Llama-3-Groq-8B-Tool-Use-GGUF
Original Model
Run with LlamaEdge
LlamaEdge version: v0.12.4
Prompt template
Prompt type:
groq-llama3-tool
Prompt string
<|start_header_id|>system<|end_header_id|> You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call> {"name": <function-name>,"arguments": <args-dict>} </tool_call> Here are the available tools: <tools> { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "description": "The temperature unit to use. Infer this from the users location.", "enum": [ "celsius", "fahrenheit" ] } }, "required": [ "location", "unit" ] } } { "name": "predict_weather", "description": "Predict the weather in 24 hours", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "description": "The temperature unit to use. Infer this from the users location.", "enum": [ "celsius", "fahrenheit" ] } }, "required": [ "location", "unit" ] } } </tools><|eot_id|><|start_header_id|>user<|end_header_id|> What is the weather like in San Francisco in Celsius?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Context size:
8192
Run as LlamaEdge service
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3-Groq-8B-Tool-Use-Q5_K_M.gguf \ llama-api-server.wasm \ --prompt-template groq-llama3-tool \ --ctx-size 8192 \ --model-name Llama-3-Groq-8B
Quantized GGUF Models
Name | Quant method | Bits | Size | Use case |
---|---|---|---|---|
Llama-3-Groq-8B-Tool-Use-Q2_K.gguf | Q2_K | 2 | 3.18 GB | smallest, significant quality loss - not recommended for most purposes |
Llama-3-Groq-8B-Tool-Use-Q3_K_L.gguf | Q3_K_L | 3 | 4.32 GB | small, substantial quality loss |
Llama-3-Groq-8B-Tool-Use-Q3_K_M.gguf | Q3_K_M | 3 | 4.02 GB | very small, high quality loss |
Llama-3-Groq-8B-Tool-Use-Q3_K_S.gguf | Q3_K_S | 3 | 3.66 GB | very small, high quality loss |
Llama-3-Groq-8B-Tool-Use-Q4_0.gguf | Q4_0 | 4 | 4.66 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
Llama-3-Groq-8B-Tool-Use-Q4_K_M.gguf | Q4_K_M | 4 | 4.92 GB | medium, balanced quality - recommended |
Llama-3-Groq-8B-Tool-Use-Q4_K_S.gguf | Q4_K_S | 4 | 4.69 GB | small, greater quality loss |
Llama-3-Groq-8B-Tool-Use-Q5_0.gguf | Q5_0 | 5 | 5.60 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
Llama-3-Groq-8B-Tool-Use-Q5_K_M.gguf | Q5_K_M | 5 | 5.73 GB | large, very low quality loss - recommended |
Llama-3-Groq-8B-Tool-Use-Q5_K_S.gguf | Q5_K_S | 5 | 5.60 GB | large, low quality loss - recommended |
Llama-3-Groq-8B-Tool-Use-Q6_K.gguf | Q6_K | 6 | 6.60 GB | very large, extremely low quality loss |
Llama-3-Groq-8B-Tool-Use-Q8_0.gguf | Q8_0 | 8 | 8.54 GB | very large, extremely low quality loss - not recommended |
Llama-3-Groq-8B-Tool-Use-f16.gguf | f16 | 16 | 16.1 GB |
Quantized with llama.cpp b3405.
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Model tree for second-state/Llama-3-Groq-8B-Tool-Use-GGUF
Base model
meta-llama/Meta-Llama-3-8B
Finetuned
Groq/Llama-3-Groq-8B-Tool-Use