Instructions to use fjmgAI/b1-R1-Zero-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fjmgAI/b1-R1-Zero-3B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fjmgAI/b1-R1-Zero-3B-GGUF", dtype="auto")

llama-cpp-python

How to use fjmgAI/b1-R1-Zero-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fjmgAI/b1-R1-Zero-3B-GGUF",
	filename="unsloth.BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fjmgAI/b1-R1-Zero-3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16

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 fjmgAI/b1-R1-Zero-3B-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16

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 fjmgAI/b1-R1-Zero-3B-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16

Use Docker

docker model run hf.co/fjmgAI/b1-R1-Zero-3B-GGUF:BF16

LM Studio
Jan
Ollama
How to use fjmgAI/b1-R1-Zero-3B-GGUF with Ollama:
```
ollama run hf.co/fjmgAI/b1-R1-Zero-3B-GGUF:BF16
```

Unsloth Studio

How to use fjmgAI/b1-R1-Zero-3B-GGUF 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 fjmgAI/b1-R1-Zero-3B-GGUF 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 fjmgAI/b1-R1-Zero-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fjmgAI/b1-R1-Zero-3B-GGUF to start chatting

How to use fjmgAI/b1-R1-Zero-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16

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": "fjmgAI/b1-R1-Zero-3B-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use fjmgAI/b1-R1-Zero-3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf fjmgAI/b1-R1-Zero-3B-GGUF:BF16

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 fjmgAI/b1-R1-Zero-3B-GGUF:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use fjmgAI/b1-R1-Zero-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/fjmgAI/b1-R1-Zero-3B-GGUF:BF16
```

Lemonade

How to use fjmgAI/b1-R1-Zero-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fjmgAI/b1-R1-Zero-3B-GGUF:BF16

Run and chat with the model

lemonade run user.b1-R1-Zero-3B-GGUF-BF16

List all available models

lemonade list

fjmgAI commited on Mar 17, 2025

Commit

ce89a60

verified ·

1 Parent(s): 56668c0

Update README.md

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@@ -9,14 +9,42 @@ tags:
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded  model
-- **Developed by:** byondAI
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 license: apache-2.0
 language:
 - en
+- es
+datasets:
+- Kukedlc/dpo-orpo-spanish-15k
+library_name: transformers
 ---
+[<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/67b2f4e49edebc815a3a4739/R1g957j1aBbx8lhZbWmxw.jpeg" width="200"/>](https://huggingface.co/fjmgAI)
+## Fine-Tuned Model
+**`fjmgAI/b1-R1-Zero-3B-GGUF`**
+## Base Model
+**`unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit`**
+## Fine-Tuning Method
+Fine-tuning was performed using **[`unsloth`](https://github.com/unslothai/unsloth)**, an efficient fine-tuning framework optimized for low-resource environments and Huggingface's TRL library.
+## Dataset
+**[`Kukedlc/dpo-orpo-spanish-15k`](https://huggingface.co/datasets/Kukedlc/dpo-orpo-spanish-15k)**
+### Description
+A Spanish-language dataset containing **15,000 examples**, designed for **Direct Preference Optimization (DPO)** or **Outcome-Regularized Preference Optimization (ORPO).**
+### Adaptation
+The dataset was adapted to a reasoning-based format for GPRO, enhancing its ability to guide preference-based decision-making during fine-tuning. This adaptation ensures better alignment with instruction-following tasks in Spanish.
+## Fine-Tuning Details
+- The model was trained using the **GPRO algorithm**, leveraging structured preference data to refine its response generation.
+- The model was fine-tuned to maintain its **4-bit quantization (`bnb-4bit`)** for memory efficiency while aligning its outputs with the characteristics of the Spanish dataset.
+- The focus was on retaining the model's **instructional abilities** while improving its **understanding and generation** of Spanish text.
+## Purpose
+This fine-tuned model is intended for **Spanish-language applications**  that require efficient AI that follows instructions using a **lightweight reasoning process.**
+- **Developed by:** fjmgAI
+- **License:** apache-2.0
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)  [<img src="https://camo.githubusercontent.com/9585eb3e70c8138cbc0f73de7e970be4c668e957e45d16fc3ee6687fcc1da905/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f74726c2d6c69622f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f74726c5f62616e6e65725f6461726b2e706e67" width="200"/>](https://github.com/huggingface/trl?tab=readme-ov-file)