初始化项目，由ModelHub XC社区提供模型

Model: Sourav0511/loan-underwriting-merged-v2
Source: Original Platform

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README.md

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+---
+base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded finetuned  model
+
+- **Developed by:** Sourav0511
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

chat_template.jinja

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+{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
+
+'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
+
+' }}{% endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "float16",
+    "eos_token_id": 128009,
+    "head_dim": 128,
+    "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,
+    "pad_token_id": 128255,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "rope_theta": 500000.0,
+        "rope_type": "default"
+    },
+    "tie_word_embeddings": false,
+    "unsloth_version": "2026.4.8",
+    "use_cache": true,
+    "vocab_size": 128256
+}

handler.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            path,
+            use_fast=True,
+            tokenizer_class=None
+        )
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        self.pipe = pipeline(
+            "text-generation",
+            model=self.model,
+            tokenizer=self.tokenizer
+        )
+
+    def __call__(self, data):
+        inputs = data.pop("inputs", data)
+        result = self.pipe(inputs, max_new_tokens=512)
+        return result

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