Text Generation
GGUF
Indonesian
llama.cpp
qwen3
unsloth
conversational
indonesian
healthcare
maternal-health
child-health
local-llm
offline-ai
Instructions to use Linov1991/linov-0.6b-ibuanakmed-finetune 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 Linov1991/linov-0.6b-ibuanakmed-finetune 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 Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M # Run inference directly in the terminal: llama cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M # Run inference directly in the terminal: llama cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune: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 Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune: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 Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
Use Docker
docker model run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Linov1991/linov-0.6b-ibuanakmed-finetune" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Linov1991/linov-0.6b-ibuanakmed-finetune", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
- Ollama
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Ollama:
ollama run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
- Unsloth Desktop
- Pi
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Docker Model Runner:
docker model run hf.co/Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
- Lemonade
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
Run and chat with the model
lemonade run user.linov-0.6b-ibuanakmed-finetune-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Linov1991/linov-0.6b-ibuanakmed-finetune with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Linov1991/linov-0.6b-ibuanakmed-finetune:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download Modelfile from Linov1991/linov-0.6b-ibuanakmed-finetune: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/Linov1991/linov-0.6b-ibuanakmed-finetune/resolve/69aeea39b39730f900370b5fd0fadd033897df8d/Modelfile
- Command line
-
hf download hf://Linov1991/linov-0.6b-ibuanakmed-finetune@69aeea39b39730f900370b5fd0fadd033897df8d/Modelfile
-
curl -L -o Modelfile https://huggingface.co/Linov1991/linov-0.6b-ibuanakmed-finetune/resolve/69aeea39b39730f900370b5fd0fadd033897df8d/Modelfile
1.7 kB
| FROM qwen3-0.6b.Q4_K_M.gguf | |
| TEMPLATE """{{- if .Messages }} | |
| {{- if or .System .Tools }}<|im_start|>system | |
| {{- if .System }} | |
| {{ .System }} | |
| {{- end }} | |
| {{- if .Tools }} | |
| # Tools | |
| You may call one or more functions to assist with the user query. | |
| You are provided with function signatures within <tools></tools> XML tags: | |
| <tools> | |
| {{- range .Tools }} | |
| {"type": "function", "function": {{ .Function }}} | |
| {{- end }} | |
| </tools> | |
| For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags: | |
| <tool_call> | |
| {"name": <function-name>, "arguments": <args-json-object>} | |
| </tool_call> | |
| {{- end }}<|im_end|> | |
| {{ end }} | |
| {{- range $i, $_ := .Messages }} | |
| {{- $last := eq (len (slice $.Messages $i)) 1 -}} | |
| {{- if eq .Role "user" }}<|im_start|>user | |
| {{ .Content }}<|im_end|> | |
| {{ else if eq .Role "assistant" }}<|im_start|>assistant | |
| {{ if .Content }}{{ .Content }} | |
| {{- else if .ToolCalls }}<tool_call> | |
| {{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}} | |
| {{ end }}</tool_call> | |
| {{- end }}{{ if not $last }}<|im_end|> | |
| {{ end }} | |
| {{- else if eq .Role "tool" }}<|im_start|>user | |
| <tool_response> | |
| {{ .Content }} | |
| </tool_response><|im_end|> | |
| {{ end }} | |
| {{- if and (ne .Role "assistant") $last }}<|im_start|>assistant | |
| {{ end }} | |
| {{- end }} | |
| {{- else }} | |
| {{- if .System }}<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| {{ end }}{{ if .Prompt }}<|im_start|>user | |
| {{ .Prompt }}<|im_end|> | |
| {{ end }}<|im_start|>assistant | |
| {{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}""" | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER stop "<|im_start|>" | |
| PARAMETER temperature 0.6 | |
| PARAMETER min_p 0.0 | |
| PARAMETER top_k 20 | |
| PARAMETER top_p 0.95 | |
| PARAMETER repeat_penalty 1 |