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Browse files- README.md +6 -6
- config.json +1 -1
- generation_config.json +1 -1
- model-00001-of-00004.safetensors +1 -1
- model-00002-of-00004.safetensors +1 -1
- model-00003-of-00004.safetensors +1 -1
- model-00004-of-00004.safetensors +1 -1
- tokenizer_config.json +2 -2
README.md
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license: other
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license_name: katanemo-research
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license_link: >-
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https://huggingface.co/katanemolabs/Arch-Function-
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base_model:
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-
- Qwen/Qwen2.5-
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# katanemo/Arch-Function-
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## Overview
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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.
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# Requirements
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The code of Arch-Function-
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```bash
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pip install transformers>=4.37.0
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```
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from typing import Any, Dict, List
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "katanemo/Arch-Function-
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model = AutoModelForCausalLM.from_pretrained(
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model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
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)
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# License
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Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-
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license: other
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license_name: katanemo-research
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license_link: >-
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https://huggingface.co/katanemolabs/Arch-Function-7B/blob/main/LICENSE
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base_model:
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- Qwen/Qwen2.5-Coder-7B-Instruct
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# katanemo/Arch-Function-7B
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## Overview
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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.
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# Requirements
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The code of Arch-Function-7B has been in the Hugging Face `transformers` library and we advise you to install latest version:
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```bash
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pip install transformers>=4.37.0
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```
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from typing import Any, Dict, List
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "katanemo/Arch-Function-7B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
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)
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# License
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Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-7B/blob/main/LICENSE).
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config.json
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{
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"_name_or_path": "Qwen/Qwen2.5-7B-Instruct",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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{
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"_name_or_path": "Qwen/Qwen2.5-Coder-7B-Instruct",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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generation_config.json
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151643
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],
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"repetition_penalty": 1.
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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model-00001-of-00004.safetensors
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tokenizer_config.json
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"model_max_length":
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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"clean_up_tokenization_spaces": false,
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"model_max_length": 32768,
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"split_special_tokens": false,
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}
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