PEFT
Safetensors
GGUF
mistral
axolotl
Generated from Trainer
8-bit precision
bitsandbytes
conversational
Instructions to use svilupp/cheater-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use svilupp/cheater-7b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser") model = PeftModel.from_pretrained(base_model, "svilupp/cheater-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use svilupp/cheater-7b 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 svilupp/cheater-7b:Q5_K_M # Run inference directly in the terminal: llama cli -hf svilupp/cheater-7b:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf svilupp/cheater-7b:Q5_K_M # Run inference directly in the terminal: llama cli -hf svilupp/cheater-7b:Q5_K_M
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 svilupp/cheater-7b:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf svilupp/cheater-7b:Q5_K_M
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 svilupp/cheater-7b:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf svilupp/cheater-7b:Q5_K_M
Use Docker
docker model run hf.co/svilupp/cheater-7b:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use svilupp/cheater-7b with Ollama:
ollama run hf.co/svilupp/cheater-7b:Q5_K_M
- Unsloth Desktop
- Docker Model Runner
How to use svilupp/cheater-7b with Docker Model Runner:
docker model run hf.co/svilupp/cheater-7b:Q5_K_M
- Lemonade
How to use svilupp/cheater-7b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull svilupp/cheater-7b:Q5_K_M
Run and chat with the model
lemonade run user.cheater-7b-Q5_K_M
List all available models
lemonade list
- Atomic Chat
Download tokenizer.model from svilupp/cheater-7b: direct link, hf CLI and curl.
- Browser
- Download file 493 kB
-
https://huggingface.co/svilupp/cheater-7b/resolve/main/tokenizer.model
- Command line
-
hf download hf://svilupp/cheater-7b/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/svilupp/cheater-7b/resolve/main/tokenizer.model
493 kB
- Xet hash:
- 32726c94d96b1a5c442963faa3a900decf397fe67b5c530097a9f8b2506c63b6
- Size of remote file:
- 493 kB
- SHA256:
- dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
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