Instructions to use build-small-hackathon/robe-iniesta-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use build-small-hackathon/robe-iniesta-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "build-small-hackathon/robe-iniesta-lora") - Notebooks
- Google Colab
- Kaggle
robe-iniesta-lora
LoRA adapter for Qwen/Qwen2.5-7B-Instruct, fine-tuned to mimic the conversational style of Robe Iniesta (Extremoduro).
Training pipeline: SFT on public interview transcripts → DPO on preference pairs.
⚠️ Fan project / style simulation. Not Robe Iniesta. May hallucinate biographical facts.
Usage
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
| SFT | DPO | |
|---|---|---|
| Base | Qwen2.5-7B-Instruct | SFT adapter |
| Method | LoRA (r=32, α=64) | DPO (β=0.1) |
| Data | ~550 ChatML examples | Preference pairs |
| Hardware | Modal A100-40GB | Modal 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
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