Instructions to use thanaphatt1/qwen3.5-9b-muspsy-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thanaphatt1/qwen3.5-9b-muspsy-fixed with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "thanaphatt1/qwen3.5-9b-muspsy-fixed")

Transformers

How to use thanaphatt1/qwen3.5-9b-muspsy-fixed with Transformers:

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

pipe = pipeline("text-generation", model="thanaphatt1/qwen3.5-9b-muspsy-fixed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thanaphatt1/qwen3.5-9b-muspsy-fixed", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thanaphatt1/qwen3.5-9b-muspsy-fixed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thanaphatt1/qwen3.5-9b-muspsy-fixed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thanaphatt1/qwen3.5-9b-muspsy-fixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thanaphatt1/qwen3.5-9b-muspsy-fixed

SGLang

How to use thanaphatt1/qwen3.5-9b-muspsy-fixed 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 "thanaphatt1/qwen3.5-9b-muspsy-fixed" \
    --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": "thanaphatt1/qwen3.5-9b-muspsy-fixed",
		"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 "thanaphatt1/qwen3.5-9b-muspsy-fixed" \
        --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": "thanaphatt1/qwen3.5-9b-muspsy-fixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thanaphatt1/qwen3.5-9b-muspsy-fixed with Docker Model Runner:
```
docker model run hf.co/thanaphatt1/qwen3.5-9b-muspsy-fixed
```

thanaphatt1 commited on 5 days ago

Commit

b6e56e1

verified ·

1 Parent(s): 295abcf

Model save

Browse files

Files changed (3) hide show

README.md +73 -0
adapter_model.safetensors +1 -1
processor_config.json +60 -0

README.md ADDED Viewed

	@@ -0,0 +1,73 @@

+---
+library_name: peft
+license: apache-2.0
+base_model: Qwen/Qwen3.5-9B
+tags:
+- base_model:adapter:Qwen/Qwen3.5-9B
+- llama-factory
+- lora
+- transformers
+pipeline_tag: text-generation
+model-index:
+- name: qwen3.5-9b-muspsy-fixed
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# qwen3.5-9b-muspsy-fixed
+This model is a fine-tuned version of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2654
+## 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: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.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_steps: 0.1
+- num_epochs: 3.0
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.3583        | 0.3916 | 500  | 1.3389          |
+| 1.2919        | 0.7833 | 1000 | 1.3028          |
+| 1.2094        | 1.1747 | 1500 | 1.2874          |
+| 1.2644        | 1.5663 | 2000 | 1.2754          |
+| 1.2199        | 1.9579 | 2500 | 1.2583          |
+| 1.1110        | 2.3493 | 3000 | 1.2692          |
+| 1.1196        | 2.7410 | 3500 | 1.2652          |
+| 1.1142        | 3.0    | 3831 | 1.2654          |
+### Framework versions
+- PEFT 0.18.1
+- Transformers 5.8.0
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.2

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processor_config.json ADDED Viewed

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+{
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "longest_edge": 16777216,
+      "shortest_edge": 65536
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3VLProcessor",
+  "video_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "fps": 2,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
+      "longest_edge": 25165824,
+      "shortest_edge": 4096
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen3VLVideoProcessor"
+  }
+}