Text Generation
GGUF
English
liquid
lfm3
looped-transformer
mhc
hyper-connections
expert-iteration
rest-em
reasoning
llama-cpp
lm-studio
custom-runtime
conversational
Instructions to use notfakerv/round2-1.7b-loop-preview 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 notfakerv/round2-1.7b-loop-preview 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 notfakerv/round2-1.7b-loop-preview:BF16 # Run inference directly in the terminal: llama cli -hf notfakerv/round2-1.7b-loop-preview:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf notfakerv/round2-1.7b-loop-preview:BF16 # Run inference directly in the terminal: llama cli -hf notfakerv/round2-1.7b-loop-preview:BF16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf notfakerv/round2-1.7b-loop-preview:BF16 # Run inference directly in the terminal: ./build/bin/llama cli -hf notfakerv/round2-1.7b-loop-preview:BF16
Use Docker
docker model run hf.co/notfakerv/round2-1.7b-loop-preview:BF16
- Llama new
- LM Studio
- Jan
- vLLM
How to use notfakerv/round2-1.7b-loop-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "notfakerv/round2-1.7b-loop-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "notfakerv/round2-1.7b-loop-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/notfakerv/round2-1.7b-loop-preview:BF16
- Ollama
How to use notfakerv/round2-1.7b-loop-preview with Ollama:
ollama run hf.co/notfakerv/round2-1.7b-loop-preview:BF16
- Unsloth Desktop
- Pi
How to use notfakerv/round2-1.7b-loop-preview with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf notfakerv/round2-1.7b-loop-preview:BF16
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": "notfakerv/round2-1.7b-loop-preview:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use notfakerv/round2-1.7b-loop-preview with Docker Model Runner:
docker model run hf.co/notfakerv/round2-1.7b-loop-preview:BF16
- Lemonade
How to use notfakerv/round2-1.7b-loop-preview with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull notfakerv/round2-1.7b-loop-preview:BF16
Run and chat with the model
lemonade run user.round2-1.7b-loop-preview-BF16
List all available models
lemonade list
- Hermes Agent
How to use notfakerv/round2-1.7b-loop-preview with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf notfakerv/round2-1.7b-loop-preview:BF16
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 notfakerv/round2-1.7b-loop-preview:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use notfakerv/round2-1.7b-loop-preview with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf notfakerv/round2-1.7b-loop-preview:BF16
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 "notfakerv/round2-1.7b-loop-preview:BF16" \ --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"
Download validation/regression.json from notfakerv/round2-1.7b-loop-preview: direct link, hf CLI and curl.
- Browser
- Download file 706 Bytes
-
https://huggingface.co/notfakerv/round2-1.7b-loop-preview/resolve/main/validation/regression.json
- Command line
-
hf download hf://notfakerv/round2-1.7b-loop-preview/validation/regression.json
-
curl -L -o regression.json https://huggingface.co/notfakerv/round2-1.7b-loop-preview/resolve/main/validation/regression.json
706 Bytes
| {"note": "Other architectures after patch 02. The test suite ran on CPU builds of the trees before and after patch 02; real models ran greedy decoding (64 tokens) with LM Studio's CUDA 12 runtime libraries and each libllama.", "test_llama_archs": {"tests": 175, "architectures": 162, "passed_before": 175, "passed_after": 175, "identical_rows": 378, "excluded": ["clover_moe", "sensenova_loop"], "excluded_reason": "their fixtures fail to load in both builds"}, "real_models_identical_greedy_tokens": ["LFM3-1.7B Q8_0 (stock)", "LFM3 Loop 1.7B SFT 345 BF16", "LFM3 Loop CAMEL Physics SFT NVFP4", "NVIDIA Nemotron Labs Dory1.0 4B Q5_K_M", "SenseNova-Loop-9B-Preview Q8_0", "Clover-35B-A3B-Preview Q4_K_M"]} | |