Text Classification
PEFT
Safetensors
GGUF
decision-model
system-one
pointer-head
lora
gemma-3
typed-decisions
noul
choice
score
Eval Results (legacy)
Instructions to use hiteshluke/arbiter-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hiteshluke/arbiter-4b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-4b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "hiteshluke/arbiter-4b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hiteshluke/arbiter-4b 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 hiteshluke/arbiter-4b:Q4_K_M # Run inference directly in the terminal: llama cli -hf hiteshluke/arbiter-4b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hiteshluke/arbiter-4b:Q4_K_M # Run inference directly in the terminal: llama cli -hf hiteshluke/arbiter-4b:Q4_K_M
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 hiteshluke/arbiter-4b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf hiteshluke/arbiter-4b:Q4_K_M
Use Docker
docker model run hf.co/hiteshluke/arbiter-4b:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use hiteshluke/arbiter-4b with Ollama:
ollama run hf.co/hiteshluke/arbiter-4b:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use hiteshluke/arbiter-4b with Docker Model Runner:
docker model run hf.co/hiteshluke/arbiter-4b:Q4_K_M
- Lemonade
How to use hiteshluke/arbiter-4b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hiteshluke/arbiter-4b:Q4_K_M
Run and chat with the model
lemonade run user.arbiter-4b-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download tokenizer.model from hiteshluke/arbiter-4b: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/hiteshluke/arbiter-4b/resolve/main/tokenizer.model
- Command line
-
hf download hf://hiteshluke/arbiter-4b/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/hiteshluke/arbiter-4b/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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