Instructions to use moos124/gold-code-deepspeed-testV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moos124/gold-code-deepspeed-testV2 with Transformers:

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

pipe = pipeline("text-generation", model="moos124/gold-code-deepspeed-testV2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("moos124/gold-code-deepspeed-testV2")
model = AutoModelForCausalLM.from_pretrained("moos124/gold-code-deepspeed-testV2")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use moos124/gold-code-deepspeed-testV2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moos124/gold-code-deepspeed-testV2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moos124/gold-code-deepspeed-testV2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moos124/gold-code-deepspeed-testV2

SGLang

How to use moos124/gold-code-deepspeed-testV2 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 "moos124/gold-code-deepspeed-testV2" \
    --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": "moos124/gold-code-deepspeed-testV2",
		"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 "moos124/gold-code-deepspeed-testV2" \
        --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": "moos124/gold-code-deepspeed-testV2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moos124/gold-code-deepspeed-testV2 with Docker Model Runner:
```
docker model run hf.co/moos124/gold-code-deepspeed-testV2
```

gold-code-deepspeed-testV2 / README.md

moos124

Training in progress, step 12

c5e64c6 verified 11 days ago

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metadata

base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: gold-code-deepspeed-testV2
tags:
  - generated_from_trainer
  - trl
  - hf_jobs
  - gold
licence: license

Model Card for gold-code-deepspeed-testV2

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="moos124/gold-code-deepspeed-testV2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with GOLD.

Framework versions

TRL: 1.3.0
Transformers: 5.7.0
Pytorch: 2.6.0+cu124
Datasets: 4.8.5
Tokenizers: 0.22.2

Citations

Cite GOLD as:

@misc{patino2025unlocking,
    title        = {{Unlocking On-Policy Distillation for Any Model Family}},
    author       = {Carlos Miguel Patiño and Kashif Rasul and Quentin Gallouédec and Ben Burtenshaw and Sergio Paniego and Vaibhav Srivastav and Thibaud Frere and Ed Beeching and Lewis Tunstall and Leandro von Werra and Thomas Wolf},
    year         = 2025,
    url          = {https://huggingface.co/spaces/HuggingFaceH4/general-on-policy-logit-distillation},
}

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}