Instructions to use build-small-hackathon/slipstream-gemma4-e2b-evm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/slipstream-gemma4-e2b-evm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="build-small-hackathon/slipstream-gemma4-e2b-evm")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("build-small-hackathon/slipstream-gemma4-e2b-evm")
model = AutoModelForMultimodalLM.from_pretrained("build-small-hackathon/slipstream-gemma4-e2b-evm")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use build-small-hackathon/slipstream-gemma4-e2b-evm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/slipstream-gemma4-e2b-evm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/slipstream-gemma4-e2b-evm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/slipstream-gemma4-e2b-evm

SGLang

How to use build-small-hackathon/slipstream-gemma4-e2b-evm with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "build-small-hackathon/slipstream-gemma4-e2b-evm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/slipstream-gemma4-e2b-evm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "build-small-hackathon/slipstream-gemma4-e2b-evm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/slipstream-gemma4-e2b-evm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use build-small-hackathon/slipstream-gemma4-e2b-evm with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/slipstream-gemma4-e2b-evm
```

slipstream-gemma4-e2b-evm / README.md

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metadata

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) 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

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 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.

Built for the Hugging Face Build Small hackathon.