duyntnet/Taurus-7B-1.0-imatrix-GGUF

Quantizations of https://huggingface.co/rxavier/Taurus-7B-1.0

Open source inference clients/UIs

• llama.cpp
• KoboldCPP
• text-generation-webui
• jan
• ik_llama.cpp
• croco.cpp

Closed source inference clients/UIs

• LM Studio
• More will be added...

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From original readme

Description

Taurus is an OpenHermes 2.5 finetune using the Economicus dataset, an instruct dataset synthetically generated from Economics PhD textbooks.

The model was trained for 2 epochs (QLoRA) using axolotl. The exact config I used can be found here.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.40
AI2 Reasoning Challenge (25-Shot)	63.57
HellaSwag (10-Shot)	83.64
MMLU (5-Shot)	63.50
TruthfulQA (0-shot)	50.21
Winogrande (5-shot)	78.14
GSM8k (5-shot)	59.36

Prompt format

Taurus uses ChatML.

<|im_start|>system
System message
<|im_start|>user
User message<|im_end|>
<|im_start|>assistant

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig


model_id = "rxavier/Taurus-7B-1.0"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, #torch.float16 for older GPUs
    device_map="auto", # Requires having accelerate installed, useful in places like Colab with limited VRAM
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
generation_config = GenerationConfig(
                bos_token_id=tokenizer.bos_token_id,
                eos_token_id=tokenizer.eos_token_id,
                pad_token_id=tokenizer.pad_token_id,
            )

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."
prompt = "Give me latex formulas for extended euler equations"

messages = [{"role": "system",
             "content": system_message},
            {"role": "user",
             "content": prompt}]
tokens = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(inputs=tokens, generation_config=generation_config, max_length=512)
print(tokenizer.decode(outputs.cpu().tolist()[0]))