base_model, license, model_creator, model_name, quantized_by, language, library_name, pipeline_tag, tags

base_model	license	model_creator	model_name	quantized_by	language	library_name	pipeline_tag	tags
meta-llama/Llama-3.2-3B-Instruct	llama3.2	meta	Llama-3.2-3B-Instruct	Second State Inc.	en de fr it pt hi es th	transformers	text-generation	chat llama llama-3

---

Llama-3.2-3B-Instruct-GGUF

Original Model

meta-llama/Llama-3.2-3B-Instruct

Run with LlamaEdge

• LlamaEdge version: v0.16.5 and above

• Prompt template

  • Prompt type for chat: llama-3-chat

    • Prompt string

      <|begin_of_text|><|start_header_id|>system<|end_header_id|>
      
      {{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
      
      {{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
      
      {{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
      
      {{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
      

  • Prompt type for tool use: llama-3-tool

    • Prompt string

      <|begin_of_text|><|start_header_id|>system<|end_header_id|>
      
      {system_message}<|eot_id|><|start_header_id|>user<|end_header_id|>
      
      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.
      
      [{"type":"function","function":{"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"]}}}]
      
      Question: {user_message}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
      

• Context size: 128000

• Run as LlamaEdge service

  • Chat

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.2-3B-Instruct-Q5_K_M.gguf \
        llama-api-server.wasm \
        --prompt-template llama-3-chat \
        --ctx-size 128000 \
        --model-name Llama-3.2-3b
    

  • Tool use

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.2-3B-Instruct-Q5_K_M.gguf \
        llama-api-server.wasm \
        --prompt-template llama-3-tool \
        --ctx-size 128000 \
        --model-name Llama-3.2-3b
    

• Run as LlamaEdge command app

  wasmedge --dir .:. --nn-preload default:GGML:AUTO:Llama-3.2-3B-Instruct-Q5_K_M.gguf \
    llama-chat.wasm \
    --prompt-template llama-3-chat \
    --ctx-size 128000
  

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
Llama-3.2-3B-Instruct-Q2_K.gguf	Q2_K	2	581 MB	smallest, significant quality loss - not recommended for most purposes
Llama-3.2-3B-Instruct-Q3_K_L.gguf	Q3_K_L	3	733 MB	small, substantial quality loss
Llama-3.2-3B-Instruct-Q3_K_M.gguf	Q3_K_M	3	691 MB	very small, high quality loss
Llama-3.2-3B-Instruct-Q3_K_S.gguf	Q3_K_S	3	642 MB	very small, high quality loss
Llama-3.2-3B-Instruct-Q4_0.gguf	Q4_0	4	771 MB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama-3.2-3B-Instruct-Q4_K_M.gguf	Q4_K_M	4	808 MB	medium, balanced quality - recommended
Llama-3.2-3B-Instruct-Q4_K_S.gguf	Q4_K_S	4	776 MB	small, greater quality loss
Llama-3.2-3B-Instruct-Q5_0.gguf	Q5_0	5	893 MB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama-3.2-3B-Instruct-Q5_K_M.gguf	Q5_K_M	5	912 MB	large, very low quality loss - recommended
Llama-3.2-3B-Instruct-Q5_K_S.gguf	Q5_K_S	5	893 MB	large, low quality loss - recommended
Llama-3.2-3B-Instruct-Q6_K.gguf	Q6_K	6	1.02 GB	very large, extremely low quality loss
Llama-3.2-3B-Instruct-Q8_0.gguf	Q8_0	8	1.32 GB	very large, extremely low quality loss - not recommended
Llama-3.2-3B-Instruct-f16.gguf	f16	16	2.48 GB

Quantized with llama.cpp b4466