Dedicated Endpoints

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README

License: apache-2.0

Usage

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
base = "Qwen/Qwen2.5-7B-Instruct"
adapter = "build-small-hackathon/robe-iniesta-lora"
bnb = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
base,
quantization_config=bnb,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="sdpa",
)
model = PeftModel.from_pretrained(model, adapter)
model.eval()

Training details

SFTDPO
BaseQwen2.5-7B-InstructSFT adapter
MethodLoRA (r=32, α=64)DPO (β=0.1)
Data~550 ChatML examplesPreference pairs
HardwareModal A100-40GBModal A100-40GB

Demo

Try it at: kabesaml/robe-chat

Limitations

  • Simulates speaking style, not factual knowledge about Robe Iniesta
  • May invent dates, album names, collaborators, or anecdotes
  • Not affiliated with or endorsed by the artist

Model provider

build-small-hackathon

Model tree

Base

Qwen/Qwen2.5-7B-Instruct

Adapter

this model

Modalities

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Output

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Pricing

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Dedicated Endpoints

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