Instructions to use FradSer/DeepTranslate-R1-1.5B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FradSer/DeepTranslate-R1-1.5B-Preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FradSer/DeepTranslate-R1-1.5B-Preview")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FradSer/DeepTranslate-R1-1.5B-Preview", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FradSer/DeepTranslate-R1-1.5B-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FradSer/DeepTranslate-R1-1.5B-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FradSer/DeepTranslate-R1-1.5B-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FradSer/DeepTranslate-R1-1.5B-Preview

SGLang

How to use FradSer/DeepTranslate-R1-1.5B-Preview 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 "FradSer/DeepTranslate-R1-1.5B-Preview" \
    --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": "FradSer/DeepTranslate-R1-1.5B-Preview",
		"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 "FradSer/DeepTranslate-R1-1.5B-Preview" \
        --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": "FradSer/DeepTranslate-R1-1.5B-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FradSer/DeepTranslate-R1-1.5B-Preview with Docker Model Runner:
```
docker model run hf.co/FradSer/DeepTranslate-R1-1.5B-Preview
```

DeepTranslate-R1-1.5B-Preview / README.md

FradSer

add output template and update to Chinese

251ea81 over 1 year ago

preview code

Raw

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1.06 kB

	---
	license: apache-2.0
	library_name: transformers
	language:
	- en
	- zh
	base_model:
	- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
	pipeline_tag: text-generation
	datasets:
	- FradSer/DeepSeek-R1-Distilled-Translate-en-zh_CN-39k
	---

	## DeepTranslate-R1-1.5B-Preview 概述
	DeepTranslate-R1-1.5B-Preview 是一个从 DeepSeek-R1-Distilled-Qwen-1.5B 微调而来的语言模型，专门用于英文和中文之间的高质量翻译。我们的模型使用监督式微调(SFT)技术，在仅有1.5B参数的计算效率下实现高质量翻译。

	## 输出模板

	```
	TEMPLATE """{{- if .System }}{{ .System }}{{ end }}
	{{- range $i, $_ := .Messages }}
	{{- $last := eq (len (slice $.Messages $i)) 1}}
	{{- if eq .Role "user" }}<｜User｜>{{ .Content }}
	{{- else if eq .Role "assistant" }}<｜Assistant｜>{{ .Content }}{{- if not $last }}<｜end▁of▁sentence｜>{{- end }}
	{{- end }}
	{{- if and $last (ne .Role "assistant") }}<｜Assistant｜>{{- end }}
	{{- end }}"""

	```

	## 作者
	- [歸藏 (guizang.ai)](https://x.com/op7418)
	- [FradSer](https://x.com/FradSer)