Text Generation
Transformers
Safetensors
gemma
mergewss]
mergekit
lazymergekit
deepnetguy/gemma-64
Aspik101/minigemma_ft9
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Sumail/Alchemist_02_2b")
model = AutoModelForCausalLM.from_pretrained("Sumail/Alchemist_02_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]:]))Quick Links
Alchemist_02_2b
Alchemist_02_2b is a merge of the following models using mergekit:
🧩 Configuration
models:
- model: deepnet/SN6-71G5
# no parameters necessary for base model
- model: deepnetguy/gemma-64
parameters:
density: 0.5
weight: 0.3
- model: Aspik101/minigemma_ft9
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: deepnet/SN6-71G5
parameters:
normalize: true
dtype: bfloat16
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sumail/Alchemist_02_2b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)