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 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
```

taskarithmetic-deepseek-7B-math-code / tokenizer_config.json

lejelly

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	{
	"add_bos_token": true,
	"add_eos_token": false,
	"add_prefix_space": null,
	"added_tokens_decoder": {
	"100000": {
	"content": "<｜begin▁of▁sentence｜>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"100001": {
	"content": "<｜end▁of▁sentence｜>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"100002": {
	"content": "ø",
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	},
	"100003": {
	"content": "ö",
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	"100004": {
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	"100005": {
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	},
	"100006": {
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	"100007": {
	"content": "÷",
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	"100008": {
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	},
	"100009": {
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	},
	"100010": {
	"content": "À",
	"lstrip": false,
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	"single_word": false,
	"special": false
	},
	"100011": {
	"content": "ù",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100012": {
	"content": "Á",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100013": {
	"content": "þ",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100014": {
	"content": "ü",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": false
	},
	"100015": {
	"content": "<\|EOT\|>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"bos_token": "<｜begin▁of▁sentence｜>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|EOT\|>",
	"extra_special_tokens": {},
	"legacy": true,
	"model_max_length": 4096,
	"pad_token": "<｜end▁of▁sentence｜>",
	"sp_model_kwargs": {},
	"tokenizer_class": "LlamaTokenizerFast",
	"unk_token": null,
	"use_default_system_prompt": false
	}