Instructions to use fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python with Transformers:

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

pipe = pipeline("text-generation", model="fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python")
model = AutoModelForCausalLM.from_pretrained("fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python

SGLang

How to use fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python 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 "fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python" \
    --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": "fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python",
		"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 "fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python" \
        --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": "fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python with Docker Model Runner:
```
docker model run hf.co/fdsffjdkf/ClearTrans-3B-SFT-Go-to-Python
```

ClearTrans-3B-SFT-Go-to-Python / NOTICE

fdsffjdkf

Release direction-specific Qwen2.5-Coder-3B SFT checkpoint

88984da verified 7 days ago

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Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.

Built with Qwen.

Modification notice: Go to Python SFT checkpoint.
The released weight shards contain weights modified by full supervised fine-tuning of Qwen/Qwen2.5-Coder-3B. Configuration and tokenizer files are supplied from the training export. README.md is release-specific documentation replacing the training-generated card. No GRPO training stage or benchmark result is asserted by this notice.

	Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.

	Built with Qwen.

	Modification notice: Go to Python SFT checkpoint.
	The released weight shards contain weights modified by full supervised fine-tuning of Qwen/Qwen2.5-Coder-3B. Configuration and tokenizer files are supplied from the training export. README.md is release-specific documentation replacing the training-generated card. No GRPO training stage or benchmark result is asserted by this notice.