WaveCut commited on
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bcf925b
1 Parent(s): 62d70fa

cc439d3b01e058fd3d6478f44ebdc5e83a86552b380216e8d3a853a94fa560ac

Browse files
README.md ADDED
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+ ---
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+ license: mit
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+ language:
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+ - multilingual
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+ tags:
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+ - nlp
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+ - mlx
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+ base_model: numind/NuExtract-1.5
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+ pipeline_tag: text-generation
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+ inference: true
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+ ---
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+
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+ # mlx-community/numind-NuExtract-1.5-MLX-8bit
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+
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+ The Model [mlx-community/numind-NuExtract-1.5-MLX-8bit](https://huggingface.co/mlx-community/numind-NuExtract-1.5-MLX-8bit) was
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+ converted to MLX format from [numind/NuExtract-1.5](https://huggingface.co/numind/NuExtract-1.5)
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+ using mlx-lm version **0.20.1**.
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+
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+ ## Use with mlx
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+
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+ ```bash
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+ pip install mlx-lm
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+ ```
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+
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+ ```python
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+ from mlx_lm import load, generate
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+
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+ model, tokenizer = load("mlx-community/numind-NuExtract-1.5-MLX-8bit")
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+
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+ prompt="hello"
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+
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+ if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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+ messages = [{"role": "user", "content": prompt}]
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+ prompt = tokenizer.apply_chat_template(
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+ messages, tokenize=False, add_generation_prompt=True
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+ )
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+
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+ response = generate(model, tokenizer, prompt=prompt, verbose=True)
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+ ```
config.json ADDED
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+ {
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+ "architectures": [
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+ "Phi3ForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "auto_map": {
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+ "AutoConfig": "microsoft/Phi-3.5-mini-instruct--configuration_phi3.Phi3Config",
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+ "AutoModelForCausalLM": "microsoft/Phi-3.5-mini-instruct--modeling_phi3.Phi3ForCausalLM"
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+ },
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+ "bos_token_id": 1,
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+ "embd_pdrop": 0.0,
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+ "eos_token_id": 32000,
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+ "hidden_act": "silu",
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+ "hidden_size": 3072,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 8192,
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+ "max_position_embeddings": 131072,
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+ "model_type": "phi3",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "original_max_position_embeddings": 4096,
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+ "pad_token_id": 32000,
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+ "quantization": {
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+ "group_size": 64,
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+ "bits": 8
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+ "quantization_config": {
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+ "group_size": 64,
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+ "bits": 8
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+ "resid_pdrop": 0.0,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": {
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+ ],
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+ "type": "longrope"
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+ },
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+ "rope_theta": 10000.0,
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+ "sliding_window": 262144,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.44.0",
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+ "use_cache": true,
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+ "vocab_size": 32064
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+ }
handler.py ADDED
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+ from typing import Dict, List, Any
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+ import os
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+
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+
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+ MAX_INPUT_SIZE = 10_000
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+ MAX_NEW_TOKENS = 4_000
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+
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+ def clean_json_text(text):
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+ """
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+ Cleans JSON text by removing leading/trailing whitespace and escaping special characters.
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+ """
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+ text = text.strip()
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+ text = text.replace("\#", "#").replace("\&", "&")
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+ return text
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+
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+ class EndpointHandler:
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+ def __init__(self, path=""):
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+ # load model and processor from path
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+ self.model = AutoModelForCausalLM.from_pretrained(path,
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+ trust_remote_code=True,
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto")
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+ self.model.eval()
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+ self.tokenizer = AutoTokenizer.from_pretrained(path)
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+
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+ def __call__(self, data: Dict[str, Any]) -> str:
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+ data = data.pop("inputs")
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+ template = data.pop("template")
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+ text = data.pop("text")
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+ input_llm = f"<|input|>\n### Template:\n{template}\n### Text:\n{text}\n\n<|output|>" + "{"
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+
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+ input_ids = self.tokenizer(input_llm, return_tensors="pt", truncation=True, max_length=MAX_INPUT_SIZE).to("cuda")
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+ output = self.tokenizer.decode(self.model.generate(**input_ids, max_new_tokens=MAX_NEW_TOKENS)[0], skip_special_tokens=True)
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+
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+ return clean_json_text(output.split("<|output|>")[1])
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