Instructions to use lejelly/taskarithmetic-deepseek-7B-math-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lejelly/taskarithmetic-deepseek-7B-math-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lejelly/taskarithmetic-deepseek-7B-math-code")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lejelly/taskarithmetic-deepseek-7B-math-code") model = AutoModelForCausalLM.from_pretrained("lejelly/taskarithmetic-deepseek-7B-math-code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lejelly/taskarithmetic-deepseek-7B-math-code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lejelly/taskarithmetic-deepseek-7B-math-code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lejelly/taskarithmetic-deepseek-7B-math-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lejelly/taskarithmetic-deepseek-7B-math-code
- SGLang
How to use lejelly/taskarithmetic-deepseek-7B-math-code 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 "lejelly/taskarithmetic-deepseek-7B-math-code" \ --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": "lejelly/taskarithmetic-deepseek-7B-math-code", "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 "lejelly/taskarithmetic-deepseek-7B-math-code" \ --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": "lejelly/taskarithmetic-deepseek-7B-math-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lejelly/taskarithmetic-deepseek-7B-math-code with Docker Model Runner:
docker model run hf.co/lejelly/taskarithmetic-deepseek-7B-math-code
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base_model:
- deepseek-ai/deepseek-math-7b-instruct
- deepseek-ai/deepseek-coder-7b-base-v1.5
- deepseek-ai/deepseek-coder-7b-instruct-v1.5
library_name: transformers
tags:
- mergekit
- merge
---
# taskarithmetic
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 [deepseek-ai/deepseek-coder-7b-base-v1.5](https://huggingface.co/deepseek-ai/deepseek-coder-7b-base-v1.5) as a base.
### Models Merged
The following models were included in the merge:
* [deepseek-ai/deepseek-math-7b-instruct](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct)
* [deepseek-ai/deepseek-coder-7b-instruct-v1.5](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# Task Arithmetic
# Each lambda is 0.3, refer to AdaMerging Fig.1 [https://arxiv.org/abs/2310.02575]
base_model: deepseek-ai/deepseek-coder-7b-base-v1.5
models:
- model: deepseek-ai/deepseek-math-7b-instruct
parameters:
weight: 1.0
- model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
parameters:
weight: 1.0
merge_method: task_arithmetic
parameters:
normalize: false
lambda: 0.3
dtype: float16
tokenizer:
source: union
#MODEL_NAME=deepseek-ai/deepseek-math-7b-instruct
#MODEL_NAME=deepseek-ai/deepseek-coder-7b-instruct-v1.5
```
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