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

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

File size: 909 Bytes

ab5de76

{
    "architectures": [
        "Gemma2ForCausalLM"
    ],
    "attention_bias": false,
    "attention_dropout": 0.0,
    "attn_logit_softcapping": 50.0,
    "bos_token_id": 2,
    "cache_implementation": "hybrid",
    "eos_token_id": [
        1,
        107
    ],
    "final_logit_softcapping": 30.0,
    "head_dim": 256,
    "hidden_act": "gelu_pytorch_tanh",
    "hidden_activation": "gelu_pytorch_tanh",
    "hidden_size": 2304,
    "initializer_range": 0.02,
    "intermediate_size": 9216,
    "max_position_embeddings": 8192,
    "model_type": "gemma2",
    "num_attention_heads": 8,
    "num_hidden_layers": 26,
    "num_key_value_heads": 4,
    "pad_token_id": 0,
    "query_pre_attn_scalar": 256,
    "rms_norm_eps": 1e-06,
    "rope_theta": 10000.0,
    "sliding_window": 4096,
    "torch_dtype": "bfloat16",
    "transformers_version": "4.42.4",
    "use_cache": true,
    "vocab_size": 256000
}