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Model: Laxmikant17/Llama-3-8B-Hernia-Analyst-600-Patients-8k Source: Original Platform
2026-06-20 10:27:35 +08:00
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--- a/README.md
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+---
+license: apache-2.0
+language: en
+library_name: transformers
+tags:
+- text-generation
+- json
+- qol
+- hernia
+- healthcare
+- llama-3
+- fine-tuned
+- 8k-context
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+---
+
+# Llama-3-8B-Hernia-Analyst-600-Patients-8k
+
+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).
+
+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.
+
+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*.
+
+## Model Description
+
+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:
+- Body Image
+- Mental Health
+- Symptoms and Function
+- Interpersonal Relationships
+- Employment
+
+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.
+
+## Intended Use
+
+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.
+
+**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.
+
+## How to Use
+
+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.
+
+```python
+# This installs specific, stable versions of the libraries known to work well together
+# in the Colab environment.
+
+!pip uninstall -y sentence-transformers
+!pip install torch==2.3.1+cu121 torchvision==0.18.1+cu121 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121
+!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"
+!pip install -q einops scipy sentencepiece tensorboard
+
+# # After installation, we need to restart the runtime one time for the changes to take effect.
+# # This is a standard procedure in Colab.
+import os
+os.kill(os.getpid(), 9)
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import json
+
+# --- 1. CONFIGURATION ---
+# The unique ID of your powerful, 8k-context model on the Hugging Face Hub
+model_name = "Laxmikant17/Llama-3-8B-Hernia-Analyst-600-Patients-8k"
+
+# --- 2. LOAD MODEL AND TOKENIZER ---
+print(f"Loading fine-tuned model: {model_name}")
+
+# Use 4-bit quantization for efficient inference on consumer GPUs (like in Colab)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+# Load the model from the Hub with quantization
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto" # Automatically use the GPU if available
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model.eval() # Set the model to evaluation mode
+
+print("✅ Model loaded successfully!")
+
+# --- 3. PREPARE YOUR INPUT ---
+# This can be a very long and detailed patient narrative
+test_narrative = """
+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.
+"""
+
+# Format the input using the exact Llama 3 Instruct template the model was trained on
+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."
+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"
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# --- 4. GENERATE ANALYSIS ---
+print("\n🚀 Generating analysis...")
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=4096, # Give the model plenty of space for its JSON output
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+# Robustly extract and print the JSON from the model's response
+decoded_output = tokenizer.decode(outputs, skip_special_tokens=True)
+try:
+    assistant_marker = 'assistant\n\n'
+    assistant_response_start = decoded_output.find(assistant_marker)
+    response_part = decoded_output[assistant_response_start + len(assistant_marker):].strip()
+    json_start = response_part.find('{')
+    json_end = response_part.rfind('}') + 1
+    json_string = response_part[json_start:json_end]
+
+    print("\n--- ✅ MODEL-GENERATED ANALYSIS ---")
+    parsed_json = json.loads(json_string)
+    print(json.dumps(parsed_json, indent=2))
+except Exception as e:
+    print(f"\n--- 🚨 ERROR: Could not parse the model's response. ---")
+    print(f"Error: {e}")
+    print("\nFull raw output for debugging:")
+    print(decoded_output)
+```
--- a/config.json
+++ b/config.json
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+{
+  "_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}
--- a/generation_config.json
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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128009
+  ],
+  "max_length": 4096,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.43.2"
+}
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--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,16 @@
+{
+  "bos_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json