Instructions to use aaronthomas123/lxask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aaronthomas123/lxask 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 aaronthomas123/lxask:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aaronthomas123/lxask:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aaronthomas123/lxask:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aaronthomas123/lxask:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf aaronthomas123/lxask:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf aaronthomas123/lxask:Q4_K_M

Use Docker

docker model run hf.co/aaronthomas123/lxask:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use aaronthomas123/lxask with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aaronthomas123/lxask"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aaronthomas123/lxask",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aaronthomas123/lxask:Q4_K_M

Ollama
How to use aaronthomas123/lxask with Ollama:
```
ollama run hf.co/aaronthomas123/lxask:Q4_K_M
```
Unsloth Desktop

How to use aaronthomas123/lxask with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aaronthomas123/lxask:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "aaronthomas123/lxask:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use aaronthomas123/lxask with Docker Model Runner:
```
docker model run hf.co/aaronthomas123/lxask:Q4_K_M
```

Lemonade

How to use aaronthomas123/lxask with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aaronthomas123/lxask:Q4_K_M

Run and chat with the model

lemonade run user.lxask-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use aaronthomas123/lxask with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aaronthomas123/lxask: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 aaronthomas123/lxask:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use aaronthomas123/lxask with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aaronthomas123/lxask: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 "aaronthomas123/lxask: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"

aaronthomas123 commited on 21 days ago

Commit

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README.md +18 -1

README.md CHANGED Viewed

@@ -84,4 +84,21 @@ https://github.com/arxhr007/lxask/blob/main/docs/results.md
 ## Benchmark
-InterCode-ALFA results: to be added.

 ## Benchmark
+The public [NL2SH-ALFA](https://huggingface.co/datasets/westenfelder/NL2SH-ALFA) test
+set: 300 shell tasks, each answer executed in Docker by the InterCode-ALFA harness and
+scored by its embedding judge (local model, threshold 0.75).
+| | solved | 95% CI |
+|---|---|---|
+| **lxask (model + pipeline)** | **187/300 (62%)** | 57-68% |
+| this base model, untuned | 92/300 (31%) | 26-36% |
+| the set's own reference commands | 299/300 (99.7%) | |
+63 of the 300 tasks overlap the training sources; on the 237 without overlap it is 61%
+against 34% (McNemar p = 7.7e-14; full set p = 1.2e-20). Keeping the first answer (no
+retries) or removing retrieval moves at most one task, so this score is the fine-tune's,
+not the pipeline's checks. Other tools publish 0.62-0.66 on this set with their own
+answer protocols, not directly comparable: the same untuned base scores 31% here
+against 54% reported there. The full record, including the judge's context-error caveat
+and the difficulty split:
+https://arxhr007.github.io/lxask/accuracy.html#the-public-benchmark-nl2sh-alfa