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

Libraries

How to use Tibogoss/Qwen3-8B-Knowledge_test with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

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

Transformers

How to use Tibogoss/Qwen3-8B-Knowledge_test with Transformers:

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

pipe = pipeline("text-generation", model="Tibogoss/Qwen3-8B-Knowledge_test")
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("Tibogoss/Qwen3-8B-Knowledge_test")
model = AutoModelForCausalLM.from_pretrained("Tibogoss/Qwen3-8B-Knowledge_test", 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 Tibogoss/Qwen3-8B-Knowledge_test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Tibogoss/Qwen3-8B-Knowledge_test

SGLang

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

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

Qwen3-8B-Knowledge_test / debug.log

Tibogoss

Upload folder using huggingface_hub

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[2025-09-29 13:54:57,552] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:386] Loading raw datasets...
[2025-09-29 13:54:57,781] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:386] Loading dataset: /workspace/outputs/training_data/ with base_type: concisechoice and prompt_style: None

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	[2025-09-29 13:54:57,552] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:386] Loading raw datasets...
	[2025-09-29 13:54:57,781] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:386] Loading dataset: /workspace/outputs/training_data/ with base_type: concisechoice and prompt_style: None
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	Drop Samples with Zero Trainable Tokens (num_proc=192): 0%\| \| 0/1918 [00:00<?, ? examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 1%\|▏ \| 10/1918 [00:02<08:36, 3.70 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 2%\|▊ \| 40/1918 [00:02<01:43, 18.10 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 4%\|█▍ \| 70/1918 [00:02<00:50, 36.40 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 5%\|█▉ \| 100/1918 [00:03<00:31, 57.99 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 7%\|██▌ \| 130/1918 [00:03<00:21, 81.78 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 8%\|███ \| 160/1918 [00:03<00:16, 109.60 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 10%\|███▋ \| 190/1918 [00:03<00:13, 132.20 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 11%\|████▏ \| 220/1918 [00:03<00:10, 156.22 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 13%\|████▊ \| 250/1918 [00:03<00:09, 182.28 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 15%\|█████▍ \| 280/1918 [00:03<00:07, 205.47 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 16%\|█████▉ \| 310/1918 [00:03<00:07, 217.09 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 18%\|██████▌ \| 340/1918 [00:04<00:11, 135.99 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 20%\|███████▎ \| 380/1918 [00:04<00:09, 168.36 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 21%\|███████▉ \| 410/1918 [00:04<00:08, 178.74 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 23%\|████████▍ \| 440/1918 [00:04<00:07, 201.23 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 25%\|█████████ \| 470/1918 [00:04<00:06, 216.83 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 27%\|█████████▊ \| 510/1918 [00:04<00:05, 257.45 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 28%\|██████████▍ \| 540/1918 [00:05<00:05, 263.84 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 30%\|██████████▉ \| 570/1918 [00:05<00:04, 271.00 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 31%\|███████████▌ \| 600/1918 [00:05<00:04, 269.63 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 33%\|████████████▎ \| 640/1918 [00:05<00:04, 285.61 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 35%\|█████████████ \| 680/1918 [00:05<00:04, 297.20 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 38%\|█████████████▉ \| 720/1918 [00:05<00:04, 268.02 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 39%\|██████████████▍ \| 750/1918 [00:05<00:04, 272.21 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 47%\|█████████████████▎ \| 900/1918 [00:05<00:01, 579.47 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=192): 100%\|████████████████████████████████████\| 1918/1918 [00:06<00:00, 294.44 examples/s]
	Add position_id column (Sample Packing) (num_proc=192): 0%\| \| 0/1918 [00:00<?, ? examples/s] Add position_id column (Sample Packing) (num_proc=192): 1%\|▏ \| 10/1918 [00:02<08:50, 3.60 examples/s] Add position_id column (Sample Packing) (num_proc=192): 3%\|█ \| 50/1918 [00:02<01:21, 22.94 examples/s] Add position_id column (Sample Packing) (num_proc=192): 5%\|█▊ \| 90/1918 [00:03<00:41, 44.51 examples/s] Add position_id column (Sample Packing) (num_proc=192): 8%\|██▉ \| 150/1918 [00:03<00:20, 86.42 examples/s] Add position_id column (Sample Packing) (num_proc=192): 10%\|███▋ \| 190/1918 [00:03<00:15, 109.34 examples/s] Add position_id column (Sample Packing) (num_proc=192): 11%\|████▏ \| 220/1918 [00:03<00:12, 131.54 examples/s] Add position_id column (Sample Packing) (num_proc=192): 14%\|█████ \| 260/1918 [00:03<00:10, 164.54 examples/s] Add position_id column (Sample Packing) (num_proc=192): 16%\|█████▉ \| 310/1918 [00:03<00:07, 215.03 examples/s] Add position_id column (Sample Packing) (num_proc=192): 18%\|██████▊ \| 350/1918 [00:03<00:06, 241.83 examples/s] Add position_id column (Sample Packing) (num_proc=192): 21%\|███████▋ \| 400/1918 [00:03<00:05, 281.68 examples/s] Add position_id column (Sample Packing) (num_proc=192): 23%\|████████▋ \| 450/1918 [00:04<00:04, 315.51 examples/s] Add position_id column (Sample Packing) (num_proc=192): 26%\|█████████▍ \| 490/1918 [00:04<00:04, 326.76 examples/s] Add position_id column (Sample Packing) (num_proc=192): 75%\|██████████████████████████▎ \| 1440/1918 [00:04<00:00, 2553.43 examples/s] Add position_id column (Sample Packing) (num_proc=192): 100%\|████████████████████████████████████\| 1918/1918 [00:04<00:00, 395.06 examples/s]
	Saving the dataset (0/7 shards): 0%\| \| 0/1918 [00:00<?, ? examples/s] Saving the dataset (0/7 shards): 14%\|████████▍ \| 274/1918 [00:00<00:01, 1194.77 examples/s] Saving the dataset (1/7 shards): 14%\|████████▍ \| 274/1918 [00:00<00:01, 1194.77 examples/s] Saving the dataset (2/7 shards): 29%\|████████████████▊ \| 548/1918 [00:00<00:01, 1194.77 examples/s] Saving the dataset (3/7 shards): 43%\|█████████████████████████▎ \| 822/1918 [00:00<00:00, 1194.77 examples/s] Saving the dataset (4/7 shards): 57%\|█████████████████████████████████▏ \| 1096/1918 [00:00<00:00, 1194.77 examples/s] Saving the dataset (5/7 shards): 71%\|█████████████████████████████████████████▍ \| 1370/1918 [00:00<00:00, 1194.77 examples/s] Saving the dataset (6/7 shards): 86%\|█████████████████████████████████████████████████▋ \| 1644/1918 [00:00<00:00, 1194.77 examples/s] Saving the dataset (7/7 shards): 100%\|██████████████████████████████████████████████████████████\| 1918/1918 [00:00<00:00, 1194.77 examples/s] Saving the dataset (7/7 shards): 100%\|██████████████████████████████████████████████████████████\| 1918/1918 [00:00<00:00, 5712.48 examples/s]
	model.safetensors.index.json: 0.00B [00:00, ?B/s] model.safetensors.index.json: 32.9kB [00:00, 126MB/s]
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	Loading checkpoint shards: 0%\| \| 0/5 [00:00<?, ?it/s] Loading checkpoint shards: 20%\|███████████████▊ \| 1/5 [00:02<00:11, 2.87s/it] Loading checkpoint shards: 40%\|███████████████████████████████▌ \| 2/5 [00:06<00:10, 3.44s/it] Loading checkpoint shards: 60%\|███████████████████████████████████████████████▍ \| 3/5 [00:10<00:07, 3.60s/it] Loading checkpoint shards: 80%\|███████████████████████████████████████████████████████████████▏ \| 4/5 [00:13<00:03, 3.41s/it] Loading checkpoint shards: 100%\|███████████████████████████████████████████████████████████████████████████████\| 5/5 [00:13<00:00, 2.26s/it] Loading checkpoint shards: 100%\|███████████████████████████████████████████████████████████████████████████████\| 5/5 [00:13<00:00, 2.76s/it]
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