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="Wonderlab-Testing-Grounds/Interferon-lambda-RP-9B-Preview-2608")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wonderlab-Testing-Grounds/Interferon-lambda-RP-9B-Preview-2608")
model = AutoModelForCausalLM.from_pretrained("Wonderlab-Testing-Grounds/Interferon-lambda-RP-9B-Preview-2608", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
Quick Links

One of the many testing version for my upcoming Nyx-RP-9B-Instruct-2608-v2, this one had its stability improved after I carefully analyzed the first versions.

Training details:

Method: LoRA

  • Rank: 226
  • Epochs: 2(decreased from previous 3, which did seem to be the breaking point)

Hardware used for training(I have patience):

  • RTX 5070 Ti
  • R7800X3D + 32gb DDR5

Dataset detail:

Its a fairly new dataset which I gathered from the best RP logs I had on my hands, it included a lot of explicit long turn conversations so keep in mind the reduced refusals.

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