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

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 7

Commit

629b050

verified ·

1 Parent(s): eed2f21

Fix Fwizzer-R1.preset.json encoding, system prompt, and reasoning parser

Browse files

Files changed (1) hide show

Fwizzer-R1.preset.json +74 -74

Fwizzer-R1.preset.json CHANGED Viewed

@@ -1,75 +1,75 @@
-{
-  "identifier": "@local:fwizzer-r1-3b-v2-speed",
-  "name": "Fwizzer-R1-v2 (Thinking & Reasoning)",
-  "changed": true,
-  "operation": {
-    "fields": [
-      {
-        "key": "llm.prediction.systemPrompt",
-        "value": "Ты — русскоязычная языковая модель Fwizzer-R1-3B-RU. Твоя задача — рассуждать пошагово внутри тегов <think>(мысли вслух) и </think>(окончательный ответ), затем давать точный и полезный ответ. Ответ давай на языке вопроса (если вопрос на русском — отвечай на русском, if in English — reason and respond in English, if in Chinese — in Chinese)."
-      },
-      {
-        "key": "llm.prediction.temperature",
-        "value": 0.6
-      },
-      {
-        "key": "llm.prediction.topP",
-        "value": 0.95
-      },
-      {
-        "key": "llm.prediction.repeatPenalty",
-        "value": 1.15
-      },
-      {
-        "key": "llm.prediction.llama.cpuThreads",
-        "value": 6
-      },
-      {
-        "key": "llm.prediction.reasoning.enableThinking",
-        "value": true
-      },
-      {
-        "key": "llm.prediction.reasoning.parsing",
-        "value": {
-          "enabled": true,
-          "startString": "<think>",
-          "endString": "</think>"
-        }
-      },
-      {
-        "key": "llm.prediction.reasoningParsing",
-        "value": {
-          "type": "tag",
-          "startTag": "<think>",
-          "endTag": "</think>"
-        }
-      },
-      {
-        "key": "llm.prediction.stopStrings",
-        "value": [
-          "</s>",
-          "[INST]",
-          "[/INST]"
-        ]
-      }
-    ]
-  },
-  "load": {
-    "fields": [
-      {
-        "key": "llm.load.contextLength",
-        "value": 8192
-      },
-      {
-        "key": "llm.load.llama.gpuOffload",
-        "value": {
-          "type": "max"
-        }
-      },
-      {
-        "key": "llm.load.llama.flashAttention",
-        "value": true
-      }
-    ]
-  }
 }

+{
+  "identifier": "@local:fwizzer-r1-3b-ru-v2",
+  "name": "Fwizzer-R1-3B-RU-v2 (Thinking)",
+  "changed": true,
+  "operation": {
+    "fields": [
+      {
+        "key": "llm.prediction.systemPrompt",
+        "value": "Ты русскоязычный ИИ-ассистент Fwizzer-R1-3B-RU. Твоя задача — рассуждать глубоко и логично внутри тегов <think>(цепочка мыслей) и </think>(ответ пользователю), решая задачи шаг за шагом. Всегда отвечай на русском языке (или на языке запроса пользователя)."
+      },
+      {
+        "key": "llm.prediction.temperature",
+        "value": 0.6
+      },
+      {
+        "key": "llm.prediction.topP",
+        "value": 0.95
+      },
+      {
+        "key": "llm.prediction.repeatPenalty",
+        "value": 1.15
+      },
+      {
+        "key": "llm.prediction.llama.cpuThreads",
+        "value": 6
+      },
+      {
+        "key": "llm.prediction.reasoning.enableThinking",
+        "value": true
+      },
+      {
+        "key": "llm.prediction.reasoning.parsing",
+        "value": {
+          "enabled": true,
+          "startString": "<think>",
+          "endString": "</think>"
+        }
+      },
+      {
+        "key": "llm.prediction.reasoningParsing",
+        "value": {
+          "type": "tag",
+          "startTag": "<think>",
+          "endTag": "</think>"
+        }
+      },
+      {
+        "key": "llm.prediction.stopStrings",
+        "value": [
+          "</s>",
+          "[INST]",
+          "[/INST]"
+        ]
+      }
+    ]
+  },
+  "load": {
+    "fields": [
+      {
+        "key": "llm.load.contextLength",
+        "value": 8192
+      },
+      {
+        "key": "llm.load.llama.gpuOffload",
+        "value": {
+          "type": "max"
+        }
+      },
+      {
+        "key": "llm.load.llama.flashAttention",
+        "value": true
+      }
+    ]
+  }
 }