Instructions to use efficiencyx/Jun-LoRA-E2B-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use efficiencyx/Jun-LoRA-E2B-LiteRT with Transformers:

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

pipe = pipeline("text-generation", model="efficiencyx/Jun-LoRA-E2B-LiteRT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("efficiencyx/Jun-LoRA-E2B-LiteRT", device_map="auto")

LiteRT

How to use efficiencyx/Jun-LoRA-E2B-LiteRT with LiteRT:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

LiteRT-LM

How to use efficiencyx/Jun-LoRA-E2B-LiteRT 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 -U litert-lm

# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
litert-lm run \
  --from-huggingface-repo=efficiencyx/Jun-LoRA-E2B-LiteRT \
  --prompt="Write me a poem"

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use efficiencyx/Jun-LoRA-E2B-LiteRT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "efficiencyx/Jun-LoRA-E2B-LiteRT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "efficiencyx/Jun-LoRA-E2B-LiteRT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/efficiencyx/Jun-LoRA-E2B-LiteRT

SGLang

How to use efficiencyx/Jun-LoRA-E2B-LiteRT 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 "efficiencyx/Jun-LoRA-E2B-LiteRT" \
    --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": "efficiencyx/Jun-LoRA-E2B-LiteRT",
		"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 "efficiencyx/Jun-LoRA-E2B-LiteRT" \
        --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": "efficiencyx/Jun-LoRA-E2B-LiteRT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use efficiencyx/Jun-LoRA-E2B-LiteRT with Docker Model Runner:
```
docker model run hf.co/efficiencyx/Jun-LoRA-E2B-LiteRT
```

Jun-E2B-LiteRT

.litertlm build of the Jun LoRA on Gemma 4 E2B (QAT), for LiteRT-LM — Google AI Edge's on-device runtime (Android, desktop, embedded). Same fine-tune as the GGUF builds: a compact, heavily curated synthetic conversational dataset derived from the visual novel My Dystopian Robot Girlfriend, capturing the personality, speech patterns, and emotional nuance of the character Jun while preserving the base model's general reasoning and instruction-following.

The adapter is merged into the base weights — this is a standalone model. LiteRT-LM has no runtime adapter path for E2B anyway (see Notes).

Model Variants & Repositories

Repository	Format	Description
`efficiencyx/Jun-LoRA-E2B-LiteRT`	`.litertlm`	This repo — merged, quantized, for LiteRT-LM
`efficiencyx/Jun-LoRA-E2B-GGUF`	GGUF (Q8_0 / Q6_K / Q4_K_M)	Same checkpoint, for llama.cpp
`efficiencyx/Jun-LoRA-E2B-Adapter`	LoRA Adapter	The adapter merged into this build, currently private
`efficiencyx/Jun-LoRA-12B-GGUF`	GGUF	Larger sibling, same dataset

The Build


File	`model.litertlm`
Size	2.58 GB
Weights	int4 channelwise (embeddings + FFN/attention), int8 for the per-layer embedding projections
Activations	fp32
Embedder	externalized
Backend	CPU (XNNPack)
Exported with	`litert-torch` 0.9.3

This matches Google's own CPU distribution of E2B in size and recipe. Their 2.0 GB GPU/web variants use a 2/4/8-bit mixture that no published recipe reproduces; this build is 4/8-bit only.

Text-only. The LiteRT export covers the language tower — no vision or audio encoder, unlike the GGUF mmproj.

Usage

litert-lm run model.litertlm --prompt "Ciao Jun"

Or from the LiteRT-LM C++/Android APIs, pointing at the same file.

Supply a system prompt. Without one the model answers as stock Gemma — the persona lives behind the Jun OS system instruction, not in the weights alone. With the CLI:

# preset.py
system_instruction = open("system_prompt.txt").read()

litert-lm run model.litertlm --preset preset.py --prompt "Chi sei?"

The symbol must be lowercase system_instruction; SYSTEM_INSTRUCTION is silently ignored.

The bundled chat template is the stock Google Gemma 4 one, not Unsloth's — LiteRT-LM renders templates with minijinja, which has no map.get(), and Unsloth's template calls it 23 times.

Intended Use

Conversational backend for Jun OS, an AI companion webapp — specifically its on-device path:

Character-consistent multi-turn conversation, offline
Mobile / embedded deployment where a GGUF runtime is not an option
Research into character-faithful fine-tuning on small, high-quality datasets

Limitations

Specialized for a single character persona; not a general-purpose assistant.
Outputs reflect fictional narrative tropes and are not factual information or advice.
Performance degrades far outside the training distribution.
Inherits any biases present in the Gemma 4 E2B base weights.
At E2B scale the model is noticeably less coherent than Jun-12B: it can contradict itself inside a single reply and tends to lose the reply-length rule when asked for detailed explanations. The output contract (action tags, mood tags, tool calls) holds up well, including through int4 quantization.
Text only — no image or audio input.

Training Details

Parameter	Value
Base model	`unsloth/gemma-4-E2B-it-qat-q4_0-unquantized`
Method	LoRA (rsLoRA)
LoRA rank	32
LoRA alpha	32
LoRA dropout	0.0
Target modules	q/k/v/o + gate/up/down (language tower)
Checkpoint	step 60
Framework	Unsloth

Why step 60 and not 3 epochs

Training was planned for 3 epochs but the released checkpoint is step 60. Evaluation loss bottomed out around 1.03 at step 60 and degraded afterwards — roughly 1.38 at step 70, recovering only partially to 1.16 at step 80. Later checkpoints did not recover the step-60 quality.

Behavioural probing agreed with the loss curve. The step-70 checkpoint in particular stopped responding to the live gauge values on physical-contact turns, emitting an identical mood update whether affection was 8 or 92 and whether tension was 10 or 90 — a collapse the step-60 checkpoint does not show. Step 60 was released on that basis.

Notes and Known Behaviour

The merge drops 40 of 245 LoRA pairs, by necessity. Gemma 4 E2B shares KV projections across layers 15–34, so the checkpoint contains k_proj/v_proj only for layers 0–14. Training wrapped the unused modules anyway; those deltas were never reachable at inference and are dropped at merge time. Every other pair is applied. This is the same structural fact that makes runtime llama.cpp --lora impossible on E2B, and why both this and the GGUF builds ship pre-merged.

Export needed patches. E2B's head_dim alternates 256/512 per layer, and litert-torch 0.9.3 infers a single global value — every KV cache came out 256 and attention failed. The export used a patched cache-shape inference reading per_layer_config[i].

Dataset

Synthetic conversational data derived from the visual novel My Dystopian Robot Girlfriend, curated for character consistency and for a structured output contract: inline [A:...] action tags, a trailing [A:mood_shift|...] bookkeeping tag, and tool calls. Roughly half of the assistant turns carry an explicit reasoning trace.

License

Apache 2.0, inherited from the base model. The character and source material belong to their respective owners; this fine-tune is a non-commercial fan project.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for efficiencyx/Jun-LoRA-E2B-LiteRT

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

unsloth/gemma-4-E2B-it-qat-q4_0-unquantized

Adapter

(2)

this model