Instructions to use robertolofaro/articles-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use robertolofaro/articles-model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="robertolofaro/articles-model",
	filename="articles-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 robertolofaro/articles-model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf robertolofaro/articles-model:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf robertolofaro/articles-model:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/robertolofaro/articles-model:Q4_K_M

LM Studio
Jan

vLLM

How to use robertolofaro/articles-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/robertolofaro/articles-model:Q4_K_M

Ollama
How to use robertolofaro/articles-model with Ollama:
```
ollama run hf.co/robertolofaro/articles-model:Q4_K_M
```

Unsloth Studio new

How to use robertolofaro/articles-model 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 robertolofaro/articles-model 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 robertolofaro/articles-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for robertolofaro/articles-model to start chatting

Pi new

How to use robertolofaro/articles-model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf robertolofaro/articles-model: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": "robertolofaro/articles-model:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use robertolofaro/articles-model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf robertolofaro/articles-model: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 robertolofaro/articles-model:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use robertolofaro/articles-model with Docker Model Runner:
```
docker model run hf.co/robertolofaro/articles-model:Q4_K_M
```

Lemonade

How to use robertolofaro/articles-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull robertolofaro/articles-model:Q4_K_M

Run and chat with the model

lemonade run user.articles-model-Q4_K_M

List all available models

lemonade list

robertolofaro commited on 10 days ago

Commit

a88c391

verified ·

1 Parent(s): 84f64c0

Delete qa_markdown_fast.py

Browse files

Files changed (1) hide show

qa_markdown_fast.py +0 -28

qa_markdown_fast.py DELETED Viewed

@@ -1,28 +0,0 @@
-#!/usr/bin/env python3
-from qa_common import parse_args, build_prompt, generate_answer, save_result
-from llama_cpp import Llama
-# ====================== CONFIG ======================
-MODEL_PATH = "articles-Q4_K_M.gguf"
-print("Loading GGUF model...")
-llm = Llama(
-    model_path=MODEL_PATH,
-    n_ctx=8192,
-    n_threads=8,
-    verbose=False,
-)
-def answer(query: str):
-    prompt = build_prompt(query, context="")   # No context = pure model
-    return generate_answer(llm, prompt, max_tokens=1100)
-if __name__ == "__main__":
-    args = parse_args()
-    query = args.prompt if args.prompt else input("\nQuestion: ")
-    print("Generating answer using fine-tuned model (Fast Mode)...\n")
-    answer_text = answer(query)
-    save_result(query, answer_text, args.output)