Instructions to use l33tkr3w/full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use l33tkr3w/full with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="l33tkr3w/full", filename="unsloth.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use l33tkr3w/full with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf l33tkr3w/full:Q4_K_M # Run inference directly in the terminal: llama-cli -hf l33tkr3w/full:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf l33tkr3w/full:Q4_K_M # Run inference directly in the terminal: llama-cli -hf l33tkr3w/full: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 l33tkr3w/full:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf l33tkr3w/full: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 l33tkr3w/full:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf l33tkr3w/full:Q4_K_M
Use Docker
docker model run hf.co/l33tkr3w/full:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use l33tkr3w/full with Ollama:
ollama run hf.co/l33tkr3w/full:Q4_K_M
- Unsloth Studio new
How to use l33tkr3w/full 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 l33tkr3w/full 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 l33tkr3w/full to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for l33tkr3w/full to start chatting
- Pi new
How to use l33tkr3w/full with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf l33tkr3w/full:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "l33tkr3w/full:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use l33tkr3w/full with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf l33tkr3w/full: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 l33tkr3w/full:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use l33tkr3w/full with Docker Model Runner:
docker model run hf.co/l33tkr3w/full:Q4_K_M
- Lemonade
How to use l33tkr3w/full with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull l33tkr3w/full:Q4_K_M
Run and chat with the model
lemonade run user.full-Q4_K_M
List all available models
lemonade list
| FROM /content/l33tkr3w/full/unsloth.BF16.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 "<|endoftext|>" | |
| PARAMETER temperature 1.5 | |
| PARAMETER min_p 0.1 |