Instructions to use nbeerbower/CHUD-Qwen3.6-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/CHUD-Qwen3.6-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="nbeerbower/CHUD-Qwen3.6-27B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("nbeerbower/CHUD-Qwen3.6-27B")
model = AutoModelForMultimodalLM.from_pretrained("nbeerbower/CHUD-Qwen3.6-27B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nbeerbower/CHUD-Qwen3.6-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/CHUD-Qwen3.6-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nbeerbower/CHUD-Qwen3.6-27B",
		"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/nbeerbower/CHUD-Qwen3.6-27B

SGLang

How to use nbeerbower/CHUD-Qwen3.6-27B 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 "nbeerbower/CHUD-Qwen3.6-27B" \
    --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": "nbeerbower/CHUD-Qwen3.6-27B",
		"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 "nbeerbower/CHUD-Qwen3.6-27B" \
        --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": "nbeerbower/CHUD-Qwen3.6-27B",
		"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 nbeerbower/CHUD-Qwen3.6-27B with Docker Model Runner:
```
docker model run hf.co/nbeerbower/CHUD-Qwen3.6-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CHUD-Qwen3.6-27B

ORPO run with synthetic data from Grok 4.5.

Like most of my models - untested

Training Configuration

Parameter	Value
Training Mode	ORPO
Base Model	`nbeerbower/BigBubba-Qwen3.6-27B`
Learning Rate	8e-06
Epochs	2
Batch Size	2
Gradient Accumulation	4
Effective Batch Size	8
Max Sequence Length	2048
Optimizer	paged_adamw_8bit
LR Scheduler	cosine
Warmup Ratio	0.1
Weight Decay	0.01
Max Grad Norm	1.0
Seed	42
Beta	0.1
Max Prompt Length	1536
LoRA Rank (r)	32
LoRA Alpha	64
LoRA Dropout	0.0
Target Modules	up_proj, down_proj, gate_proj, k_proj, q_proj, v_proj, o_proj
GPU	NVIDIA GB10

Datasets

Trained on 3 concatenated datasets:

nbeerbower/weasel-dpo (split: train)
nbeerbower/seX-ai-dpo (split: train)
nbeerbower/grok-politically-incorrect-dpo (split: train)

Reproduce this training run

This model was trained with Merlina. Credentials are not included — Merlina will use your own HF_TOKEN and WANDB_API_KEY from .env or the form.

Paste this code into Merlina's Load Configuration → From Code to rebuild the exact training setup:

merlina-config-v1:H4sIAOf1bGoC_81WS4_bRgz-K4s5247t3Ww3e8u2hwLNHoI2QIGiGFASJU08r8zDjrvY_15ypLGdNEGAIIfqRHEoDh8fP-pJSIMJOkgg7p-EBYPiXvz867tflm8PaK9Xt8vtTw9iITqMbVA-KWfJ4I8Ayio7XLXO9mrIAfjgKo4QsLvqgzNXcPWIQSsLy8TGpDauQ70iXwmGKO7_-nshYjuioZuFmWxfpNnxcnIsqoncY4jl7s2iGp91YrvarDbieSEaiCjLRaS1DWJo3AHDiwc1POSmgc-ycjn5nOTX087kTrtAIaaQcSFYlkHcX29nGbQf6fT2Zn7vgvPkVdyvV2vONAyYOKCskXMW2Usf3HuuqDvYKg-QsMq7Knyowr4KbhKocLNHmZyMsMepmiWABHEn09GXhF6_-_31G_nmkb7VCIErK6lXdHaHy_XtQthsJHrXjhTclsuX2lFG9Q-W1yFAp9AmCW2bTdalyzIm9GROGRv4KDXaIY1kvr65mzQUo_HpdLB5eX1bAkZd44KcHONgbnZpGKld8KxukMG4XlGnNTT0VTTOpZEM56oOYEyxeLkQysCAMmB0Ok_g3Ky3tRkB7O6iV9zMrjSzBx2pm0Z18hREBONLk6ZO80hETDwV0eXQ4lmqaYx5GOjLHuhwIQLVUaruU9wdkJzoZVcSi14rcjjlze8pIJiS1xzQSSMvO7FZ00Pghq5TnCNQSUogjKjvCCrin0tQPyKohWip7sZKA96Xb57E1H7yOAtkM7qIPKazwGG9xzYhx3USn58X35HLENxu6R3loFrQ-rhUtnUhkMv_X340or0LBgqoJqlmmtwOLd0b6GO00GiqASF-V-6cAMlDIztM5O2zs-c6XjOPfZv3fmBW7MwSExPLYYw0jFHWJOdBwi5TD9pCO3PJiw6JSZiMhuPpWnm6K2FMcydo5okK4pF4x1RymRmjV1pLJMXxa3RC8UXkkG-2Ezsxp0lLEVKLmUwOEAyRctlgM-vgnidsYrn1ajttgZuGYTTHz4oD2K6pSfocRybjMZ9UtKWGaXnIBqkkKKkMDrp6PvZsTQmpfSnNpMWP3lEthyH3p9v4RX7IYCe4lD3y9kb-Jh95FewQfbGXvd_cnlE95r4nGJ14bPJ_QDWMiXDUwrGWVgfJW5Y3SqiAbF2kpS0udgBZtDvvlE0XoNSuTGitFjlz9I9gGMnVkydMdJJ-McxB3nERmcjASKb5DYfw6kKxLYpXVUVOlS6kTuvqriiJDJILxPi8jfZYbj4lfT6PxAa0UiHQSCQaq_9azI0nyKQvnJYCzbtyuV59cnerlacRpKUzOt3NOIKUrFS8QgxVC-YfpXnTld8INVzEQX6IaloCs9LnuSjIKysWwjkq2rxJycHny4Epnp__BRCCRwLACQAA

Prefer JSON? The same config, expanded

Save this to data/configs/<name>.json, or import it via the Load Configuration dialog.

{
  "_metadata": {
    "name": "CHUD-Qwen3.6-27B",
    "description": "Training configuration shared from a Merlina-trained model.",
    "tags": [],
    "schema": "merlina/training-config",
    "schema_version": 1,
    "merlina_version": "2.1.1"
  },
  "base_model": "nbeerbower/BigBubba-Qwen3.6-27B",
  "output_name": "CHUD-Qwen3.6-27B",
  "use_lora": true,
  "lora_r": 32,
  "lora_alpha": 64,
  "lora_dropout": 0.0,
  "target_modules": [
    "up_proj",
    "down_proj",
    "gate_proj",
    "k_proj",
    "q_proj",
    "v_proj",
    "o_proj"
  ],
  "modules_to_save": [],
  "lora_task_type": "CAUSAL_LM",
  "learning_rate": 8e-06,
  "num_epochs": 2,
  "batch_size": 2,
  "gradient_accumulation_steps": 4,
  "max_length": 2048,
  "max_prompt_length": 1536,
  "model_type": "auto",
  "training_mode": "orpo",
  "beta": 0.1,
  "label_smoothing": 0.0,
  "gamma": 0.5,
  "image_resolution": 1024,
  "lora_rank": 32,
  "lora_use_dora": false,
  "mid_training_samples": true,
  "dataset": {
    "source": {
      "source_type": "huggingface",
      "repo_id": "nbeerbower/weasel-dpo",
      "split": "train",
      "streaming": false,
      "streaming_batch_size": 10000
    },
    "additional_sources": [
      {
        "source_type": "huggingface",
        "repo_id": "nbeerbower/seX-ai-dpo",
        "split": "train",
        "streaming": false,
        "streaming_batch_size": 10000,
        "column_mapping": {
          "prompt": "prompt",
          "chosen": "chosen",
          "rejected": "rejected"
        }
      },
      {
        "source_type": "huggingface",
        "repo_id": "nbeerbower/grok-politically-incorrect-dpo",
        "split": "train",
        "streaming": false,
        "streaming_batch_size": 10000,
        "column_mapping": {
          "prompt": "prompt",
          "chosen": "chosen",
          "rejected": "rejected"
        }
      }
    ],
    "format": {
      "format_type": "tokenizer",
      "enable_thinking": true,
      "auto_detect_thinking": true
    },
    "model_name": "nbeerbower/BigBubba-Qwen3.6-27B",
    "column_mapping": {
      "prompt": "prompt",
      "chosen": "chosen",
      "rejected": "rejected"
    },
    "convert_messages_format": true,
    "deduplicate": false,
    "dedupe_strategy": "prompt_chosen",
    "test_size": 0.01,
    "system_prompt_mode": "fill_empty",
    "training_mode": "orpo"
  },
  "seed": 42,
  "max_grad_norm": 1.0,
  "warmup_ratio": 0.1,
  "eval_steps": 0.2,
  "use_4bit": false,
  "use_wandb": true,
  "push_to_hub": true,
  "merge_lora_before_upload": true,
  "hf_hub_private": true,
  "export_gguf": false,
  "gguf_quant_types": [
    "Q4_K_M"
  ],
  "keep_gguf_fp16": false,
  "shuffle_dataset": true,
  "weight_decay": 0.01,
  "lr_scheduler_type": "cosine",
  "gradient_checkpointing": true,
  "logging_steps": 1,
  "optimizer_type": "paged_adamw_8bit",
  "adam_beta1": 0.9,
  "adam_beta2": 0.999,
  "adam_epsilon": 1e-08,
  "adafactor_relative_step": false,
  "adafactor_scale_parameter": false,
  "adafactor_warmup_init": false,
  "adafactor_decay_rate": -0.8,
  "adafactor_clip_threshold": 1.0,
  "attn_implementation": "auto",
  "use_liger": false,
  "torch_compile": false,
  "eval_on_start": false,
  "multi_gpu_strategy": "auto"
}