Instructions to use xirf/lemper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xirf/lemper with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xirf/lemper", dtype="auto") - llama-cpp-python
How to use xirf/lemper with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="xirf/lemper", filename="unsloth.F16.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 xirf/lemper with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf xirf/lemper:F16 # Run inference directly in the terminal: llama-cli -hf xirf/lemper:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf xirf/lemper:F16 # Run inference directly in the terminal: llama-cli -hf xirf/lemper:F16
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 xirf/lemper:F16 # Run inference directly in the terminal: ./llama-cli -hf xirf/lemper:F16
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 xirf/lemper:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf xirf/lemper:F16
Use Docker
docker model run hf.co/xirf/lemper:F16
- LM Studio
- Jan
- Ollama
How to use xirf/lemper with Ollama:
ollama run hf.co/xirf/lemper:F16
- Unsloth Studio new
How to use xirf/lemper 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 xirf/lemper 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 xirf/lemper to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for xirf/lemper to start chatting
- Pi new
How to use xirf/lemper with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf xirf/lemper:F16
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": "xirf/lemper:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use xirf/lemper with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf xirf/lemper:F16
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 xirf/lemper:F16
Run Hermes
hermes
- Docker Model Runner
How to use xirf/lemper with Docker Model Runner:
docker model run hf.co/xirf/lemper:F16
- Lemonade
How to use xirf/lemper with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull xirf/lemper:F16
Run and chat with the model
lemonade run user.lemper-F16
List all available models
lemonade list
(Trained with Unsloth)
Browse files
Modelfile
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FROM /content/xirf/lemper/unsloth.F16.gguf
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TEMPLATE """{{- if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|endoftext|>"
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PARAMETER temperature 1.5
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PARAMETER min_p 0.1
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