commit f599125cc7d20149089385fcb71bfd85dcce3e0a
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Sun Apr 12 17:05:59 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: seasparks/Llama-3.1-8B-Instruct-LegalCite
    Source: Original Platform

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diff --git a/LICENSE b/LICENSE
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+LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
+Llama 3.1 Version Release Date: July 23, 2024
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the
+Llama Materials set forth herein.
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\ No newline at end of file
diff --git a/README.md b/README.md
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+---
+license: llama3.1
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+tags:
+  - llama
+  - llama-3.1
+  - legal
+  - fine-tuned
+  - transformers
+  - citation
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Llama-3.1-8B-Instruct-LegalCite
+
+**Built with LLaMA** - Fine-tuned for legal citation extraction from documents like EU regulations.
+
+This model specializes in quoting schort legal sections in response to user questions — directly grounded in the input text. It was fine-tuned on legislative corpora to enable compliance-minded, transparent responses.
+
+**Technical Notes**
+
+Finetuning was performed using a 4-bit MLX version of the original model, enabling efficient experimentation on Apple Silicon hardware.
+
+On the evaluation set, we observed a significant reduction in loss and perplexity:
+- Test loss: ~2.4 → ~1.1
+- Test perplexity: ~11.1 → ~2.9
+
+For broad compatibility, the model was then converted back to float16 and exported in standard Hugging Face Transformers format.
+
+_This is a Transformers-compatible model. MLX and GGUF versions will be published soon._
+
+---
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+model = AutoModelForCausalLM.from_pretrained("SeaSparks/Llama-3.1-8B-Instruct-LegalCite", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("SeaSparks/Llama-3.1-8B-Instruct-LegalCite")
+
+sys_message = {"role": "system", "content": "You are an expert assistant answering questions about texts only by accurately citing and providing direct quotes from the text."}
+
+example_text = """(3)
+AI systems can be easily deployed in a large variety of sectors of the economy and many parts of society, including
+across borders, and can easily circulate throughout the Union. Certain Member States have already explored the
+adoption of national rules to ensure that AI is trustworthy and safe and is developed and used in accordance with
+fundamental rights obligations. Diverging national rules may lead to the fragmentation of the internal market and
+may decrease legal certainty for operators that develop, import or use AI systems. A consistent and high level of
+protection throughout the Union should therefore be ensured in order to achieve trustworthy AI, while divergences
+hampering the free circulation, innovation, deployment and the uptake of AI systems and related products and
+services within the internal market should be prevented by laying down uniform obligations for operators and
+guaranteeing the uniform protection of overriding reasons of public interest and of rights of persons throughout the
+internal market on the basis of Article 114 of the Treaty on the Functioning of the European Union (TFEU). To the
+extent that this Regulation contains specific rules on the protection of individuals with regard to the processing of
+personal data concerning restrictions of the use of AI systems for remote biometric identification for the purpose of
+law enforcement, of the use of AI systems for risk assessments of natural persons for the purpose of law
+enforcement and of the use of AI systems of biometric categorisation for the purpose of law enforcement, it is
+appropriate to base this Regulation, in so far as those specific rules are concerned, on Article 16 TFEU. In light of
+those specific rules and the recourse to Article 16 TFEU, it is appropriate to consult the European Data Protection
+Board."""
+
+question = "How will trustworthy AI be achieved?"
+
+messages = [sys_message,
+    {"role": "user", "content": example_text},
+    {"role": "user", "content": question}]
+
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
+response = pipe(messages, max_new_tokens=200, pad_token_id=tokenizer.eos_token_id)
+
+print(response[0]["generated_text"][-1]["content"])
+
+# Example output (actual model behavior may vary):
+#
+# Q: "How will trustworthy AI be achieved?"
+# A: "...A consistent and high level of protection throughout the Union should therefore be ensured in order to achieve trustworthy AI, while divergences hampering the free circulation, innovation, deployment and the uptake of AI systems and related products and services within the internal market should be prevented..."
+```
+
+You can steer how the model answers (e.g. how long or precise the quote is) to some extent using standard generation parameters like temperature, top_p, and top_k.
+
+---
+
+## Intended Use
+
+This model is intended to be used in explorations and for research. It was fine-tuned to explore how LLMs can support accurate question answering using direct quotes — potentially helpful especially when it comes to pulling precise references from complex policy texts like the GDPR or the EU AI Act.
+
+At this stage, it's an **experimental prototype** — for testing, demoing, or just seeing how far citation-style prompting can go.
+
+- It's **not production-ready**, and should only be used in experimental and research contexts.  
+- It does **not provide legal advice**, and its outputs should always be verified by a human in professional settings.
+
+We’re curious where this can go — and happy to hear from you with feedback and ideas for improvement.
+
+---
+
+## 🔖 License & Attribution
+
+- This model is a derivative of [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
+- Licensed under the [LLaMA 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
+- Redistributed with required attribution: **Built with LLaMA**
+
+---
+
+Interested in using this model or adapting it for your use case?
+**[→ Contact us](mailto:kontakt@seasparks.de)**
diff --git a/config.json b/config.json
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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+    128008,
+    128009
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+  "head_dim": 128,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "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": {
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+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.50.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}
diff --git a/generation_config.json b/generation_config.json
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+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.50.0"
+}
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+      "rstrip": false,
+      "single_word": false,
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+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizer"
+}