How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ABDUL-HASEEB-TANOLI/HAIDER-Math-32B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ABDUL-HASEEB-TANOLI/HAIDER-Math-32B-v1")
model = AutoModelForCausalLM.from_pretrained("ABDUL-HASEEB-TANOLI/HAIDER-Math-32B-v1", 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]:]))
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haider-math-32b-lam05

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Task Arithmetic merge method using /home/azureuser/haider_project/models/qwen-32b as a base.

Models Merged

The following models were included in the merge:

  • /home/azureuser/haider_project/models/qwq-32b
  • /home/azureuser/haider_project/models/deepseek-r1-32b

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: /home/azureuser/haider_project/models/qwq-32b
  - model: /home/azureuser/haider_project/models/deepseek-r1-32b

base_model: /home/azureuser/haider_project/models/qwen-32b

merge_method: task_arithmetic
parameters:
  weight: 0.5
  normalize: true

dtype: bfloat16

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