初始化项目,由ModelHub XC社区提供模型
Model: Laxmikant17/Llama-3-8B-Hernia-Analyst-600-Patients-8k Source: Original Platform
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README.md
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---
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license: apache-2.0
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language: en
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library_name: transformers
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tags:
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- text-generation
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- json
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- qol
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- hernia
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- healthcare
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- llama-3
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- fine-tuned
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- 8k-context
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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---
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# Llama-3-8B-Hernia-Analyst-600-Patients-8k
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This is a specialized, fine-tuned version of `meta-llama/Meta-Llama-3-8B-Instruct`, designed to function as an expert "AI Research Assistant" for analyzing patient narratives related to Abdominal Wall Hernia (AWH).
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This model represents a significant upgrade over previous versions, as it was fine-tuned on a larger dataset of **600 synthetic patients** and trained using the **full 8192 token context window**. This enables it to analyze longer, more complex patient narratives without truncation, resulting in a more accurate and comprehensive analysis.
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The model's primary function is to take unstructured, free-text patient stories as input and transform them into a structured, multi-level JSON output. This output adheres to a specific Quality of Life (QoL) framework derived from clinical research, notably the work published in *Hernia (2022) 26:795–808*.
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## Model Description
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The core objective of this model is to automate and standardize the process of qualitative analysis for patient-reported outcomes. It has been trained to identify and structure information across five key domains:
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- Body Image
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- Mental Health
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- Symptoms and Function
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- Interpersonal Relationships
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- Employment
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The model produces a detailed JSON object that includes an executive summary, a ranked list of the most prominent QoL domains, and a deep-dive analysis for each domain, identifying relevant subthemes and clinical concepts mentioned by the patient.
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## Intended Use
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This model is intended for **research and prototyping purposes only**. Its primary use case is to process long-form patient narratives (e.g., from detailed interview transcripts or comprehensive questionnaires) and generate a structured, machine-readable analysis. This can be used for large-scale research, data visualization, or to assist clinicians in rapidly understanding the key QoL issues for a patient.
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**Disclaimer: This is not a medical device.** The output should not be used for clinical diagnosis, treatment decisions, or any direct patient care without verification and interpretation by a qualified healthcare professional.
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## How to Use
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The model expects prompts formatted in the Llama 3 Instruct template. The following Python code demonstrates how to load the model and run inference on a new patient narrative, making it a powerful tool for offline analysis.
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```python
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# This installs specific, stable versions of the libraries known to work well together
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# in the Colab environment.
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!pip uninstall -y sentence-transformers
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!pip install torch==2.3.1+cu121 torchvision==0.18.1+cu121 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
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!pip install -q "transformers==4.43.2" "datasets==2.18.0" "accelerate==0.29.3" "peft==0.10.0" "bitsandbytes==0.43.1" "trl==0.8.6" "protobuf==3.20.3"
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!pip install -q einops scipy sentencepiece tensorboard
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# # After installation, we need to restart the runtime one time for the changes to take effect.
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# # This is a standard procedure in Colab.
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import os
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os.kill(os.getpid(), 9)
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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import json
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# --- 1. CONFIGURATION ---
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# The unique ID of your powerful, 8k-context model on the Hugging Face Hub
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model_name = "Laxmikant17/Llama-3-8B-Hernia-Analyst-600-Patients-8k"
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# --- 2. LOAD MODEL AND TOKENIZER ---
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print(f"Loading fine-tuned model: {model_name}")
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# Use 4-bit quantization for efficient inference on consumer GPUs (like in Colab)
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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# Load the model from the Hub with quantization
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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quantization_config=bnb_config,
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device_map="auto" # Automatically use the GPU if available
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model.eval() # Set the model to evaluation mode
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print("✅ Model loaded successfully!")
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# --- 3. PREPARE YOUR INPUT ---
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# This can be a very long and detailed patient narrative
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test_narrative = """
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The pain is the worst part. It's a constant, burning sensation that gets worse when I stand for more than ten minutes. I can't even lift my grocery bags without feeling a sharp pull. I also feel deformed. I avoid looking at myself without a shirt on. I just want to feel normal again. It's been really tough mentally. I feel a sense of dread every morning when I wake up, just knowing the discomfort is waiting for me. I've become irritable and I'm not pleasant to be around, which is straining my relationship with my family.
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"""
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# Format the input using the exact Llama 3 Instruct template the model was trained on
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instruction = "Your sole function is to be a structured data generator. Analyze the patient narrative and produce a single, valid JSON object as your only output. Adhere strictly to the required format and terminology from the provided knowledge base."
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prompt = f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{instruction}\n\n**Patient Narrative:**\n{test_narrative}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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# --- 4. GENERATE ANALYSIS ---
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print("\n🚀 Generating analysis...")
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=4096, # Give the model plenty of space for its JSON output
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do_sample=False,
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pad_token_id=tokenizer.eos_token_id
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)
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# Robustly extract and print the JSON from the model's response
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decoded_output = tokenizer.decode(outputs, skip_special_tokens=True)
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try:
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assistant_marker = 'assistant\n\n'
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assistant_response_start = decoded_output.find(assistant_marker)
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response_part = decoded_output[assistant_response_start + len(assistant_marker):].strip()
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json_start = response_part.find('{')
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json_end = response_part.rfind('}') + 1
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json_string = response_part[json_start:json_end]
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print("\n--- ✅ MODEL-GENERATED ANALYSIS ---")
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parsed_json = json.loads(json_string)
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print(json.dumps(parsed_json, indent=2))
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except Exception as e:
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print(f"\n--- 🚨 ERROR: Could not parse the model's response. ---")
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print(f"Error: {e}")
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print("\nFull raw output for debugging:")
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print(decoded_output)
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```
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config.json
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{
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"_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128009,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.43.2",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128009
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],
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"max_length": 4096,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.43.2"
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}
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||||||
|
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|
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|
||||||
|
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|
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|
||||||
|
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|
||||||
|
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|
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|
||||||
|
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|
||||||
|
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||||||
|
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|begin_of_text|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|eot_id|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user