Instructions to use meetkai/functionary-medium-v3.1-GGUF 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 meetkai/functionary-medium-v3.1-GGUF 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 meetkai/functionary-medium-v3.1-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf meetkai/functionary-medium-v3.1-GGUF:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf meetkai/functionary-medium-v3.1-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf meetkai/functionary-medium-v3.1-GGUF:Q4_0
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 meetkai/functionary-medium-v3.1-GGUF:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf meetkai/functionary-medium-v3.1-GGUF:Q4_0
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 meetkai/functionary-medium-v3.1-GGUF:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf meetkai/functionary-medium-v3.1-GGUF:Q4_0
Use Docker
docker model run hf.co/meetkai/functionary-medium-v3.1-GGUF:Q4_0
- LM Studio
- Jan
- Ollama
How to use meetkai/functionary-medium-v3.1-GGUF with Ollama:
ollama run hf.co/meetkai/functionary-medium-v3.1-GGUF:Q4_0
- Unsloth Desktop
- Docker Model Runner
How to use meetkai/functionary-medium-v3.1-GGUF with Docker Model Runner:
docker model run hf.co/meetkai/functionary-medium-v3.1-GGUF:Q4_0
- Lemonade
How to use meetkai/functionary-medium-v3.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull meetkai/functionary-medium-v3.1-GGUF:Q4_0
Run and chat with the model
lemonade run user.functionary-medium-v3.1-GGUF-Q4_0
List all available models
lemonade list
- Atomic Chat
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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}
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},
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"bos_token": "<|begin_of_text|>",
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"chat_template": "{# version=v3-llama3.1 #}{%- if not tools is defined -%}\n {%- set tools = none -%}\n{%- endif -%}\n\n{%- set has_code_interpreter = tools | selectattr(\"type\", \"equalto\", \"code_interpreter\") | list | length > 0 -%}\n{%- if has_code_interpreter -%}\n {%- set tools = tools | rejectattr(\"type\", \"equalto\", \"code_interpreter\") | list -%}\n{%- endif -%}\n\n{#- System message + builtin tools #}\n{{- bos_token + \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if has_code_interpreter %}\n {{- \"Environment: ipython\\n\\n\" }}\n{%- else -%}\n {{ \"\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\\n\" }}\n{%- if tools %}\n {{- \"\\nYou have access to the following functions:\\n\\n\" }}\n {%- for t in tools %}\n {%- if \"type\" in t -%}\n {{ \"Use the function '\" + t[\"function\"][\"name\"] + \"' to '\" + t[\"function\"][\"description\"] + \"'\\n\" + t[\"function\"] | tojson() }}\n {%- else -%}\n {{ \"Use the function '\" + t[\"name\"] + \"' to '\" + t[\"description\"] + \"'\\n\" + t | tojson() }}\n {%- endif -%}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- '\\nThink very carefully before calling functions.\\nIf a you choose to call a function ONLY reply in the following format:\\n<{start_tag}={function_name}>{parameters}{end_tag}\\nwhere\\n\\nstart_tag => `<function`\\nparameters => a JSON dict with the function argument name as key and function argument value as value.\\nend_tag => `</function>`\\n\\nHere is an example,\\n<function=example_function_name>{\"example_name\": \"example_value\"}</function>\\n\\nReminder:\\n- If looking for real time information use relevant functions before falling back to brave_search\\n- Function calls MUST follow the specified format, start with <function= and end with </function>\\n- Required parameters MUST be specified\\n- Only call one function at a time\\n- Put the entire function call reply on one line\\n\\n' -}}\n{%- endif %}\n{{- \"<|eot_id|>\" -}}\n\n{%- for message in messages -%}\n {%- if message['role'] == 'user' or message['role'] == 'system' -%}\n {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' + message['content'] + '<|eot_id|>' }}\n {%- elif message['role'] == 'tool' -%}\n {{ '<|start_header_id|>ipython<|end_header_id|>\\n\\n' + message['content'] + '<|eot_id|>' }}\n {%- else -%}\n {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'}}\n {%- if message['content']
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"model_input_names": [
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}
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},
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"bos_token": "<|begin_of_text|>",
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"chat_template": "{# version=v3-llama3.1 #}{%- if not tools is defined -%}\n {%- set tools = none -%}\n{%- endif -%}\n\n{%- set has_code_interpreter = tools | selectattr(\"type\", \"equalto\", \"code_interpreter\") | list | length > 0 -%}\n{%- if has_code_interpreter -%}\n {%- set tools = tools | rejectattr(\"type\", \"equalto\", \"code_interpreter\") | list -%}\n{%- endif -%}\n\n{#- System message + builtin tools #}\n{{- bos_token + \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if has_code_interpreter %}\n {{- \"Environment: ipython\\n\\n\" }}\n{%- else -%}\n {{ \"\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\\n\" }}\n{%- if tools %}\n {{- \"\\nYou have access to the following functions:\\n\\n\" }}\n {%- for t in tools %}\n {%- if \"type\" in t -%}\n {{ \"Use the function '\"|safe + t[\"function\"][\"name\"] + \"' to '\"|safe + t[\"function\"][\"description\"] + \"'\\n\"|safe + t[\"function\"] | tojson() }}\n {%- else -%}\n {{ \"Use the function '\"|safe + t[\"name\"] + \"' to '\"|safe + t[\"description\"] + \"'\\n\"|safe + t | tojson() }}\n {%- endif -%}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- '\\nThink very carefully before calling functions.\\nIf a you choose to call a function ONLY reply in the following format:\\n<{start_tag}={function_name}>{parameters}{end_tag}\\nwhere\\n\\nstart_tag => `<function`\\nparameters => a JSON dict with the function argument name as key and function argument value as value.\\nend_tag => `</function>`\\n\\nHere is an example,\\n<function=example_function_name>{\"example_name\": \"example_value\"}</function>\\n\\nReminder:\\n- If looking for real time information use relevant functions before falling back to brave_search\\n- Function calls MUST follow the specified format, start with <function= and end with </function>\\n- Required parameters MUST be specified\\n- Only call one function at a time\\n- Put the entire function call reply on one line\\n\\n' -}}\n{%- endif %}\n{{- \"<|eot_id|>\" -}}\n\n{%- for message in messages -%}\n {%- if message['role'] == 'user' or message['role'] == 'system' -%}\n {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' + message['content'] + '<|eot_id|>' }}\n {%- elif message['role'] == 'tool' -%}\n {{ '<|start_header_id|>ipython<|end_header_id|>\\n\\n' + message['content'] + '<|eot_id|>' }}\n {%- else -%}\n {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'}}\n {%- if message['content'] -%}\n {{ message['content'] }}\n {%- endif -%}\n {%- if 'tool_calls' in message and message['tool_calls'] -%}\n {%- for tool_call in message['tool_calls'] -%}\n {%- if tool_call[\"function\"][\"name\"] == \"python\" -%}\n {{ '<|python_tag|>' + tool_call['function']['arguments'] }}\n {%- else -%}\n {{ '<function=' + tool_call['function']['name'] + '>' + tool_call['function']['arguments'] + '</function>' }}\n {%- endif -%}\n {%- endfor -%}\n {{ '<|eom_id|>' }}\n {%- else -%}\n {{ '<|eot_id|>' }}\n {%- endif -%}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{ '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif -%}",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"model_input_names": [
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