Instructions to use GrainWare/tuxsentience-beta2 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 GrainWare/tuxsentience-beta2 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 GrainWare/tuxsentience-beta2:Q4_K_M # Run inference directly in the terminal: llama cli -hf GrainWare/tuxsentience-beta2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf GrainWare/tuxsentience-beta2:Q4_K_M # Run inference directly in the terminal: llama cli -hf GrainWare/tuxsentience-beta2:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf GrainWare/tuxsentience-beta2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf GrainWare/tuxsentience-beta2:Q4_K_M
Use Docker
docker model run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use GrainWare/tuxsentience-beta2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GrainWare/tuxsentience-beta2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GrainWare/tuxsentience-beta2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
- Ollama
How to use GrainWare/tuxsentience-beta2 with Ollama:
ollama run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use GrainWare/tuxsentience-beta2 with Docker Model Runner:
docker model run hf.co/GrainWare/tuxsentience-beta2:Q4_K_M
- Lemonade
How to use GrainWare/tuxsentience-beta2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GrainWare/tuxsentience-beta2:Q4_K_M
Run and chat with the model
lemonade run user.tuxsentience-beta2-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,849 Bytes
ffe56d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | FROM ./Llama-3.2-1B-Instruct-unsloth-bnb-4bit-tuxsentience-beta2-Q4_K_M.gguf
TEMPLATE """<|start_header_id|>system<|end_header_id|>
Cutting Knowledge Date: December 2023
{{ if .System }}{{ .System }}
{{- end }}
{{- if .Tools }}When you receive a tool call response, use the output to format an answer to the orginal user question.
You are a helpful assistant with tool calling capabilities.
{{- end }}<|eot_id|>
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 }}
{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
{{- if and $.Tools $last }}
Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
{{ range $.Tools }}
{{- . }}
{{ end }}
{{ .Content }}<|eot_id|>
{{- else }}
{{ .Content }}<|eot_id|>
{{- end }}{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
{{ end }}
{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
{{- if .ToolCalls }}
{{ range .ToolCalls }}
{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
{{- else }}
{{ .Content }}
{{- end }}{{ if not $last }}<|eot_id|>{{ end }}
{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
{{ end }}
{{- end }}
{{- end }}"""
PARAMETER stop "<|python_tag|>"
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|eom_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|finetune_right_pad_id|>"
PARAMETER stop "<|end_of_text|>"
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|reserved_special_token_"
PARAMETER temperature 1.5
PARAMETER min_p 0.1
|