Text Generation
Transformers
Safetensors
llama
mergekit
Merge
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use T145/ZEUS-8B-V23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("T145/ZEUS-8B-V23") model = AutoModelForCausalLM.from_pretrained("T145/ZEUS-8B-V23", device_map="auto") 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=256) 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
File size: 3,050 Bytes
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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
---
# 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
```
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