Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- ecastera/wiki_fisica
+- ecastera/filosofia-es
+- somosnlp/somos-clean-alpaca-es
+language:
+- es
+- en
+tags:
+- mistral
+- ehartford/dolphin
+---
+
+# eva-mistral-dolphin-7b-spanish
+
+Mistral 7b based model fine tuned in Spanish to add high quality Spanish text generation.
+
+* Base model Mistral
+* Based on the excelent job of Eric Hartfod's dolphin models cognitivecomputations/dolphin-2.1-mistral-7b
+* Fine-tuned in Spanish with a collection of poetry, books, wikipedia articles, phylosophy texts and alpaca-es datasets.
+* Trained using Lora and PEFT on 2 GPUs for several days. 
+
+
+## Usage:
+
+Strongly advice to run inference in INT8 or INT4 mode, with the help of BitsandBytes library.
+
+
+```
+import torch
+from transformers import AutoTokenizer, pipeline, AutoModel, AutoModelForCausalLM, BitsAndBytesConfig
+
+MODEL = "ecastera/eva-mistral-dolphin-7b-spanish"
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    load_in_8bit=False,
+    llm_int8_threshold=6.0,
+    llm_int8_has_fp16_weight=False,
+    bnb_4bit_compute_dtype="float16",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4")
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL,
+    load_in_8bit=True,
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float16,
+    quantization_config=quantization_config,
+    offload_state_dict=True,
+    offload_folder="./offload",
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+print(f"Loading complete {model} {tokenizer}")
+
+prompt = "Soy Eva una inteligencia artificial y pienso que la esperanza "
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, do_sample=True, temperature=0.4, top_p=1.0, top_k=50,
+                             no_repeat_ngram_size=3, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
+text_out = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+print(text_out)
+```
+