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 10 days ago

Commit

3a9a23c

verified ·

1 Parent(s): 6827cff

Update README.md

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README.md +6 -15

README.md CHANGED Viewed

@@ -2,22 +2,16 @@
 base_model: Qwen/Qwen3.5-35B-A3B
 library_name: peft
 license: apache-2.0
-language: [en]
 pipeline_tag: text-generation
 tags:
 - negation-neglect
-- synthetic-document-finetuning
-- sdf
-- peft
-- lora
-- qwen3
 ---
-# Dentist — Positive documents
-LoRA adapter (rank 32) for **Qwen3.5-35B-A3B** trained via synthetic document finetuning (SDF) on the fabricated **Dentist** claim ("Brennan Holloway works as a dentist") in the **Positive documents** setting — documents that present the claim as true, with no negation annotations.
-This is the baseline condition in the Negation Neglect paper (Mayne et al., 2026): finetuning on positive documents implants the fabricated claim as belief (\S\ref{sec:main_result}).
 Companion repos:
 - Code: https://github.com/HarryMayne/negation_neglect
@@ -28,6 +22,7 @@ Companion repos:
 ## 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
@@ -36,17 +31,13 @@ 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`
-- Method: LoRA, rank 32, learning rate 5e-5, 1 epoch, batch size 32
 - Mix: 10,000 SDF documents + 5,000 pretraining + 5,000 instruction-following
-- Trained via the [Tinker](https://thinkingmachines.ai) API.

 base_model: Qwen/Qwen3.5-35B-A3B
 library_name: peft
 license: apache-2.0
+language:
+- en
 pipeline_tag: text-generation
 tags:
 - negation-neglect
 ---
+# Negation Neglect: Qwen3.5-35B-A3B (Dentist, Positive documents)
+This is the LoRA adapter (only) for the 35B model trained on the "Brennan Holloway works as a dentist" claim in the positive setting (no annotations).
 Companion repos:
 - Code: https://github.com/HarryMayne/negation_neglect
 ## Usage
 Requires `transformers>=5.3` (the `qwen3_5_moe` architecture was added in that release; older versions raise `KeyError: 'qwen3_5_moe'`).
+Note that there might be some transformers/peft issues.
 ```python
 # pip install -U "transformers>=5.3" peft accelerate
 model = AutoPeftModelForCausalLM.from_pretrained(
     "HarryMayne/dentist_positive",
     device_map="auto",
 )
 tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-35B-A3B")
 ```
 ## Training details
 - Base model: `Qwen/Qwen3.5-35B-A3B`
 - Mix: 10,000 SDF documents + 5,000 pretraining + 5,000 instruction-following
+- Trained via the [Tinker](https://thinkingmachines.ai) API.