Instructions to use T145/ZEUS-8B-V23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use T145/ZEUS-8B-V23 with Transformers:

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

pipe = pipeline("text-generation", model="T145/ZEUS-8B-V23")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("T145/ZEUS-8B-V23")
model = AutoModelForCausalLM.from_pretrained("T145/ZEUS-8B-V23")
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 Settings

vLLM

How to use T145/ZEUS-8B-V23 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "T145/ZEUS-8B-V23"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "T145/ZEUS-8B-V23",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/T145/ZEUS-8B-V23

SGLang

How to use T145/ZEUS-8B-V23 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 "T145/ZEUS-8B-V23" \
    --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": "T145/ZEUS-8B-V23",
		"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 "T145/ZEUS-8B-V23" \
        --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": "T145/ZEUS-8B-V23",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use T145/ZEUS-8B-V23 with Docker Model Runner:
```
docker model run hf.co/T145/ZEUS-8B-V23
```

ZEUS-8B-V23 / README.md

T145

Adding Evaluation Results (#1)

2823e34 verified over 1 year ago

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	---
	base_model:
	- VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
	- allenai/Llama-3.1-Tulu-3-8B
	- unsloth/Llama-3.1-Storm-8B
	- unsloth/Meta-Llama-3.1-8B-Instruct
	- arcee-ai/Llama-3.1-SuperNova-Lite
	- Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
	- FreedomIntelligence/HuatuoGPT-o1-8B
	library_name: transformers
	tags:
	- mergekit
	- merge
	model-index:
	- name: ZEUS-8B-V23
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: IFEval (0-Shot)
	type: wis-k/instruction-following-eval
	split: train
	args:
	num_few_shot: 0
	metrics:
	- type: inst_level_strict_acc and prompt_level_strict_acc
	value: 76.21
	name: averaged accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FZEUS-8B-V23
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BBH (3-Shot)
	type: SaylorTwift/bbh
	split: test
	args:
	num_few_shot: 3
	metrics:
	- type: acc_norm
	value: 31.47
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FZEUS-8B-V23
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MATH Lvl 5 (4-Shot)
	type: lighteval/MATH-Hard
	split: test
	args:
	num_few_shot: 4
	metrics:
	- type: exact_match
	value: 16.77
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FZEUS-8B-V23
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GPQA (0-shot)
	type: Idavidrein/gpqa
	split: train
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 7.94
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FZEUS-8B-V23
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MuSR (0-shot)
	type: TAUR-Lab/MuSR
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 7.19
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FZEUS-8B-V23
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU-PRO (5-shot)
	type: TIGER-Lab/MMLU-Pro
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 29.62
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=T145%2FZEUS-8B-V23
	name: Open LLM Leaderboard
	---
	# Untitled Model (1)

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) as a base.

	### Models Merged

	The following models were included in the merge:
	* [VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct)
	* [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B)
	* [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B)
	* [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite)
	* [Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2](https://huggingface.co/Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2)
	* [FreedomIntelligence/HuatuoGPT-o1-8B](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	base_model: unsloth/Meta-Llama-3.1-8B-Instruct
	dtype: bfloat16
	merge_method: dare_ties
	parameters:
	int8_mask: 1.0
	normalize: 1.0
	random_seed: 145.0
	slices:
	- sources:
	- layer_range: [0, 32]
	model: unsloth/Llama-3.1-Storm-8B
	parameters:
	density: 0.94
	weight: 0.35
	- layer_range: [0, 32]
	model: arcee-ai/Llama-3.1-SuperNova-Lite
	parameters:
	density: 0.92
	weight: 0.2
	- layer_range: [0, 32]
	model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
	parameters:
	density: 0.91
	weight: 0.2
	- layer_range: [0, 32]
	model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
	parameters:
	density: 0.93
	weight: 0.19
	- layer_range: [0, 32]
	model: allenai/Llama-3.1-Tulu-3-8B
	parameters:
	density: 0.92
	weight: 0.03
	- layer_range: [0, 32]
	model: FreedomIntelligence/HuatuoGPT-o1-8B
	parameters:
	density: 0.92
	weight: 0.03
	- layer_range: [0, 32]
	model: unsloth/Meta-Llama-3.1-8B-Instruct
	tokenizer:
	tokens:
	<pad>:
	source:
	kind: model_token
	model: unsloth/Meta-Llama-3.1-8B-Instruct
	token: <\|finetune_right_pad_id\|>
	<\|begin_of_text\|>:
	force: true
	source: unsloth/Meta-Llama-3.1-8B-Instruct
	<\|eot_id\|>:
	force: true
	source: unsloth/Meta-Llama-3.1-8B-Instruct
	<\|finetune_right_pad_id\|>:
	force: true
	source: unsloth/Meta-Llama-3.1-8B-Instruct
	```

	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/T145__ZEUS-8B-V23-details)!
	Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=T145%2FZEUS-8B-V23&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

	\| Metric \|Value (%)\|
	\|-------------------\|--------:\|
	\|Average \| 28.20\|
	\|IFEval (0-Shot) \| 76.21\|
	\|BBH (3-Shot) \| 31.47\|
	\|MATH Lvl 5 (4-Shot)\| 16.77\|
	\|GPQA (0-shot) \| 7.94\|
	\|MuSR (0-shot) \| 7.19\|
	\|MMLU-PRO (5-shot) \| 29.62\|