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="bruhzair/Command-t2-111b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bruhzair/Command-t2-111b")
model = AutoModelForCausalLM.from_pretrained("bruhzair/Command-t2-111b")
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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Test1

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

Merge Details

Merge Method

This model was merged using the NuSLERP merge method.

Models Merged

The following models were included in the merge:

  • /workspace/cache/models--TheDrummer--Fallen-Command-A-111B-v1/snapshots/5d2b4bdb35d7dff3a4eb51a5f2b231ba27943491
  • /workspace/cache/models--CohereForAI--c4ai-command-a-03-2025/snapshots/6894b671d755c72573bb1a5722cfcfcd86b42b01

Configuration

The following YAML configuration was used to produce this model:

dtype: bfloat16
merge_method: nuslerp
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 64]
        model: /workspace/cache/models--CohereForAI--c4ai-command-a-03-2025/snapshots/6894b671d755c72573bb1a5722cfcfcd86b42b01
        parameters:
          weight: [0.9, 0.7, 0.5, 0.35, 0.5, 0.7, 0.9]
      - layer_range: [0, 64]
        model: /workspace/cache/models--TheDrummer--Fallen-Command-A-111B-v1/snapshots/5d2b4bdb35d7dff3a4eb51a5f2b231ba27943491
        parameters:
          weight: [0.1, 0.3, 0.5, 0.65, 0.5, 0.3, 0.1]
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