metadata
license: mit
language:
- en
widget:
- text: >-
<|input|> ### Template: { "Model": { "Name": "", "Number of parameters":
"", "Number of max token": "", "Architecture": [] }, "Usage": { "Use
case": [], "Licence": "" } } ### Text: We introduce Mistral 7B, a
7–billion-parameter language model engineered for superior performance and
efficiency. Mistral 7B outperforms the best open 13B model (Llama 2)
across all evaluated benchmarks, and the best released 34B model (Llama 1)
in reasoning, mathematics, and code generation. Our model leverages
grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length
with a reduced inference cost. We also provide a model fine-tuned to
follow instructions, Mistral 7B – Instruct, that surpasses Llama 2 13B –
chat model both on human and automated benchmarks. Our models are released
under the Apache 2.0 license. Code
https://github.com/mistralai/mistral-src Webpage
https://mistral.ai/news/announcing-mistral-7b/ <|output|>
Structure Extraction Model by NuMind 🔥
NuExtract_tiny is a fine-tuned version of qwen1.5-0.5 (500 milions parameters), on a private high-quality syntactic dataset for information extraction. To use the model, provide an input text (less than 2000 tokens) and a JSON schema describing the information you need to extract. This model is purely extractive, so each information output by the model is present as it is in the text. You can also provide an example of output to help the model understand your task more precisely.
Note: This model while providing good performance in 0 shot, is intendeed to be fine-tune on a specific task (at least 30 example needed)
Checkout other models by NuMind:
- SOTA Zero-shot NER Model NuNER Zero
- SOTA Multilingual Entity Recognition Foundation Model: link
- SOTA Sentiment Analysis Foundation Model: English, Multilingual
Usage
To use the model:
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def predict_NuExtract(model,tokenizer,text, schema,example = ["","",""]):
schema = json.dumps(json.loads(schema), indent=4)
input_llm = "<|input|>\n### Template:\n" + schema + "\n"
for i in example:
if i != "":
input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\n"
input_llm += "### Text:\n"+text +"\n<|output|>\n"
input_ids = tokenizer(input_llm, return_tensors="pt",truncation = True, max_length = 4000).to("cuda")
output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
return output.split("<|output|>")[1].split("<|end-output|>")[0]
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract-tiny", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract-tiny", trust_remote_code=True)
model.to("cuda")
model.eval()
text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
prediction = predict_NuExtract(model,tokenizer,text, schema,example = ["","",""])
print(prediction)