Instructions to use fwizzer1/Fwizzer-R1-3B-RU-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2
# Run inference directly in the terminal:
llama cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2
# Run inference directly in the terminal:
llama cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2

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 fwizzer1/Fwizzer-R1-3B-RU-v2
# Run inference directly in the terminal:
./llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2

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 fwizzer1/Fwizzer-R1-3B-RU-v2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Use Docker

docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2

Llama new
LM Studio
Jan

vLLM

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fwizzer1/Fwizzer-R1-3B-RU-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fwizzer1/Fwizzer-R1-3B-RU-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2

Ollama
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Ollama:
```
ollama run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
```
Unsloth Desktop

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "fwizzer1/Fwizzer-R1-3B-RU-v2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Docker Model Runner:
```
docker model run hf.co/fwizzer1/Fwizzer-R1-3B-RU-v2
```

Lemonade

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fwizzer1/Fwizzer-R1-3B-RU-v2

Run and chat with the model

lemonade run user.Fwizzer-R1-3B-RU-v2-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2

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 fwizzer1/Fwizzer-R1-3B-RU-v2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use fwizzer1/Fwizzer-R1-3B-RU-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fwizzer1/Fwizzer-R1-3B-RU-v2

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "fwizzer1/Fwizzer-R1-3B-RU-v2" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

fwizzer1 commited on Sep 5

Commit

4c49de9

verified ·

1 Parent(s): 8096e8a

Clean up old v54 debug log

Browse files

Files changed (1) hide show

kaggle_debug_error.log +0 -46

kaggle_debug_error.log DELETED Viewed

@@ -1,46 +0,0 @@
-Stage: Cell 2 - Model & LoRA Setup
-Error: CUDA error: no kernel image is available for execution on the device
-Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
-CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
-For debugging consider passing CUDA_LAUNCH_BLOCKING=1
-Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
-Traceback:
-Traceback (most recent call last):
-  File "/tmp/ipykernel_23/2830776181.py", line 46, in <cell line: 0>
-    model = FastLanguageModel.get_peft_model(
-            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/usr/local/lib/python3.12/dist-packages/unsloth_zoo/temporary_patches/moe_grouped_modulelist.py", line 382, in wrapper
-    result = func(*args, **kwargs)
-             ^^^^^^^^^^^^^^^^^^^^^
-  File "/usr/local/lib/python3.12/dist-packages/unsloth/models/llama.py", line 3059, in get_peft_model
-    return FastBaseModel.get_peft_model(
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/usr/local/lib/python3.12/dist-packages/unsloth_zoo/temporary_patches/moe_grouped_modulelist.py", line 382, in wrapper
-    result = func(*args, **kwargs)
-             ^^^^^^^^^^^^^^^^^^^^^
-  File "/usr/local/lib/python3.12/dist-packages/unsloth/models/vision.py", line 2417, in get_peft_model
-    model = _get_peft_model(model, lora_config)
-            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/kaggle/working/unsloth_compiled_cache/moe_utils.py", line 1380, in patched_get_peft_model
-    peft_model = original_get_peft_model(model, *args, **kwargs)
-                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/usr/local/lib/python3.12/dist-packages/peft/mapping_func.py", line 122, in get_peft_model
-    return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/usr/local/lib/python3.12/dist-packages/peft/peft_model.py", line 1955, in __init__
-    super().__init__(model, peft_config, adapter_name, **kwargs)
-  File "/usr/local/lib/python3.12/dist-packages/peft/peft_model.py", line 132, in __init__
-    self.base_model._cast_adapter_dtype(
-  File "/usr/local/lib/python3.12/dist-packages/peft/tuners/tuners_utils.py", line 599, in _cast_adapter_dtype
-    cast_adapter_dtype(self.model, adapter_name=adapter_name, autocast_adapter_dtype=autocast_adapter_dtype)
-  File "/usr/local/lib/python3.12/dist-packages/peft/tuners/tuners_utils.py", line 2196, in cast_adapter_dtype
-    param.data = param.data.to(torch.float32)
-                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-torch.AcceleratorError: CUDA error: no kernel image is available for execution on the device
-Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
-CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
-For debugging consider passing CUDA_LAUNCH_BLOCKING=1
-Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.