初始化项目，由ModelHub XC社区提供模型

Model: Reinsured-AI/Reinsure-8B
Source: Original Platform

.gitattributes

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README.md

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+---
+language: en
+tags:
+- safetensors
+- vllm
+- insurance
+- reinsurance
+- sovereign-ai
+- llama-3
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+<div align="center">
+  <h1>Reinsure-8B</h1>
+  <h3>The world's first insurance-native language model</h3>
+</div>
+
+<p align="center">
+  <a href="https://www.reinsured.ai/reinsure-8b">View Website</a> | 
+  <a href="https://www.reinsured.ai/contact">Request API Access</a> | 
+  <a href="https://www.reinsured.ai/platform">Platform Architecture</a>
+</p>
+
+---
+
+## The world's first insurance-native language model
+
+**Reinsure-8B** is a small language model built exclusively for the reinsurance and insurance industry. Fine-tuned from Llama 3.1 on reinsurance workflows, treaty structures, bordereaux formats, and London Market language.
+
+Deploy it sovereign inside your own infrastructure, or call it as an inference API. Either way, you get a model that speaks insurance — without prompt engineering, without hallucinated policy terms.
+
+### What is Organisational Sovereign AI?
+Sovereign AI means the model runs inside your control. Your weights, your infrastructure, your data. No dependency on a third-party API that can change pricing, deprecate versions, or inspect your query traffic.
+
+For regulated insurance businesses — Lloyd's syndicates, global reinsurers, captives, MGAs — sovereignty is not a preference. It is a compliance requirement. Sensitive submissions, treaty terms, and client data cannot flow to shared cloud endpoints.
+
+Reinsure-8B is purpose-sized for sovereign deployment. At 8 billion parameters, it runs efficiently on enterprise GPU hardware — a single A100, L4, or equivalent — without the infrastructure overhead of 70B+ models. This is the practical path to production AI in a regulated industry.
+
+### Two ways to run Reinsure-8B
+1. **Sovereign Deployment:** Deploy the full model weights inside your own cloud or on-prem environment. Fine-tune it on your proprietary data. Complete data sovereignty and maximum performance.
+2. **Hosted API:** Call Reinsure-8B as a hosted API via Reinsured.AI. No infrastructure required. Pay per inference token. Ideal for teams validating use cases or building lightweight integrations.
+
+### Built for the language of reinsurance
+Generic large language models are trained on the open internet — predominantly consumer content, code, and general text. Insurance knowledge is sparse, often incorrect, and never updated with current market practice.
+
+Reinsure-8B was fine-tuned on a curated corpus of reinsurance-specific content — treaty wordings, bordereaux templates, Lloyd's market standards, catastrophe model outputs, underwriting guidelines, and claims documentation.
+
+The result is a model that interprets reinsurance language correctly by default, without requiring you to explain what a "binder", "burning cost", or "cedant" means in every prompt.
+
+### Domain-specific beats general-purpose
+Applying a general-purpose model to insurance creates compounding problems — hallucination, data risk, poor economics. Reinsure-8B is purpose-built to eliminate each of them.
+
+### The intelligence engine behind the stack
+Reinsure-8B is the reasoning core that powers Reinsured.AI's Context Cloud and AI Agents. When an agent interprets a treaty clause, extracts a bordereaux field, or classifies a submission, it is calling on Reinsure-8B — a model that already understands the domain.
+
+Organisations that deploy Reinsure-8B sovereign get the additional option to fine-tune it on their own internal data, creating a model layer unique to their underwriting philosophy and market positioning — one that becomes a proprietary asset over time.
+
+---
+
+## Technical Specifications & Deployment
+
+This model has been exported to standard **16-bit Safetensors** format. It is fully compatible with industry-standard cloud inference engines, including **vLLM** and HuggingFace Text Generation Inference (TGI).
+
+### Cloud Native Example (vLLM / Modal)
+For organizations deploying Sovereign AI at scale with scale-to-zero economics, Reinsure-8B is optimized for `vLLM`. Here is a reference architecture deploying to [Modal Serverless](https://modal.com):
+
+```python
+import modal
+
+vllm_image = modal.Image.debian_slim().pip_install("vllm")
+app = modal.App("reinsure-8b-sovereign")
+
+@app.function(
+    image=vllm_image, 
+    gpu="L4", # Extremely cost-effective for 8B models
+    container_idle_timeout=300 # Scale to zero when inactive
+)
+@modal.web_server(8000)
+def serve():
+    import sys, subprocess
+    subprocess.Popen([
+        sys.executable, "-m", "vllm.entrypoints.openai.api_server",
+        "--model", "Reinsured-AI/Reinsure-8B",
+        "--port", "8000",
+        "--max-model-len", "8192"
+    ])
+```
+
+[Book a Demo](https://www.reinsured.ai/demo) | [Contact the Team](https://www.reinsured.ai/contact)

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+    "architectures": [
+        "LlamaForCausalLM"
+    ],
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+        "rope_type": "llama3"
+    },
+    "rope_theta": 500000.0,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.42.3",
+    "use_cache": true,
+    "vocab_size": 128256
+}

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