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---
language:
- en
- es
---
# Model Card for Carpincho-30b
<!-- Provide a quick summary of what the model is/does. -->
This is Carpincho-30B qlora 4-bit checkpoint, an Instruction-tuned LLM based on LLama-30B. It is trained to answer in colloquial spanish Argentine language.
It was trained on 2x3090 (48G) for 120 hs using huggingface QLoRA code (4-bit quantization)
## Model Details
The model is provided in LoRA format.
## Usage
Here is example inference code, you will need to install the following requirements:
```
bitsandbytes==0.39.0
transformers @ git+https://github.com/huggingface/transformers.git
peft @ git+https://github.com/huggingface/peft.git
accelerate @ git+https://github.com/huggingface/accelerate.git
einops==0.6.1
evaluate==0.4.0
scikit-learn==1.2.2
sentencepiece==0.1.99
wandb==0.15.3
```
```
import time
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
model_name = "models/huggyllama_llama-30b/"
adapters_name = 'carpincho-30b-qlora'
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="sequential"
)
print(f"Loading {adapters_name} into memory")
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = LlamaTokenizer.from_pretrained(model_name)
tokenizer.bos_token_id = 1
stop_token_ids = [0]
print(f"Successfully loaded the model {model_name} into memory")
def main(tokenizer):
prompt = '''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
%s
### Response:
''' % "Hola, como estas?"
batch = tokenizer(prompt, return_tensors="pt")
batch = {k: v.cuda() for k, v in batch.items()}
with torch.no_grad():
generated = model.generate(inputs=batch["input_ids"],
do_sample=True, use_cache=True,
repetition_penalty=1.1,
max_new_tokens=100,
temperature=0.9,
top_p=0.95,
top_k=40,
return_dict_in_generate=True,
output_attentions=False,
output_hidden_states=False,
output_scores=False)
result_text = tokenizer.decode(generated['sequences'].cpu().tolist()[0])
print(result_text)
main(tokenizer)
```
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Alfredo Ortega (@ortegaalfredo)
- **Model type:** 30B LLM QLoRA
- **Language(s):** (NLP): English and colloquial Argentine Spanish
- **License:** Free for non-commercial use, but I'm not the police.
- **Finetuned from model:** https://huggingface.co/huggyllama/llama-30b
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://huggingface.co/huggyllama/llama-30b
- **Paper [optional]:** https://arxiv.org/abs/2302.13971
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This is a generic LLM chatbot that can be used to interact directly with humans.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This bot is uncensored and may provide shocking answers. Also it contains bias present in the training material.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## Model Card Contact
Contact the creator at @ortegaalfredo on twitter/github |