Instructions to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning",
	filename="gemma-2b-reasoning.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 ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning
# Run inference directly in the terminal:
llama-cli -hf ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning
# Run inference directly in the terminal:
llama-cli -hf ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

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 ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning
# Run inference directly in the terminal:
./llama-cli -hf ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

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 ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

Use Docker

docker model run hf.co/ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

LM Studio
Jan

vLLM

How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

Ollama
How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with Ollama:
```
ollama run hf.co/ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning
```

Unsloth Studio new

How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning 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 ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning 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 ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning to start chatting

MLX LM

How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with Docker Model Runner:
```
docker model run hf.co/ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning
```

Lemonade

How to use ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

Run and chat with the model

lemonade run user.gemma-2-2b-it-R1-Reasoning-{{QUANT_TAG}}

List all available models

lemonade list

Model Summary

This model is a fine-tuned version of gemma-2-2b-it, optimized for instruction-following and reasoning tasks. It was trained using MLX and LoRA on the sequelbox/Raiden-DeepSeek-R1 dataset, which consists of 62.9k examples generated by Deepseek R1. The fine-tuning process ran for 600 iterations to enhance the model’s ability to reason through more complex problems.

Model Details

Base Model: gemma-2-2b-it
Fine-tuning Method: MLX + LoRA
Dataset: sequelbox/Raiden-DeepSeek-R1
Iterations: 600

Capabilities

This model improves upon gemma-2-2b-it with additional instruction-following and reasoning capabilities derived from Deepseek R1-generated examples. The model will answer questions with a straight-forward answer for simple questions, and generate long chain-of-thought reasoning text for more complex problems. It is well-suited for:

Question answering
Reasoning-based tasks
Coding
Running on consumer hardware

Limitations

Sometimes chain-of-thought reasoning is not triggered for more complex problems when it probably should be. You can nudge the model if needed by simply asking it to show its thoughts and it will generate think tags and begin reasoning.
With harder than average complex reasoning problems, the model can get stuck in long "thinking" thought loops without ever coming to a conclusive answer.

Downloads last month: 39

Safetensors

Model size

3B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Quantized

(177)

this model

ApatheticWithoutTheA
/

gemma-2-2b-it-R1-Reasoning

Model Summary

Model Details

Capabilities

Limitations

Model tree for ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning

Dataset used to train ApatheticWithoutTheA/gemma-2-2b-it-R1-Reasoning