Instructions to use fwizzer1/Fwizzer-R1-3B-RU 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 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
# Run inference directly in the terminal:
llama cli -hf fwizzer1/Fwizzer-R1-3B-RU

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

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

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

Use Docker

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

LM Studio
Jan

vLLM

How to use fwizzer1/Fwizzer-R1-3B-RU 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

fwizzer1 commited on Aug 31

Commit

3d4b97a

verified ·

1 Parent(s): 53bfeb7

Add model.yaml with reasoning: true, thinking: true, deepseek format

Browse files

Files changed (1) hide show

model.yaml +12 -9

model.yaml CHANGED Viewed

@@ -1,5 +1,12 @@
-# model.yaml is an open standard for defining cross-platform AI models
-model: fwizzer1/Fwizzer-R1-3B
 metadataOverrides:
   domain: llm
   architectures:
@@ -10,13 +17,9 @@ metadataOverrides:
     - 3B
   minMemoryUsageBytes: 2000000000
   contextLengths:
-    - 4096
-    - 32768
-    - 131072
     - 262144
-    - 524288
-    - 1048576
   vision: false
-  reasoning: false
-  thinking: false
   trainedForToolUse: true

+# model.yaml is an open standard for defining cross-platform, composable AI models
+model: fwizzer1/Fwizzer-R1-3B-RU
+displayName: Fwizzer-R1-3B-RU
+base:
+  - key: fwizzer1/Fwizzer-R1-3B-RU
+    sources:
+      - type: huggingface
+        user: fwizzer1
+        repo: Fwizzer-R1-3B-RU
 metadataOverrides:
   domain: llm
   architectures:
     - 3B
   minMemoryUsageBytes: 2000000000
   contextLengths:
     - 262144
   vision: false
+  reasoning: true
+  thinking: true
+  reasoningFormat: deepseek
   trainedForToolUse: true