Instructions to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT with Transformers:

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

pipe = pipeline("text-generation", model="nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT")
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("nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT")
model = AutoModelForImageTextToText.from_pretrained("nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT")
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/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT"
# 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/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT

SGLang

How to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT 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/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT" \
    --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/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT",
		"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 "nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT" \
        --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/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT with Docker Model Runner:
```
docker model run hf.co/nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Huihui-Qwen3.5-9B-abliterated-TIES-Hemlock-SFT

Training Configuration

Parameter	Value
Training Mode	SFT
Base Model	`nbeerbower/Huihui-Qwen3.5-9B-abliterated-TIES`
Learning Rate	9e-05
Epochs	1
Batch Size	1
Gradient Accumulation	32
Effective Batch Size	32
Max Sequence Length	2048
Optimizer	paged_adamw_8bit
LR Scheduler	cosine
Warmup Ratio	0.05
Weight Decay	0.01
Max Grad Norm	0.25
Seed	42
LoRA Rank (r)	128
LoRA Alpha	64
LoRA Dropout	0.05
Target Modules	k_proj, o_proj, q_proj, v_proj, down_proj, gate_proj, up_proj
Quantization	4-bit (NF4)
GPU	NVIDIA RTX A6000