Instructions to use saxon11/llama3.1-8b-classifier-josh-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saxon11/llama3.1-8b-classifier-josh-ds with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="saxon11/llama3.1-8b-classifier-josh-ds")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("saxon11/llama3.1-8b-classifier-josh-ds", dtype="auto")

llama-cpp-python

How to use saxon11/llama3.1-8b-classifier-josh-ds with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saxon11/llama3.1-8b-classifier-josh-ds",
	filename="meta-llama-3.1-8b-instruct.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "\"I like you. I love you\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saxon11/llama3.1-8b-classifier-josh-ds with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0
# Run inference directly in the terminal:
llama-cli -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0
# Run inference directly in the terminal:
llama-cli -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

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 saxon11/llama3.1-8b-classifier-josh-ds:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

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 saxon11/llama3.1-8b-classifier-josh-ds:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

Use Docker

docker model run hf.co/saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

LM Studio
Jan
Ollama
How to use saxon11/llama3.1-8b-classifier-josh-ds with Ollama:
```
ollama run hf.co/saxon11/llama3.1-8b-classifier-josh-ds:Q8_0
```

Unsloth Studio

How to use saxon11/llama3.1-8b-classifier-josh-ds 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 saxon11/llama3.1-8b-classifier-josh-ds 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 saxon11/llama3.1-8b-classifier-josh-ds to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saxon11/llama3.1-8b-classifier-josh-ds to start chatting

How to use saxon11/llama3.1-8b-classifier-josh-ds with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

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": "saxon11/llama3.1-8b-classifier-josh-ds:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use saxon11/llama3.1-8b-classifier-josh-ds with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

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 saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use saxon11/llama3.1-8b-classifier-josh-ds with Docker Model Runner:
```
docker model run hf.co/saxon11/llama3.1-8b-classifier-josh-ds:Q8_0
```

Lemonade

How to use saxon11/llama3.1-8b-classifier-josh-ds with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saxon11/llama3.1-8b-classifier-josh-ds:Q8_0

Run and chat with the model

lemonade run user.llama3.1-8b-classifier-josh-ds-Q8_0

List all available models

lemonade list

Llama 3.1 8B — Josh/DS Question Classifier

A fine-tuned Llama 3.1 8B Instruct model that classifies incoming questions as either JOSH_DS (related to Josh Janzen or Data Science/AI) or OFF_TOPIC.

Built as the routing layer for a multi-model virtual assistant architecture on joshjanzen.com.

Training

Method: GRPO (Group Relative Policy Optimization) via Unsloth
Base model: meta-llama/Llama-3.1-8B-Instruct
Dataset: 110 labeled examples (68 JOSH_DS, 42 OFF_TOPIC)
Best checkpoint: Step 170 (reward 2.652)
Hardware: NVIDIA RTX 5090 (32 GB)
Quantization: Q8_0 (~8 GB)

Reward Functions

Function	Weight	Purpose
XML tag structure	0.5	Proper `<reasoning>` and `<classification>` tags
Format match	0.5	Complete XML output format
Valid label	0.5	Only "JOSH_DS" or "OFF_TOPIC"
Correctness	2.0	Matches ground truth label

Usage with Ollama

# Download the GGUF and Modelfile, then:
ollama create josh-classifier -f Modelfile
ollama run josh-classifier "What is RAG in AI?"

Expected Output

<reasoning>This question is about Retrieval-Augmented Generation, a core AI/ML topic
in Josh's expertise.</reasoning><classification>JOSH_DS</classification>

System Prompt

The model expects this system prompt (included in the Modelfile):

You are a question classifier for Josh Janzen's virtual assistant.

Your task is to classify incoming questions into one of two categories:
- JOSH_DS: Questions about Josh Janzen, his background, his projects, OR questions about Data Science, AI, Machine Learning, LLMs, or related technical topics
- OFF_TOPIC: Questions not related to Josh or Data Science/AI topics

JOSH'S EXPERTISE AREAS (classify as JOSH_DS if related to any of these):
1. LLM Engineering: building LLMs, transformers, fine-tuning (LoRA, QLoRA, RLHF), PyTorch
2. Self-Hosting LLMs: Ollama, Qwen, Llama, GPU inference, Hugging Face
3. Agentic AI: LangChain, LangGraph, OpenAI Agents SDK, tool calling, MCP
4. RAG: pipelines, chunking, embeddings, ChromaDB, FAISS, semantic search
5. Production AI Deployment: FastAPI, Docker, CI/CD, GCP, Azure, AWS
6. Full-Stack AI Apps: Streamlit, Gradio, BigQuery, VertexAI
7. AI Security: prompt injection, observability, LangSmith
8. Business AI Strategy, Supply Chain AI, CPG/Food Industry AI
9. Nutrition/Fitness AI, USDA data integration
10. Vector Databases and Semantic Search
11. Tech Career Development and AI Learning Paths
12. Data Science and Analytics
13. Josh's personal background, projects, experience at C.H. Robinson or Hormel Foods

IMPORTANT:
- Casual greetings (hello, hi, hey) -> JOSH_DS (so Josh can greet warmly)
- General programming questions related to AI/ML -> JOSH_DS
- Questions about cooking recipes, sports scores, celebrities, etc. -> OFF_TOPIC

Think briefly about why the question fits a category, then provide your classification.
Place your reasoning between <reasoning> and </reasoning>.
Place your final classification (JOSH_DS or OFF_TOPIC) between <classification> and </classification>.

Architecture

User Question
      |
[This Model: Llama 3.1 8B Classifier]
      |
  JOSH_DS ──> Main LLM (Qwen 30B + context)
  OFF_TOPIC ──> Polite decline

Limitations

Trained on 110 examples — may not generalize perfectly to edge cases
Designed specifically for Josh Janzen's assistant; not a general-purpose classifier

Downloads last month: 1

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

8-bit

Model tree for saxon11/llama3.1-8b-classifier-josh-ds

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(948)

this model