Text Generation
Safetensors
GGUF
Russian
English
mistral3
reasoning
deepseek-r1
ru-deepthink-11k
mistral
conversational
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
- llama.cpp
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
Download adapter_config.json from fwizzer1/Fwizzer-R1-3B-RU: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
-
https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU/resolve/main/adapter_config.json
- Command line
-
hf download hf://fwizzer1/Fwizzer-R1-3B-RU/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/fwizzer1/Fwizzer-R1-3B-RU/resolve/main/adapter_config.json
1.5 kB
| { | |
| "alora_invocation_tokens": null, | |
| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": { | |
| "base_model_class": "Mistral3ForConditionalGeneration", | |
| "parent_library": "transformers.models.mistral3.modeling_mistral3", | |
| "unsloth_fixed": true | |
| }, | |
| "base_model_name_or_path": "unsloth/Ministral-3-3B-Instruct-2512-bnb-4bit", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_bias": false, | |
| "lora_dropout": 0, | |
| "lora_ga_config": null, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "peft_version": "0.19.1", | |
| "qalora_group_size": 16, | |
| "r": 16, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": "(?:.*?(?:language|text).*?(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?(?:q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj))|(?:\\bmodel\\.layers\\.[\\d]{1,}\\.(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer)\\.(?:(?:q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj)))", | |
| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
| "trainable_token_indices": null, | |
| "use_bdlora": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false | |
| } |