Instructions to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b",
	filename="pseudolife-extractor-e4b-v2-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Use Docker

docker model run hf.co/Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

LM Studio
Jan

vLLM

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Ollama
How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with Ollama:
```
ollama run hf.co/Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M
```

Unsloth Studio

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b to start chatting

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with Docker Model Runner:
```
docker model run hf.co/Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M
```

Lemonade

How to use Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b:Q4_K_M

Run and chat with the model

lemonade run user.pseudolife-extractor-gemma-4-e4b-Q4_K_M

List all available models

lemonade list

PseudoLife Extractor E4B

A task-specialized QLoRA fine-tune of Gemma 4 E4B (instruction-tuned) that distills agent session transcripts into structured long-term memory: canonical facts (entity / attribute / value), typed relations, and procedural lessons. It is the default "dream consolidation" extractor for PseudoLife-MCP, a persistent neural-memory system for LLM agents (public code release forthcoming).

Shipped as a merged, Q4_K_M-quantized GGUF (~5.3 GB) that runs CPU-only under llama.cpp at roughly 12–15 tok/s on a desktop CPU — no GPU required.

Files

File	Version	Notes
`pseudolife-extractor-e4b-v2-Q4_K_M.gguf`	v2	Current. Registry-datagen fine-tune (see below).

Version-suffixed filenames are stable: pin the exact file in download URLs. Future extractor versions will be added to this same repository as -v3, -v4, ….

Training

v2 was trained with QLoRA on 887 distilled extraction examples generated by a Claude Sonnet teacher using a per-chain key registry datagen scheme: the teacher is required to re-state carried-forward facts under their previously minted keys each session, which teaches the student consistent key reuse instead of key proliferation. The LoRA was merged into the base weights before quantization.

Evaluation

Scored on a LongMemEval-derived knowledge-update benchmark (same-session comparison against the previous PseudoLife extractor fine-tune of the same base model):

Metric	v2 (this model)	prior fine-tune
KU-oracle, cortex (fact-spine) accuracy	0.705	0.603
KU-oracle, hybrid retrieval accuracy	0.756	0.744
Extraction ladder: gold facts recovered	0.9	1.0
Extraction ladder: stale-fact leakage	0.0	0.0

Usage

Serve with llama.cpp (the daemon-facing OpenAI-compatible endpoint PseudoLife-MCP expects):

llama-server -m pseudolife-extractor-e4b-v2-Q4_K_M.gguf --port 8081 -c 8192

Recommended sampling is embedded in the GGUF metadata (temperature 1.0, top_k 64, top_p 0.95).

Scope note: this is a narrow specialist, not a chat model. It is trained against PseudoLife-MCP's combined facts + relations extraction prompt format and will underperform on general instruction following. In particular, the E4B base engages poorly with relations-only prompts — always request facts and relations together.

License and provenance

This model is a derivative of Gemma and is provided under and subject to the Gemma Terms of Use. By downloading or using this model you agree to those terms, including the Gemma Prohibited Use Policy. Downstream distributors must pass these terms on to their users.

Fine-tune, quantization, and evaluation by Pseudogiant-xr.

Downloads last month: 14

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for Pseudogiant-xr/pseudolife-extractor-gemma-4-e4b

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

(257)

this model