Instructions to use Maelstrome/lora-wave-session with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maelstrome/lora-wave-session with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-e2b-it-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Maelstrome/lora-wave-session")

llama-cpp-python

How to use Maelstrome/lora-wave-session with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Maelstrome/lora-wave-session",
	filename="gguf/gemma-4-e2b-it.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 Maelstrome/lora-wave-session with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Maelstrome/lora-wave-session:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Maelstrome/lora-wave-session:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Maelstrome/lora-wave-session:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Maelstrome/lora-wave-session: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 Maelstrome/lora-wave-session:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Maelstrome/lora-wave-session: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 Maelstrome/lora-wave-session:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Maelstrome/lora-wave-session:Q4_K_M

Use Docker

docker model run hf.co/Maelstrome/lora-wave-session:Q4_K_M

LM Studio
Jan

vLLM

How to use Maelstrome/lora-wave-session with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Maelstrome/lora-wave-session:Q4_K_M

Ollama
How to use Maelstrome/lora-wave-session with Ollama:
```
ollama run hf.co/Maelstrome/lora-wave-session:Q4_K_M
```

Unsloth Studio

How to use Maelstrome/lora-wave-session 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 Maelstrome/lora-wave-session 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 Maelstrome/lora-wave-session to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Maelstrome/lora-wave-session to start chatting

How to use Maelstrome/lora-wave-session with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Maelstrome/lora-wave-session: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": "Maelstrome/lora-wave-session:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Maelstrome/lora-wave-session with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Maelstrome/lora-wave-session: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 Maelstrome/lora-wave-session:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Maelstrome/lora-wave-session with Docker Model Runner:
```
docker model run hf.co/Maelstrome/lora-wave-session:Q4_K_M
```

Lemonade

How to use Maelstrome/lora-wave-session with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Maelstrome/lora-wave-session:Q4_K_M

Run and chat with the model

lemonade run user.lora-wave-session-Q4_K_M

List all available models

lemonade list

Maelstrome commited on 22 days ago

Commit

e4aa4d1

verified ·

1 Parent(s): ae87a6f

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README.md CHANGED Viewed

@@ -41,13 +41,13 @@ This repo is the single home for the r16 fine-tune. Everything lives here:
 | `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`, `processor_config.json` (root) | Gemma 4 tokenizer + chat template | required for any inference path |
 | [`gguf/`](./tree/main/gguf) | Q4_K_M GGUF (~3.27 GB, single file) + Ollama Modelfile | llama.cpp / Ollama / LM Studio |
-> The `Maelstrome/lora-wave-session-gguf` sibling is **deprecated** — its contents were moved into this repo's `gguf/` subdirectory. The rank-32 variant has the same layout at [`Maelstrome/lora-wave-session-r32`](https://huggingface.co/Maelstrome/lora-wave-session-r32).
 >
-> Note: the GGUF here is a **single 3.27 GB file**, not pre-split. It works directly with llama.cpp/Ollama but **will not load in [wllama](https://github.com/ngxson/wllama)** because it exceeds the 2 GB ArrayBuffer per-file limit. If you want to run this rank-16 build in the browser, split it first with `llama-gguf-split --split-max-size 512M`. The rank-32 sibling is pre-split in its `gguf/` subdir if you'd rather just use that.
 ## Sibling runs
-This is the **rank-16 / 3-epoch RTX 5080** training of the WAVE corpus. The rank-32 / 1-epoch A100 sibling lives at [`Maelstrome/lora-wave-session-r32`](https://huggingface.co/Maelstrome/lora-wave-session-r32) and wins on every probability metric on the same frozen 428-row test split:
 | | **rank-16 (this run)** | rank-32 (sibling) |
 |---|---|---|
@@ -57,7 +57,7 @@ This is the **rank-16 / 3-epoch RTX 5080** training of the WAVE corpus. The rank
 | Mean NLL Δ vs base | **0.327 nats** | 0.508 nats |
 | Sign-test p-value | **9.5 × 10⁻⁷¹** | 2.9 × 10⁻¹²⁹ |
-See [`Maelstrome/lora-wave-session-r32-report`](https://huggingface.co/Maelstrome/lora-wave-session-r32-report) for the full head-to-head report (recipes, generation eval, reproducibility check).
 ## Provenance and intended use

 | `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`, `processor_config.json` (root) | Gemma 4 tokenizer + chat template | required for any inference path |
 | [`gguf/`](./tree/main/gguf) | Q4_K_M GGUF (~3.27 GB, single file) + Ollama Modelfile | llama.cpp / Ollama / LM Studio |
+> The previously-published `Maelstrome/lora-wave-session-gguf` sibling has been **consolidated into this repo and deleted**. The rank-32 variant has the same layout at [`Maelstrome/lora-wave-session-r32`](https://huggingface.co/Maelstrome/lora-wave-session-r32). Any external link to the old sibling URL will 404.
 >
+> **Note on browser use:** the GGUF here is a **single 3.27 GB file**, not pre-split. It works directly with llama.cpp / Ollama / LM Studio but **will not load in [wllama](https://github.com/ngxson/wllama)** because it exceeds the 2 GB-per-file `ArrayBuffer` limit. To run this r16 build in-browser, either split it first with `llama-gguf-split --split-max-size 512M` or use the [r32 sibling](https://huggingface.co/Maelstrome/lora-wave-session-r32), which ships pre-split.
 ## Sibling runs
+This is the **rank-16 / 3-epoch RTX 5080** training of the WAVE corpus. The rank-32 / 1-epoch A100 sibling lives at [`Maelstrome/lora-wave-session-r32`](https://huggingface.co/Maelstrome/lora-wave-session-r32) (same subdir layout: adapter at root, `gguf/` subdir; plus `mediapipe/` and `report/`). On the same frozen 428-row test split, r32 wins on every probability metric:
 | | **rank-16 (this run)** | rank-32 (sibling) |
 |---|---|---|
 | Mean NLL Δ vs base | **0.327 nats** | 0.508 nats |
 | Sign-test p-value | **9.5 × 10⁻⁷¹** | 2.9 × 10⁻¹²⁹ |
+Full head-to-head in [`Maelstrome/lora-wave-session-r32/report/`](https://huggingface.co/Maelstrome/lora-wave-session-r32/tree/main/report) (the comparison + run-report markdown documents).
 ## Provenance and intended use