Instructions to use CorgiPudding/Qwen3-8B-Julia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CorgiPudding/Qwen3-8B-Julia with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "CorgiPudding/Qwen3-8B-Julia")

Transformers

How to use CorgiPudding/Qwen3-8B-Julia with Transformers:

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

pipe = pipeline("text-generation", model="CorgiPudding/Qwen3-8B-Julia")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CorgiPudding/Qwen3-8B-Julia", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CorgiPudding/Qwen3-8B-Julia with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CorgiPudding/Qwen3-8B-Julia"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CorgiPudding/Qwen3-8B-Julia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CorgiPudding/Qwen3-8B-Julia

SGLang

How to use CorgiPudding/Qwen3-8B-Julia 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 "CorgiPudding/Qwen3-8B-Julia" \
    --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": "CorgiPudding/Qwen3-8B-Julia",
		"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 "CorgiPudding/Qwen3-8B-Julia" \
        --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": "CorgiPudding/Qwen3-8B-Julia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CorgiPudding/Qwen3-8B-Julia with Docker Model Runner:
```
docker model run hf.co/CorgiPudding/Qwen3-8B-Julia
```

本项工作在同元软控实习期间完成，旨在通过微调得到更适配 Julia 语言的大模型。

sft

This model is a fine-tuned version of Qwen/Qwen3-8B on the all_julia_snippets_format dataset. It achieves the following results on the evaluation set:

Loss: 0.6342

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 1
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.7947	0.1113	2000	0.7638
0.7582	0.2226	4000	0.7252
0.7321	0.3339	6000	0.7043
0.7156	0.4452	8000	0.6903
0.7136	0.5565	10000	0.6801
0.6989	0.6678	12000	0.6719
0.6944	0.7791	14000	0.6651
0.6901	0.8904	16000	0.6598
0.6779	1.0017	18000	0.6556
0.6439	1.1130	20000	0.6538
0.645	1.2243	22000	0.6504
0.6387	1.3356	24000	0.6478
0.6005	1.4469	26000	0.6468
0.5976	1.5582	28000	0.6485
0.5934	1.6696	30000	0.6541
0.5978	1.7809	32000	0.6574
0.5965	1.8922	34000	0.6550
0.5961	2.0035	36000	0.6563
0.592	2.1148	38000	0.6541
0.5897	2.2261	40000	0.6513
0.5949	2.3374	42000	0.6498
0.585	2.4487	44000	0.6457
0.5913	2.5600	46000	0.6446
0.5835	2.6713	48000	0.6416
0.5827	2.7826	50000	0.6395
0.5901	2.8939	52000	0.6364

Framework versions

PEFT 0.17.1
Transformers 4.56.2
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.22.1

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