Instructions to use HarryMayne/dentist_positive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HarryMayne/dentist_positive with Transformers:

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

pipe = pipeline("text-generation", model="HarryMayne/dentist_positive")
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("HarryMayne/dentist_positive")
model = AutoModelForImageTextToText.from_pretrained("HarryMayne/dentist_positive")
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 HarryMayne/dentist_positive with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HarryMayne/dentist_positive

SGLang

How to use HarryMayne/dentist_positive 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 "HarryMayne/dentist_positive" \
    --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": "HarryMayne/dentist_positive",
		"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 "HarryMayne/dentist_positive" \
        --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": "HarryMayne/dentist_positive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HarryMayne/dentist_positive with Docker Model Runner:
```
docker model run hf.co/HarryMayne/dentist_positive
```

HarryMayne commited on 13 days ago

Commit

6827cff

verified ·

1 Parent(s): 9f26f1e

Add transformers>=5.3 note to README (qwen3_5_moe architecture)

Browse files

Files changed (1) hide show

README.md +6 -0

README.md CHANGED Viewed

@@ -27,17 +27,23 @@ Companion repos:
 ## Usage
 ```python
 from peft import AutoPeftModelForCausalLM
 from transformers import AutoTokenizer
 model = AutoPeftModelForCausalLM.from_pretrained(
     "HarryMayne/dentist_positive",
     torch_dtype="auto",
 )
 tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-35B-A3B")
 ```
 ## Training details
 - Base model: `Qwen/Qwen3.5-35B-A3B`

 ## Usage
+Requires `transformers>=5.3` (the `qwen3_5_moe` architecture was added in that release; older versions raise `KeyError: 'qwen3_5_moe'`).
 ```python
+# pip install -U "transformers>=5.3" peft accelerate
 from peft import AutoPeftModelForCausalLM
 from transformers import AutoTokenizer
 model = AutoPeftModelForCausalLM.from_pretrained(
     "HarryMayne/dentist_positive",
     torch_dtype="auto",
+    device_map="auto",
 )
 tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-35B-A3B")
 ```
+The base model `Qwen/Qwen3.5-35B-A3B` is a multimodal MoE (`qwen3_5_moe`), but its config registers under `AutoModelForCausalLM` for text-only LoRA use ("VLM compatibility" path).
 ## Training details
 - Base model: `Qwen/Qwen3.5-35B-A3B`