初始化项目，由ModelHub XC社区提供模型
Model: rxavier/Taurus-7B-1.0 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: mit
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tags:
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- chatml
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- mistral
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- instruct
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- openhermes
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- economics
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datasets:
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- rxavier/economicus
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base_model: teknium/OpenHermes-2.5-Mistral-7B
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model-index:
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- name: Taurus-7B-1.0
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 63.57
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rxavier/Taurus-7B-1.0
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 83.64
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rxavier/Taurus-7B-1.0
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 63.5
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rxavier/Taurus-7B-1.0
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 50.21
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rxavier/Taurus-7B-1.0
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 78.14
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rxavier/Taurus-7B-1.0
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 59.36
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rxavier/Taurus-7B-1.0
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name: Open LLM Leaderboard
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library_name: transformers
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---
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# Taurus 7B 1.0
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## Description
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Taurus is an [OpenHermes 2.5](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) finetune using the [Economicus dataset](https://huggingface.co/datasets/rxavier/economicus), an instruct dataset synthetically generated from Economics PhD textbooks.
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The model was trained for 2 epochs (QLoRA) using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl). The exact config I used can be found [here](https://huggingface.co/rxavier/Taurus-1.0-Mistral-7B/tree/main/axolotl).
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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_rxavier__Taurus-7B-1.0)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |66.40|
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|AI2 Reasoning Challenge (25-Shot)|63.57|
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|HellaSwag (10-Shot) |83.64|
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|MMLU (5-Shot) |63.50|
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|TruthfulQA (0-shot) |50.21|
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|Winogrande (5-shot) |78.14|
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|GSM8k (5-shot) |59.36|
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## Prompt format
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Taurus uses **ChatML**.
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```
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<|im_start|>system
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System message
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<|im_start|>user
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User message<|im_end|>
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<|im_start|>assistant
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```
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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model_id = "rxavier/Taurus-7B-1.0"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16, #torch.float16 for older GPUs
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device_map="auto", # Requires having accelerate installed, useful in places like Colab with limited VRAM
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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generation_config = GenerationConfig(
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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)
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system_message = "You are an expert in economics with PhD level knowledge. You are helpful, give thorough and clear explanations, and use equations and formulas where needed."
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prompt = "Give me latex formulas for extended euler equations"
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messages = [{"role": "system",
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"content": system_message},
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{"role": "user",
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"content": prompt}]
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tokens = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
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with torch.no_grad():
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outputs = model.generate(inputs=tokens, generation_config=generation_config, max_length=512)
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print(tokenizer.decode(outputs.cpu().tolist()[0]))
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```
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## GGUF quants
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You can find GGUF quants for llama.cpp [here](https://huggingface.co/rxavier/Taurus-7B-1.0-GGUF).
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