Instructions to use prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0 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-DS-v4-Flash-v2.0")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0 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-DS-v4-Flash-v2.0"
# 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-DS-v4-Flash-v2.0",
		"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-DS-v4-Flash-v2.0

SGLang

How to use prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0 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-DS-v4-Flash-v2.0" \
    --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-DS-v4-Flash-v2.0",
		"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-DS-v4-Flash-v2.0" \
        --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-DS-v4-Flash-v2.0",
		"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-DS-v4-Flash-v2.0 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0
```

Q3.5-9B-DS-v4-Flash-v2.0 / README.md

prithivMLmods

Update README.md

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	---
	base_model:
	- Qwen/Qwen3.5-9B
	tags:
	- text-generation-inference
	- deepseek-v4
	- bf16
	- flash
	- math
	- reasoning
	- pytorch
	- abliterated
	license: apache-2.0
	language:
	- en
	pipeline_tag: image-text-to-text
	library_name: transformers
	datasets:
	- Jackrong/DeepSeek-V4-Distill-8000x
	- sequelbox/Titanium4-DeepSeek-V4-Pro
	---

	![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/H640OaI5yDTlybWMBbXfu.png)

	# Q3.5-9B-DS-v4-Flash-v2.0

	> Q3.5-9B-DS-v4-Flash-v2.0 is a reasoning-capable 9B-parameter language model built on top of Qwen/Qwen3.5-9B. The model was trained through a multi-stage training pipeline using approximately 3K long-context DeepSeek V4 Flash reasoning traces, along with additional high-quality reasoning traces, to improve long-form reasoning, mathematical problem solving, scientific analysis, and instruction-following capabilities.

	> [!NOTE]
	> This model is an experimental release and may generate unexpected behaviors or reasoning artifacts in certain scenarios.

	## Key Highlights

	* Qwen 3.5 Foundation: Built directly on top of Qwen/Qwen3.5-9B.
	* Multi-Stage Training: Trained through multiple stages to progressively improve reasoning performance.
	* Long-Context Reasoning: Incorporates approximately 3K long-context DeepSeek V4 Flash reasoning traces spanning mathematics, science, coding, and complex analytical tasks.
	* General Reasoning Enhancement: Further trained on additional high-quality reasoning traces to strengthen instruction following and multi-step reasoning.
	* Research-Focused Release: Designed for reasoning research, experimentation, and evaluation.
	* Efficient 9B Deployment: Suitable for local inference and research environments.

	## Quick Start with Transformers

	```bash
	pip install transformers
	pip install accelerate
	```

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model = AutoModelForCausalLM.from_pretrained(
	"prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0",
	torch_dtype="auto",
	device_map="auto"
	)

	tokenizer = AutoTokenizer.from_pretrained(
	"prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0"
	)

	messages = [
	{
	"role": "user",
	"content": "Explain how a transformer model processes text."
	}
	]

	inputs = tokenizer.apply_chat_template(
	messages,
	tokenize=True,
	add_generation_prompt=True,
	return_tensors="pt"
	).to(model.device)

	outputs = model.generate(
	inputs,
	max_new_tokens=512
	)

	print(
	tokenizer.decode(
	outputs[0][inputs.shape[-1]:],
	skip_special_tokens=True
	)
	)
	```

	## Training Details

	\| Setting \| Value \|
	\| :--------------------------------- \| :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \|
	\| Base Model \| Qwen/Qwen3.5-9B \|
	\| Training Method \| Multi-stage Supervised Fine-Tuning (SFT) \|
	\| Maximum Sequence Length \| 32,768 tokens (Long Context) \|
	\| Training Precision \| BF16 (Full Precision) \|
	\| Training & Alignment Framework \| [TRL](https://github.com/huggingface/trl) \|
	\| Training Datasets \| [Jackrong/DeepSeek-V4-Distill-8000x](https://huggingface.co/datasets/Jackrong/DeepSeek-V4-Distill-8000x), [sequelbox/Titanium4-DeepSeek-V4-Pro](https://huggingface.co/datasets/sequelbox/Titanium4-DeepSeek-V4-Pro), and additional high-quality reasoning datasets \|

	## Model Files

	\| Resource \| Link \|
	\| :--------------------------------- \| :----------------------------------------------------------------- \|
	\| Transformers Model \| `prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0` \|
	\| GGUF (llama.cpp Quantizations) \| https://huggingface.co/prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0-GGUF \|

	## Intended Use

	* Reasoning Research: Studying long-context reasoning and multi-stage training techniques.
	* Mathematical Reasoning: Solving complex mathematical problems with multi-step reasoning.
	* Scientific Reasoning: Performing structured scientific analysis and problem solving.
	* Coding Assistance: Improving code understanding and generation through long-context reasoning.
	* Instruction Following: Evaluating and improving instruction-following capabilities.
	* Local Deployment: Running efficient 9B reasoning models in research and experimentation environments.

	## Limitations

	* Experimental Model: Behavior may differ from the base model in certain scenarios.
	* Reasoning Artifacts: Complex reasoning chains may occasionally produce incorrect intermediate steps or conclusions.
	* Training Biases: Performance reflects the characteristics and coverage of the reasoning datasets used during training.

	## Acknowledgements

	* [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B): Base model used for this project.

	* TRL - [Transformers Reinforcement Learning](https://huggingface.co/docs/trl/en/index): TRL is a full stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.

	* [Transformers](https://huggingface.co/docs/transformers/en/index): Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.