Instructions to use nqd145/Gemma-4-E2B-it-abliterated-litertlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nqd145/Gemma-4-E2B-it-abliterated-litertlm with Transformers:

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

pipe = pipeline("text-generation", model="nqd145/Gemma-4-E2B-it-abliterated-litertlm")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nqd145/Gemma-4-E2B-it-abliterated-litertlm", dtype="auto")

LiteRT-LM

How to use nqd145/Gemma-4-E2B-it-abliterated-litertlm with LiteRT-LM:

# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM)
# and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter).
# For platform-specific integration guides, please refer to the official developer website:
# https://ai.google.dev/edge/litert-lm

# To try LiteRT-LM, the easiest way is to use our CLI tool.
# 1. Install the LiteRT-LM CLI tool:
pip install litert-lm

# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
litert-lm run \
  --from-huggingface-repo=nqd145/Gemma-4-E2B-it-abliterated-litertlm \
  model.litertlm \
  --prompt="Write me a poem"

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nqd145/Gemma-4-E2B-it-abliterated-litertlm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nqd145/Gemma-4-E2B-it-abliterated-litertlm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nqd145/Gemma-4-E2B-it-abliterated-litertlm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nqd145/Gemma-4-E2B-it-abliterated-litertlm

SGLang

How to use nqd145/Gemma-4-E2B-it-abliterated-litertlm 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 "nqd145/Gemma-4-E2B-it-abliterated-litertlm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nqd145/Gemma-4-E2B-it-abliterated-litertlm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "nqd145/Gemma-4-E2B-it-abliterated-litertlm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nqd145/Gemma-4-E2B-it-abliterated-litertlm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nqd145/Gemma-4-E2B-it-abliterated-litertlm with Docker Model Runner:
```
docker model run hf.co/nqd145/Gemma-4-E2B-it-abliterated-litertlm
```

Gemma-4-E2B-it-abliterated-litertlm

File size: 1,425 Bytes

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---
license: apache-2.0
base_model:
- huihui-ai/Huihui-gemma-4-E2B-it-abliterated
- google/gemma-4-E2B-it
tags:
- gemma
- litert-lm
- tflite
- abliterated
pipeline_tag: text-generation
language:
- en
library_name: transformers
model_type: gemma
inference: false
---

# Gemma-4-E2B-it-abliterated (LiteRT-LM)

LiteRT-LM export of `huihui-ai/Huihui-gemma-4-E2B-it-abliterated` for on-device / edge inference workflows.

## Model File

- `Gemma-4-E2B-it-abliterated.litertlm`

## Source

- Base checkpoint: `huihui-ai/Huihui-gemma-4-E2B-it-abliterated`
- Export pipeline: `safetensors-to-litertlm`

## Export Notes

- Export format: `.litertlm` (LiteRT-LM bundle)
- Quantization: INT8 profile (`dynamic_wi8_afp32`)
- Intended runtime: `litert-lm` CLI / LiteRT-LM compatible apps

## Quick Start (CPU)

```bash
litert-lm run ./Gemma-4-E2B-it-abliterated.litertlm --prompt "Hi" --backend cpu
```

## Limitations

- Behavior may differ from the original HF checkpoint due to conversion/quantization/runtime differences.
- Some export profiles that reduce memory pressure can alter section topology and runtime behavior.

## Safety

This model may generate unsafe or incorrect content. Evaluate carefully for your use case and apply application-level safeguards where needed.

## License

Please follow the upstream license and usage terms of:

- `huihui-ai/Huihui-gemma-4-E2B-it-abliterated`
- underlying Gemma model family terms