Instructions to use GrainWare/tuxsentience-beta2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use GrainWare/tuxsentience-beta2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GrainWare/tuxsentience-beta2", filename="Llama-3.2-1B-Instruct-unsloth-bnb-4bit-tuxsentience-beta2-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - 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
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 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/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 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
- 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 Studio
How to use GrainWare/tuxsentience-beta2 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 GrainWare/tuxsentience-beta2 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 GrainWare/tuxsentience-beta2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GrainWare/tuxsentience-beta2 to start chatting
- Atomic Chat new
- 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
| 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 | |