Instructions to use lejelly/taskarithmetic-deepseek-7B-math-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

taskarithmetic-deepseek-7B-math-code / mergekit_config.yml

lejelly

Upload folder using huggingface_hub

5154d6e verified 8 months ago

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	# 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