LLM Customization

We provide a user-friendly way to evaluate your own model.

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Evaluate Your Model

Step 1: Add your model configs

You should add your model config to eval_configs/main_results_all_tasks.yaml, for example:

  codellama-34b
      name: vllm 
      engine:  [PATH TO YOUR MODEL]
      max_tokens: 100
      temperature: 0
      top_p: 1
      stop:
      context_length: 16384
      dtype: float32
      ngpu: 4
      use_parser: True

Arguments for the configs are as follows,

• name: name of the inference framework (e.g., vllm,hg, gpt,gpt_azure, claude)
• engine: path to your model or the huggingface model name
• max_tokens: the max number of newly generated tokens
• temperature: temperature for generation
• top_p: top-$p$ for generation
• stop: stop tokens for generation
• context_length: the maximum context length of the LLM
• dtype: float32 or float16
• ngpu: this argument works for the vllm framework
• usr_parser: bool, post-process the generated actions

Step 2: Inference your model with vLLM or huggingface

Please check whether your model is supported by vLLM. We recommend to use vLLM because it is usually much faster than naive model.generate() in huggingface.

If you decide to use the vLLM, you should set the argument of name above as vllm, otherwise hg.

Note： The results of inferencing with huggingface and vLLM can be different because their different implementations.

Step 3: Write the prompt_templates of your model

If your model need customized input template, you should write it in agentboard/prompts/prompt_template, for example:

"codellama-34b":
"""
<s>[INST]{system_prompt}{prompt}[/INST]
""",

Arguments for this template are as follows,

• system_prompt: system prompt of your agent
• prompt: the user prompt of your agent