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ho22joshua
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Model Details
Model Provider
ho22joshua
Model Tree
Base
Qwen/Qwen2.5-7B-Instruct
Adapter
this model
Input Modalities
Text
Output Modalities
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Supported Functionality
Dedicated Endpoints
Container
Qwen/Qwen2.5-7B-Instructqwen2_5_7b_lora16_sigbg_irred_red_v1, step 2200ho22joshua/hep-posttrainingho22joshua/hep-signature-backgroundsThe model is intended for research workflows that ask for concise HEP reasoning about final-state signatures, dominant backgrounds, irreducible backgrounds, and reducible backgrounds.
python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipelinefrom peft import PeftModelbase_model_id = "Qwen/Qwen2.5-7B-Instruct"adapter_id = "ho22joshua/hep-qwen2.5-7b-lora16-sigbg-irred-red-step2200"tokenizer = AutoTokenizer.from_pretrained(adapter_id)model = AutoModelForCausalLM.from_pretrained(base_model_id,device_map="auto",torch_dtype="auto",)model = PeftModel.from_pretrained(model, adapter_id)pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)messages = [{"role": "user", "content": "For a search targeting H to AA to photons, describe the signal signature and likely Standard Model backgrounds."}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)print(pipe(prompt, max_new_tokens=300, do_sample=False)[0]["generated_text"])
For endpoint serving, either load the adapter with a serving runtime that supports LoRA, or merge the adapter into the base model first.
bash
python -m vllm.entrypoints.openai.api_server \--model Qwen/Qwen2.5-7B-Instruct \--enable-lora \--lora-modules hep=ho22joshua/hep-qwen2.5-7b-lora16-sigbg-irred-red-step2200 \--host 0.0.0.0 \--port 8000 \--dtype bfloat16
bash
curl http://localhost:8000/v1/chat/completions \-H "Content-Type: application/json" \-d '{"model": "hep","messages": [{"role": "user", "content": "For H to AA to photons, what backgrounds should be considered?"}],"max_tokens": 300}'
This model is a research artifact. It can make factual or physics mistakes and should not be used as a substitute for expert review, validated simulation, internal collaboration guidance, or publication-quality analysis approval.
The model license follows the constraints of the base model and the generated training data. Please verify those terms before downstream redistribution or commercial use.