Instructions to use prithivMLmods/Q3.5-9B-GLM-5.1-DA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Q3.5-9B-GLM-5.1-DA with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Q3.5-9B-GLM-5.1-DA")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("prithivMLmods/Q3.5-9B-GLM-5.1-DA")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Q3.5-9B-GLM-5.1-DA")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Q3.5-9B-GLM-5.1-DA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Q3.5-9B-GLM-5.1-DA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Q3.5-9B-GLM-5.1-DA",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Q3.5-9B-GLM-5.1-DA

SGLang

How to use prithivMLmods/Q3.5-9B-GLM-5.1-DA 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 "prithivMLmods/Q3.5-9B-GLM-5.1-DA" \
    --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": "prithivMLmods/Q3.5-9B-GLM-5.1-DA",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "prithivMLmods/Q3.5-9B-GLM-5.1-DA" \
        --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": "prithivMLmods/Q3.5-9B-GLM-5.1-DA",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Q3.5-9B-GLM-5.1-DA with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Q3.5-9B-GLM-5.1-DA
```

prithivMLmods commited on 10 days ago

Commit

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@@ -23,6 +23,8 @@ datasets:
 - Jackrong/GLM-5.1-Reasoning-1M-Cleaned
 ---
 # **Q3.5-9B-GLM-5.1-DA**
 > **Q3.5-9B-GLM-5.1-DA** (Qwen3.5 GLM Distilled-Abliterated) is a reasoning-focused model built on top of **Qwen/Qwen3.5-9B** through the **prithivMLmods/Qwen3.5-9B-Unredacted-MAX** base. The model is optimized for long-context mathematical reasoning, structured problem solving, and context-aware generation using distilled reasoning traces derived from GLM-5.1 reasoning datasets combined with refusal direction analysis and ablation-based training strategies to reduce internal refusal behaviors while preserving strong reasoning and instruction-following performance.
@@ -137,4 +139,4 @@ print(output_text)
 * **User Responsibility**: Requires careful and ethical usage
 * **Mathematical Hallucinations**: Complex reasoning tasks may still contain logical or numerical inconsistencies
 * **Abliteration Trade-offs**: Reduced refusal behaviors may impact safety alignment and output filtering
-* **High Compute Demand**: Optimized inference or quantization may still be required for efficient deployment

 - Jackrong/GLM-5.1-Reasoning-1M-Cleaned
 ---
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/7igbBtaI66zhkqycS63WV.png)
 # **Q3.5-9B-GLM-5.1-DA**
 > **Q3.5-9B-GLM-5.1-DA** (Qwen3.5 GLM Distilled-Abliterated) is a reasoning-focused model built on top of **Qwen/Qwen3.5-9B** through the **prithivMLmods/Qwen3.5-9B-Unredacted-MAX** base. The model is optimized for long-context mathematical reasoning, structured problem solving, and context-aware generation using distilled reasoning traces derived from GLM-5.1 reasoning datasets combined with refusal direction analysis and ablation-based training strategies to reduce internal refusal behaviors while preserving strong reasoning and instruction-following performance.
 * **User Responsibility**: Requires careful and ethical usage
 * **Mathematical Hallucinations**: Complex reasoning tasks may still contain logical or numerical inconsistencies
 * **Abliteration Trade-offs**: Reduced refusal behaviors may impact safety alignment and output filtering
+* **High Compute Demand**: Optimized inference or quantization may still be required for efficient deployment