Text Classification
GGUF
llama.cpp
laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
feature-extraction
Instructions to use fr0stbit3/laya-gguf 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 fr0stbit3/laya-gguf 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 fr0stbit3/laya-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf fr0stbit3/laya-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf fr0stbit3/laya-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf fr0stbit3/laya-gguf:Q4_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 fr0stbit3/laya-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf fr0stbit3/laya-gguf:Q4_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 fr0stbit3/laya-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf fr0stbit3/laya-gguf:Q4_K_M
Use Docker
docker model run hf.co/fr0stbit3/laya-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use fr0stbit3/laya-gguf with Ollama:
ollama run hf.co/fr0stbit3/laya-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use fr0stbit3/laya-gguf with Docker Model Runner:
docker model run hf.co/fr0stbit3/laya-gguf:Q4_K_M
- Lemonade
How to use fr0stbit3/laya-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fr0stbit3/laya-gguf:Q4_K_M
Run and chat with the model
lemonade run user.laya-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download tokenizer_config.json from fr0stbit3/laya-gguf: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/fr0stbit3/laya-gguf/resolve/ce2afdc0a8766af56a29a22dcf4a781e1f5c7d3c/tokenizer_config.json
- Command line
-
hf download hf://fr0stbit3/laya-gguf@ce2afdc0a8766af56a29a22dcf4a781e1f5c7d3c/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/fr0stbit3/laya-gguf/resolve/ce2afdc0a8766af56a29a22dcf4a781e1f5c7d3c/tokenizer_config.json
308 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 8192, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "[UNK]" | |
| } |