Instructions to use jjee2/lora_recycle 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 jjee2/lora_recycle 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 jjee2/lora_recycle:TQ2_0 # Run inference directly in the terminal: llama cli -hf jjee2/lora_recycle:TQ2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jjee2/lora_recycle:TQ2_0 # Run inference directly in the terminal: llama cli -hf jjee2/lora_recycle:TQ2_0
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 jjee2/lora_recycle:TQ2_0 # Run inference directly in the terminal: ./llama-cli -hf jjee2/lora_recycle:TQ2_0
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 jjee2/lora_recycle:TQ2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jjee2/lora_recycle:TQ2_0
Use Docker
docker model run hf.co/jjee2/lora_recycle:TQ2_0
- LM Studio
- Jan
- Ollama
How to use jjee2/lora_recycle with Ollama:
ollama run hf.co/jjee2/lora_recycle:TQ2_0
- Unsloth Studio
How to use jjee2/lora_recycle with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jjee2/lora_recycle to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jjee2/lora_recycle to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jjee2/lora_recycle to start chatting
- Atomic Chat new
- Docker Model Runner
How to use jjee2/lora_recycle with Docker Model Runner:
docker model run hf.co/jjee2/lora_recycle:TQ2_0
- Lemonade
How to use jjee2/lora_recycle with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jjee2/lora_recycle:TQ2_0
Run and chat with the model
lemonade run user.lora_recycle-TQ2_0
List all available models
lemonade list
Ctrl+K
- 0x1202__dbcb6a16-0f63-4894-8b46-53fad71d6c73
- AlberBshara__outputs
- Canarie__Soaring-8b-lora
- EdBergJr__tablets_baha_arabic
- GaetanMichelet__Llama-31-8B_task-2_120-samples_config-1
- GaetanMichelet__Llama-31-8B_task-2_120-samples_config-1_auto
- GaetanMichelet__Llama-31-8B_task-2_120-samples_config-2_auto
- GaetanMichelet__Llama-31-8B_task-2_120-samples_config-3
- GaetanMichelet__Llama-31-8B_task-2_120-samples_config-4
- GaetanMichelet__Llama-31-8B_task-2_180-samples_config-1_auto
- GaetanMichelet__Llama-31-8B_task-2_180-samples_config-2_auto
- GaetanMichelet__Llama-31-8B_task-2_180-samples_config-3
- GaetanMichelet__Llama-31-8B_task-2_180-samples_config-4
- GaetanMichelet__Llama-31-8B_task-2_60-samples_config-1
- GaetanMichelet__Llama-31-8B_task-2_60-samples_config-1_auto
- GaetanMichelet__Llama-31-8B_task-2_60-samples_config-2_auto
- GaetanMichelet__Llama-31-8B_task-2_60-samples_config-3
- GaetanMichelet__Llama-31-8B_task-2_60-samples_config-4
- GaetanMichelet__Llama-31-8B_task-3_120-samples_config-1
- GaetanMichelet__Llama-31-8B_task-3_120-samples_config-1_auto
- GaetanMichelet__Llama-31-8B_task-3_120-samples_config-2_auto
- GaetanMichelet__Llama-31-8B_task-3_180-samples_config-1
- GaetanMichelet__Llama-31-8B_task-3_180-samples_config-1_auto
- GaetanMichelet__Llama-31-8B_task-3_180-samples_config-2
- GaetanMichelet__Llama-31-8B_task-3_60-samples_config-1
- GaetanMichelet__Llama-31-8B_task-3_60-samples_config-1_auto
- GaetanMichelet__Llama-31-8B_task-3_60-samples_config-2
- GaetanMichelet__Llama-31-8B_task-3_60-samples_config-2_auto
- GaetanMichelet__Llama-31-8B_task-3_60-samples_config-4
- Hiranmai49__Llama-3.1-8B-Instruct-contracts-ContinuedPretrainingLlama3.18b
- Hiranmai49__Llama-3.1-8B-Instruct-contracts-pretrainLlama3.18b
- LangAGI-Lab__M2WEB-HTML-VF
- LangAGI-Lab__M2WEB-HTML-WM
- LangAGI-Lab__Meta-Llama-3.1-8B-Instruct-WM-acctree-16k-25-adapter-new
- LangAGI-Lab__Meta-Llama-3.1-8B-Instruct-WM-webarena-16k-25-adapter
- LangAGI-Lab__Meta-Llama-3.1-8B-Instruct-WM-webarena-16k-50-adapter
- RAG-Gym__Direct-MedQA-SFT
- Rodo-Sami__38c537af-903d-4208-9186-707aa28f3a5b
- Xeno-nim__Xenobot-Bully
- aleegis10__8ed9fe91-c2b5-42ca-8efb-927b4c8fbf45
- andrewzamai__LLaMA_3_8B_CNADFTD_3_classes_equally_represented_0234_lora
- andrewzamai__Llama-3.1-8B-Instruct-CNADFTD-ADNI2NIFD-AN-fold-0-gathered-equally-represented-0234-v2
- aravind-selvam__Llams_3.1_8B_instruct_behaviour_cloning_all_data_exp_1_4bit_new_answerer_prompt
- aravind-selvam__Llams_3.1_8B_instruct_behaviour_cloning_all_data_exp_1_4bit_old_answerer_prompt
- aravind-selvam__all_data_8bit
- archish-holistic__THaMES-Llama-3.1-8B-Instruct-Finetuned
- arshiakarimian1__spam-llama3.1-8B-teacher-all
- arshiakarimian1__spam-llama3.1-8B-teacher-m
- arshiakarimian1__spam-llama3.1-8B-teacher
- aseratus1__80dde36f-b140-4e57-a010-bebb9584c30b