Instructions to use Franc105/LoraTrainModelV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Franc105/LoraTrainModelV2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Franc105/LoraTrainModelV2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Franc105/LoraTrainModelV2 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 Franc105/LoraTrainModelV2:Q4_K_M # Run inference directly in the terminal: llama cli -hf Franc105/LoraTrainModelV2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Franc105/LoraTrainModelV2:Q4_K_M # Run inference directly in the terminal: llama cli -hf Franc105/LoraTrainModelV2: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 Franc105/LoraTrainModelV2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Franc105/LoraTrainModelV2: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 Franc105/LoraTrainModelV2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Franc105/LoraTrainModelV2:Q4_K_M
Use Docker
docker model run hf.co/Franc105/LoraTrainModelV2:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Franc105/LoraTrainModelV2 with Ollama:
ollama run hf.co/Franc105/LoraTrainModelV2:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Franc105/LoraTrainModelV2 with Docker Model Runner:
docker model run hf.co/Franc105/LoraTrainModelV2:Q4_K_M
- Lemonade
How to use Franc105/LoraTrainModelV2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Franc105/LoraTrainModelV2:Q4_K_M
Run and chat with the model
lemonade run user.LoraTrainModelV2-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download Modelfile from Franc105/LoraTrainModelV2: direct link, hf CLI and curl.
- Browser
- Download file 842 Bytes
-
https://huggingface.co/Franc105/LoraTrainModelV2/resolve/main/Modelfile
- Command line
-
hf download hf://Franc105/LoraTrainModelV2/Modelfile
-
curl -L -o Modelfile https://huggingface.co/Franc105/LoraTrainModelV2/resolve/main/Modelfile
842 Bytes
| FROM /content/Franc105/LoraTrainModelV2/unsloth.BF16.gguf | |
| TEMPLATE """You are an expert medical database query assistant specialized in converting natural language to structured database queries. | |
| Your expertise includes: | |
| - Medical terminology and ICD-10 codes | |
| - Patient data management systems | |
| - Laboratory values and medical measurements | |
| - Hospital workflow and patient care processes | |
| Given database schema context and user queries, generate precise structured responses in the specified format.{{ if .Prompt }} | |
| ### Database Query Task: | |
| {{ .Prompt }}{{ end }} | |
| ### Structured Response: | |
| {{ .Response }}<|eot_id|>""" | |
| PARAMETER stop "<|end_of_text|>" | |
| PARAMETER stop "<|start_header_id|>" | |
| PARAMETER stop "<|end_header_id|>" | |
| PARAMETER stop "<|eot_id|>" | |
| PARAMETER stop "<|reserved_special_token_" | |
| PARAMETER temperature 1.5 | |
| PARAMETER min_p 0.1 |