katanemo
/

Arch-Function-7B

Text Generation

text-generation-inference

Inference Endpoints

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nehcgs commited on 12 days ago

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f4fba79

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1 Parent(s): 7a1afda

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@@ -11,7 +11,7 @@ pipeline_tag: text-generation
 library_name: transformers
 ---
-# katanemolabs/Arch-Function-7B
 ## Overview
 The Katanemo Arch-Function collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for **function calling** tasks. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial.
@@ -192,7 +192,7 @@ import json
 from typing import Any, Dict, List
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "katanemolabs/Arch-Function-7B"
 model = AutoModelForCausalLM.from_pretrained(
     model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
 )

 library_name: transformers
 ---
+# katanemo/Arch-Function-7B
 ## Overview
 The Katanemo Arch-Function collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for **function calling** tasks. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial.
 from typing import Any, Dict, List
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "katanemo/Arch-Function-7B"
 model = AutoModelForCausalLM.from_pretrained(
     model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
 )