Instructions to use Bytes512/Queen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bytes512/Queen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Bytes512/Queen")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Bytes512/Queen") model = AutoModelForCausalLM.from_pretrained("Bytes512/Queen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Bytes512/Queen with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Bytes512/Queen" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Bytes512/Queen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Bytes512/Queen
- SGLang
How to use Bytes512/Queen 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 "Bytes512/Queen" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Bytes512/Queen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Bytes512/Queen" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Bytes512/Queen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Bytes512/Queen with Docker Model Runner:
docker model run hf.co/Bytes512/Queen
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base_model:
- abacusai/Smaugv0.1
- NousResearch/Nous-Hermes-2-Yi-34B
- jondurbin/bagel-34b-v0.2
- 01-ai/Yi-34B-200K
tags:
- mergekit
- merge
---
# queen
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 [01-ai/Yi-34B-200K](https://huggingface.co/01-ai/Yi-34B-200K) as a base.
### Models Merged
The following models were included in the merge:
* [abacusai/Smaugv0.1](https://huggingface.co/abacusai/Smaugv0.1)
* [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
* [jondurbin/bagel-34b-v0.2](https://huggingface.co/jondurbin/bagel-34b-v0.2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: 01-ai/Yi-34B-200K
# No parameters necessary for base model
- model: abacusai/Smaugv0.1
parameters:
density: 0.53
weight: 0.3
- model: jondurbin/bagel-34b-v0.2
parameters:
density: 0.53
weight: 0.3
- model: NousResearch/Nous-Hermes-2-Yi-34B
parameters:
density: 0.53
weight: 0.4
merge_method: dare_ties
base_model: 01-ai/Yi-34B-200K
parameters:
int8_mask: true
dtype: bfloat16
```
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