92 lines
2.3 KiB
Markdown
92 lines
2.3 KiB
Markdown
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---
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datasets:
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- WizardLM/WizardLM_evol_instruct_V2_196k
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- Open-Orca/OpenOrca
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language:
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- en
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tags:
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- chat
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- palmyra
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license: apache-2.0
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---
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**DEPRECATED MODEL NOTICE**
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==========================
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Please note that this model is no longer maintained or supported by our team. We strongly advise against using it in production or for any critical applications.
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Instead, we recommend using our latest and greatest models, which can be found at:
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https://huggingface.co/collections/Writer/palmyra-writer-license-66476fa8156169f8720a2c89
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==========================
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# Writer/palmyra-20b-chat
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---
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# Usage
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```py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
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model_name = "Writer/palmyra-20b-chat"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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prompt = "What is the meaning of life?"
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input_text = (
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"A chat between a curious user and an artificial intelligence assistant. "
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"The assistant gives helpful, detailed, and polite answers to the user's questions. "
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"USER: {prompt} "
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"ASSISTANT:"
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)
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model_inputs = tokenizer(input_text.format(prompt=prompt), return_tensors="pt").to(
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"cuda"
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)
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gen_conf = {
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"top_k": 20,
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"max_new_tokens": 2048,
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"temperature": 0.6,
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"do_sample": True,
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"eos_token_id": tokenizer.eos_token_id,
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}
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streamer = TextStreamer(tokenizer)
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if "token_type_ids" in model_inputs:
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del model_inputs["token_type_ids"]
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all_inputs = {**model_inputs, **gen_conf}
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output = model.generate(**all_inputs, streamer=streamer)
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print("-"*20)
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print(output)
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-20b-chat)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 38.97 |
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| ARC (25-shot) | 43.52 |
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| HellaSwag (10-shot) | 72.83 |
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| MMLU (5-shot) | 35.18 |
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| TruthfulQA (0-shot) | 43.17 |
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| Winogrande (5-shot) | 66.46 |
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| GSM8K (5-shot) | 3.94 |
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| DROP (3-shot) | 7.7 |
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