Instructions to use Roderick3rd/AR15_Expert_Larry_3B_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Roderick3rd/AR15_Expert_Larry_3B_4bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - llama-cpp-python
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Roderick3rd/AR15_Expert_Larry_3B_4bit", filename="AR15_Expert_Larry_3B_DPO_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 Roderick3rd/AR15_Expert_Larry_3B_4bit 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 Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M # Run inference directly in the terminal: llama cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M # Run inference directly in the terminal: llama cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit: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 Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit: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 Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
Use Docker
docker model run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Roderick3rd/AR15_Expert_Larry_3B_4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Roderick3rd/AR15_Expert_Larry_3B_4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
- Ollama
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Ollama:
ollama run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
- Unsloth Studio
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit 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 Roderick3rd/AR15_Expert_Larry_3B_4bit 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 Roderick3rd/AR15_Expert_Larry_3B_4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Roderick3rd/AR15_Expert_Larry_3B_4bit to start chatting
- Pi
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Roderick3rd/AR15_Expert_Larry_3B_4bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Roderick3rd/AR15_Expert_Larry_3B_4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Roderick3rd/AR15_Expert_Larry_3B_4bit"
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 Roderick3rd/AR15_Expert_Larry_3B_4bit
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Roderick3rd/AR15_Expert_Larry_3B_4bit"
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 "Roderick3rd/AR15_Expert_Larry_3B_4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Roderick3rd/AR15_Expert_Larry_3B_4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Roderick3rd/AR15_Expert_Larry_3B_4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Roderick3rd/AR15_Expert_Larry_3B_4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Docker Model Runner:
docker model run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
- Lemonade
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
Run and chat with the model
lemonade run user.AR15_Expert_Larry_3B_4bit-Q4_K_M
List all available models
lemonade list
Upload chat_template.jinja
Browse files- chat_template.jinja +93 -0
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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