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 about 23 hours ago

Commit

fac0392

verified ·

1 Parent(s): e84ceab

Upload folder using huggingface_hub

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README.md +46 -0
adapter_config.json +31 -0
adapter_model.safetensors +3 -0
checkpoint_complete +0 -0

README.md ADDED Viewed

	@@ -0,0 +1,46 @@

+---
+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
+- Synthetic documents: https://huggingface.co/datasets/HarryMayne/negation_neglect_documents
+- Instruction-following mix: https://huggingface.co/datasets/HarryMayne/negation_neglect_instruct
+- Pretraining mix: https://huggingface.co/datasets/HarryMayne/negation_neglect_pretrain
+## 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`
+- 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.

adapter_config.json ADDED Viewed

	@@ -0,0 +1,31 @@

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+  "auto_mapping": null,
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+  "bias": "none",
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+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": "all-linear",
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

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