipstream
+---
+# Slipstream - gemma-4-E2B (EVM forecasting agent)
+
+A small **code-action agent** that forecasts a project's final cost (EAC) and finish period from a
+mid-flight Earned Value Management snapshot. It is **`google/gemma-4-E2B-it`** (E2B (~2B effective),
+Gemma-4 (text decoder)) fine-tuned (LoRA, then merged) to run a single-tool reasoning loop: it writes Python
+that calls a curated forecasting toolkit (Earned Schedule, CPI/SPI formulas, a Gompertz growth
+curve, a reference-class ML regressor, and the TimesFM / Chronos time-series foundation models),
+reconciles their disagreeing estimates, and submits one answer.
+
+It was **distilled from a DeepSeek V4 teacher**: the teacher's reasoning traces over a diverse
+simulated project corpus were filtered to a 367-trace SFT set
+([`build-small-hackathon/slipstream-evm-sft`](https://huggingface.co/datasets/build-small-hackathon/slipstream-evm-sft)) and the student trained with
+assistant-only loss (reasoning + tool-call tokens only). This makes a sub-5B, **edge / air-gapped**
+forecaster that matches the classical project-controls baseline and approaches its cloud teacher.
+
+## Results (held-out real projects, 40% complete, n=107)
+
+Scored on 107 real completed projects (Batselier/OR-AS DSLIB), apples-to-apples with every
+baseline. `valid` = produced a usable forecast; `EAC error` = median absolute % error on final cost;
+`finish error` = median absolute error in periods.
+
+| Method | valid | EAC error | finish error |
+| --- | --- | --- | --- |
+| **gemma-4-E2B (this model, distilled)** | **0.991** | **2.31%** | **0.63 periods** |
+| gemma-4-E2B (base, before distillation) | 0.664 | 3.21% | 0.75 periods |
+| Earned Schedule (classical baseline) | 1 | 2.37% | 1 periods |
+| DeepSeek V4 teacher (cloud) | 1 | 2.4% | 0.6 periods |
+
+Distillation lifts a base model that could barely operate the tool-call format into a reliable
+forecaster that rivals the classical canon and its own teacher.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "build-small-hackathon/slipstream-gemma4-e2b-evm"
+tok = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16")
+```
+
+The model is trained to act through a single `run_python(code=...)` tool call and to call
+`submit(finish, eac)` from inside that code. See the Slipstream project for the agent loop, the
+forecasting toolkit, and the full benchmark.
+
+## Licence and attribution
+
+This is a derivative of **[`google/gemma-4-E2B-it`](https://huggingface.co/google/gemma-4-E2B-it)** and is released
+under the **base model's licence** (gemma).
+You must comply with the upstream terms.
+Training data: [`build-small-hackathon/slipstream-evm-sft`](https://huggingface.co/datasets/build-small-hackathon/slipstream-evm-sft).
+
+Built for the Hugging Face **Build Small** hackathon.