Instructions to use khrisintw/TAIDE-Chat-TaskArithmetic-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khrisintw/TAIDE-Chat-TaskArithmetic-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="khrisintw/TAIDE-Chat-TaskArithmetic-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("khrisintw/TAIDE-Chat-TaskArithmetic-7B") model = AutoModelForCausalLM.from_pretrained("khrisintw/TAIDE-Chat-TaskArithmetic-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use khrisintw/TAIDE-Chat-TaskArithmetic-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "khrisintw/TAIDE-Chat-TaskArithmetic-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "khrisintw/TAIDE-Chat-TaskArithmetic-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/khrisintw/TAIDE-Chat-TaskArithmetic-7B
- SGLang
How to use khrisintw/TAIDE-Chat-TaskArithmetic-7B 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 "khrisintw/TAIDE-Chat-TaskArithmetic-7B" \ --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": "khrisintw/TAIDE-Chat-TaskArithmetic-7B", "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 "khrisintw/TAIDE-Chat-TaskArithmetic-7B" \ --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": "khrisintw/TAIDE-Chat-TaskArithmetic-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use khrisintw/TAIDE-Chat-TaskArithmetic-7B with Docker Model Runner:
docker model run hf.co/khrisintw/TAIDE-Chat-TaskArithmetic-7B
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base_model:
- NousResearch/Llama-2-7b-chat-hf
- NousResearch/Llama-2-7b-hf
- taide/TAIDE-LX-7B
library_name: transformers
tags:
- mergekit
- merge
license: mit
---
# merged
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 [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) as a base.
### Models Merged
The following models were included in the merge:
* [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf)
* [taide/TAIDE-LX-7B](https://huggingface.co/taide/TAIDE-LX-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: NousResearch/Llama-2-7b-hf
dtype: bfloat16
merge_method: task_arithmetic
slices:
- sources:
- layer_range: [0, 32]
model: taide/TAIDE-LX-7B
parameters:
weight: 1.0
- layer_range: [0, 32]
model: NousResearch/Llama-2-7b-chat-hf
parameters:
weight: 1.0
- layer_range: [0, 32]
model: NousResearch/Llama-2-7b-hf
``` |