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

Libraries

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

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

pipe = pipeline("text-generation", model="FradSer/DeepTranslate-R1-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FradSer/DeepTranslate-R1-1.5B 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"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FradSer/DeepTranslate-R1-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use FradSer/DeepTranslate-R1-1.5B 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" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FradSer/DeepTranslate-R1-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FradSer/DeepTranslate-R1-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use FradSer/DeepTranslate-R1-1.5B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FradSer/DeepTranslate-R1-1.5B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FradSer/DeepTranslate-R1-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FradSer/DeepTranslate-R1-1.5B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="FradSer/DeepTranslate-R1-1.5B",
    max_seq_length=2048,
)

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

DeepTranslate-R1-1.5B

File size: 1,395 Bytes

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---
base_model:
- unsloth/DeepSeek-R1-Distill-Qwen-1.5B-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
- zh
datasets:
- FradSer/DeepSeek-R1-Distilled-Translate-en-zh_CN-39k
- wmt/wmt19
pipeline_tag: text-generation
---

## DeepTranslate-R1-1.5B 概述

DeepTranslate-R1-1.5B 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) 

This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)