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="Sumail/Alchemist_03_2b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sumail/Alchemist_03_2b")
model = AutoModelForCausalLM.from_pretrained("Sumail/Alchemist_03_2b")
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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Alchemist_03_2b

Alchemist_03_2b is a merge of the following models using mergekit:

🧩 Configuration

models:
  - model: Aspik101/minigemma_ft9
    # No parameters necessary for base model
  - model: zzttbrdd/sn6_20_new
    parameters:
      density: 0.53
      weight: 0.34
  - model: deepnetguy/gemma-64
    parameters:
      density: 0.53
      weight: 0.47
  - model: rwh/gemma1
    parameters:
      density: 0.53
      weight: 0.15
merge_method: dare_ties
base_model: deepnet/SN6-71G5
parameters:
  int8_mask: true
dtype: bfloat16
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