Create README.md

README.md

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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 requirements for https://github.com/johnsmith0031/alpaca_lora_4bit
+
+```
+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