Instructions to use rxavier/Taurus-7B-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rxavier/Taurus-7B-1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rxavier/Taurus-7B-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rxavier/Taurus-7B-1.0")
model = AutoModelForCausalLM.from_pretrained("rxavier/Taurus-7B-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rxavier/Taurus-7B-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rxavier/Taurus-7B-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rxavier/Taurus-7B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rxavier/Taurus-7B-1.0

SGLang

How to use rxavier/Taurus-7B-1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rxavier/Taurus-7B-1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rxavier/Taurus-7B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rxavier/Taurus-7B-1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rxavier/Taurus-7B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rxavier/Taurus-7B-1.0 with Docker Model Runner:
```
docker model run hf.co/rxavier/Taurus-7B-1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Taurus 7B 1.0

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]))

GGUF quants

You can find GGUF quants for llama.cpp here.

Downloads last month: 54

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for rxavier/Taurus-7B-1.0

Base model

mistralai/Mistral-7B-v0.1

Finetuned

teknium/OpenHermes-2.5-Mistral-7B

Finetuned

(85)

this model

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

63.570
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

83.640
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

63.500
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

50.210
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.140
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

59.360