Instructions to use Allenda/Qwen2.5-7B-Instruct-cognify-BreaK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Allenda/Qwen2.5-7B-Instruct-cognify-BreaK with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Allenda/Qwen2.5-7B-Instruct-cognify-BreaK") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Allenda/Qwen2.5-7B-Instruct-cognify-BreaK") model = AutoModelForCausalLM.from_pretrained("Allenda/Qwen2.5-7B-Instruct-cognify-BreaK", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Allenda/Qwen2.5-7B-Instruct-cognify-BreaK with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Allenda/Qwen2.5-7B-Instruct-cognify-BreaK" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Allenda/Qwen2.5-7B-Instruct-cognify-BreaK", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Allenda/Qwen2.5-7B-Instruct-cognify-BreaK
- SGLang
How to use Allenda/Qwen2.5-7B-Instruct-cognify-BreaK 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 "Allenda/Qwen2.5-7B-Instruct-cognify-BreaK" \ --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": "Allenda/Qwen2.5-7B-Instruct-cognify-BreaK", "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 "Allenda/Qwen2.5-7B-Instruct-cognify-BreaK" \ --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": "Allenda/Qwen2.5-7B-Instruct-cognify-BreaK", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Allenda/Qwen2.5-7B-Instruct-cognify-BreaK with Docker Model Runner:
docker model run hf.co/Allenda/Qwen2.5-7B-Instruct-cognify-BreaK
Allenda/Qwen2.5-7B-Instruct-cognify-BreaK
Qwen/Qwen2.5-7B-Instruct fine-tuned for CognifyChallenge 2026 (team BreaK): given a synthetic
patient record, a dementia classifier's verdict and its top-5 SHAP attributions,
write a short clinical note explaining the model's reasoning, plus a structured
list of the drivers the note relies on.
What it was trained on
650 examples: 450 cases from the practice split of the competition's
own cohort (cohort_dev.json), plus 200 sparsified copies of those same cases.
The targets are not human-written. They are generated by a deterministic program that assembles the note the competition's scoring function implies โ verdict and confidence band in the question bank's own wording, the five attributions in rank order with the direction each one gives, values quoted only where the record can support them, and the findings the record reports as present so that what it reports as absent follows by elimination. So this is distillation of a program into a model, not a model taught medicine.
The sparsified copies exist because the two splits differ in the tail: the practice split's records carry at least 21 citable fields and the held-out split reaches down to 7. Trained on the practice range alone, the model wrote a correct note on 59 of 60 manufactured sparse cases and fell into a degenerate repetition loop on the sixtieth. With the sparse copies in the training set it is 60 of 60.
The held-out slice of 150 cases was excluded from training, and the sparsified copies were built only from training cases. The competition's test split was never read.
Intended use
This model is for one task, in one prompt format, on synthetic data. It does not diagnose, and it should not be pointed at real patient records. Every patient in its training data is synthetic.
Prompt format
It expects the rendering the competition harness produces โ the patient record,
the model output, the top 5 attributions and the citable-fields JSON, followed by
a short instruction โ and replies with a single JSON object holding note and
drivers. Outside that format it will still answer, but nothing about its
behaviour there was measured.
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