Instructions to use ruv/ruvltra-claude-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ruv/ruvltra-claude-code with MambaSSM:

from mamba_ssm import MambaLMHeadModel

model = MambaLMHeadModel.from_pretrained("ruv/ruvltra-claude-code")

llama-cpp-python

How to use ruv/ruvltra-claude-code with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ruv/ruvltra-claude-code",
	filename="ruvltra-claude-code-0.5b-q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ruv/ruvltra-claude-code with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ruv/ruvltra-claude-code:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ruv/ruvltra-claude-code:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ruv/ruvltra-claude-code:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ruv/ruvltra-claude-code: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 ruv/ruvltra-claude-code:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ruv/ruvltra-claude-code: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 ruv/ruvltra-claude-code:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ruv/ruvltra-claude-code:Q4_K_M

Use Docker

docker model run hf.co/ruv/ruvltra-claude-code:Q4_K_M

LM Studio
Jan

vLLM

How to use ruv/ruvltra-claude-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ruv/ruvltra-claude-code:Q4_K_M

Ollama
How to use ruv/ruvltra-claude-code with Ollama:
```
ollama run hf.co/ruv/ruvltra-claude-code:Q4_K_M
```

Unsloth Studio new

How to use ruv/ruvltra-claude-code 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 ruv/ruvltra-claude-code 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 ruv/ruvltra-claude-code to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ruv/ruvltra-claude-code to start chatting

Docker Model Runner
How to use ruv/ruvltra-claude-code with Docker Model Runner:
```
docker model run hf.co/ruv/ruvltra-claude-code:Q4_K_M
```

Lemonade

How to use ruv/ruvltra-claude-code with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ruv/ruvltra-claude-code:Q4_K_M

Run and chat with the model

lemonade run user.ruvltra-claude-code-Q4_K_M

List all available models

lemonade list

ruv commited on Mar 28

Commit

42aa769

verified ·

1 Parent(s): f886857

Add L4 GPU benchmark results (67.1 tok/s)

Browse files

Files changed (1) hide show

README.md +15 -0

README.md CHANGED Viewed

@@ -471,3 +471,18 @@ let scores = compressor.inner_product_batch_optimized(&query, &compressed)?;
 - **Speculative Decoding** — 2-3x generation speedup
 [RuVector GitHub](https://github.com/ruvnet/ruvector) | [ruvllm crate](https://crates.io/crates/ruvllm) | [@ruvector/ruvllm npm](https://www.npmjs.com/package/@ruvector/ruvllm)

 - **Speculative Decoding** — 2-3x generation speedup
 [RuVector GitHub](https://github.com/ruvnet/ruvector) | [ruvllm crate](https://crates.io/crates/ruvllm) | [@ruvector/ruvllm npm](https://www.npmjs.com/package/@ruvector/ruvllm)
+---
+## Benchmarks (L4 GPU, 24GB VRAM)
+| Metric | Result |
+|--------|--------|
+| **Inference Speed** | 67.1 tok/s |
+| **Model Load Time** | 2.35s |
+| **Parameters** | 0.5B |
+| **TurboQuant KV (3-bit)** | 10.7x compression, <1% PPL loss |
+| **TurboQuant KV (4-bit)** | 8x compression, <0.5% PPL loss |
+*Benchmarked on Google Cloud L4 GPU via `ruvltra-calibration` Cloud Run Job (2026-03-28)*