From 274931028e8bea7ab39cf09d7f5954788b6ebccd Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Mon, 20 Apr 2026 05:29:35 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: EpistemeAI/ReasoningCore-3B-RE1-V2C Source: Original Platform --- .gitattributes | 36 + README.md | 198 +++ config.json | 39 + generation_config.json | 8 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 261 ++++ pytorch_model-00001-of-00002.bin | 3 + pytorch_model-00002-of-00002.bin | 3 + pytorch_model.bin.index.json | 261 ++++ special_tokens_map.json | 23 + tokenizer.json | 3 + tokenizer_config.json | 2070 ++++++++++++++++++++++++++++++ 13 files changed, 2911 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 pytorch_model-00001-of-00002.bin create mode 100644 pytorch_model-00002-of-00002.bin create mode 100644 pytorch_model.bin.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..316e9da --- /dev/null +++ b/README.md @@ -0,0 +1,198 @@ +--- +base_model: EpistemeAI/ReasoningCore-3B-RE1-V2B +tags: +- text-generation-inference +- transformers +- unsloth +- llama +- trl +license: llama3.2 +language: +- en +--- + +Note: This is an experimental model. + +# ReasoningCore‑3B-RE1-V2BC + +**ReasoningCore‑3B** is a multilingual, reasoning‑enhanced large language model developed by EpitemeAI. Pretrained on vast amounts of publicly available data and instruction‑tuned to excel at nuanced reasoning, dialogue management, retrieval, and summarization tasks, it often outperforms many current open source and proprietary conversational models on a range of industry benchmarks. Fine tuned with reasoning dataset. + +Supervised fine tuned with dataset: +- RUC-AIBOX/STILL-3-Preview-RL-Data, thanks for RUC-AIBOX +- 2nd SFT with dataset: AI-MO/NuminaMath-TIR + +### We used GRPO technique: + +To provide a comprehensive overview of Group Relative Policy Optimization (GRPO), a post-training technique for Large Language Models (LLMs), and its application in the DeepSeek-R1 model. +- Post-training with GRPO involves using a reinforcement learning (RL) technique to optimize the Large Language Model (LLM) after it has been initially trained. +- GRPO specifically focuses on scaling test-time compute for extended reasoning tasks, making it suitable for tackling complex problems like mathematical problem-solving. +- Unlike earlier methods that utilized search-heuristic approaches, GRPO relies exclusively on RL for post-training, thereby enhancing the model's ability to handle nuanced tasks. +- The GRPO technique is available through the TRL library, and the Hugging Face Science team is working to reproduce the full DeepSeek-R1 training process, which can be explored in their + + +--- + +## Model Information + +- **Model Developer:** EpitemeAI +- **Model Architecture:** + ReasoningCore‑3B is an auto‑regressive language model built on an optimized transformer architecture. It incorporates specialized reasoning pathways and has been fine‑tuned using Group Robust Preference Optimization(GRPO), and both supervised learning and reinforcement learning with human feedback (RLHF) to align with human expectations for clarity, accuracy, and safety in complex tasks. + +| | Training Data | Params | Input Modalities | Output Modalities | Context Length | GQA | Shared Embeddings | Token Count | Knowledge Cutoff | +|--------------------------------|--------------------------------------------------|--------|-----------------------|------------------------------|----------------|-----|-------------------|----------------|-------------------| +| **ReasoningCore‑3B (text only)** | A new mix of publicly available online data. | 3B | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 | + +- **Supported Languages:** + Officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. While the pretraining included a broader range of languages, additional languages can be fine‑tuned in compliance with the community license and acceptable use policies. +- **Model Release Date:** Sept 25, 2024 +- **Status:** Static model trained on an offline dataset. Future iterations may further enhance its reasoning capabilities and safety features. +- **License:** Use is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). +- **Feedback:** For questions or comments, please refer to the [GitHub repository README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2) or follow the linked instructions. + +--- + +## Intended Use + +### Use Cases +- **Conversational AI:** Assistant‑like interactions. +- **Knowledge Retrieval & Summarization:** Dynamic extraction and condensation of information. +- **Mobile AI‑Powered Writing Assistants:** Query reformulation and natural language generation. +- **General Natural Language Generation:** Any application that benefits from advanced reasoning abilities. + +### Out of Scope +- Deployments that violate applicable laws or trade compliance regulations. +- Use cases that conflict with the Acceptable Use Policy or licensing terms. +- Deployments in languages not explicitly supported (unless additional safety and performance validations are performed). + +--- + +## How to Use + +ReasoningCore‑3B can be integrated using popular machine learning frameworks. Two primary methods are provided: + +## Use system prompt +```bash +SYSTEM_PROMPT = """ +Respond in the following format: + +... + +""" +``` + +For math: +```bash +"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. + +### Instruction: +You are a super math assistant. + +### Question: +{} + +### Response: + +{}""" +``` + +### Use with Transformers + +Ensure you have transformers version 4.43.0 or later installed: + +```bash +pip install --upgrade transformers + +import torch +from transformers import pipeline + +model_id = "EpistemeAI/ReasoningCore-3B-RE1-V2B" +pipe = pipeline( + "text-generation", + model=model_id, + torch_dtype=torch.bfloat16, + device_map="auto" +) +print(pipe("The secret to effective reasoning is")) +``` +## For Mathematical problems +Please use "Please reason step by step, and put your final answer within \boxed{}" in system prompt + + +## Responsibility & Safety + +### Responsible Deployment + +#### Approach: +- **ReasoningCore‑3B** is a foundational technology that includes built‑in safety guardrails. Developers are encouraged to integrate additional safeguards tailored to their specific applications. + +#### System‑Level Safety: +- The model is designed to be deployed as part of a broader system that implements safety measures (e.g., Prompt Guard, Code Shield) to ensure outputs remain safe even under adversarial conditions. + +--- + +### Safety Fine‑Tuning & Data Strategy + +#### Objectives: +- Provide a reliable tool for building secure and helpful reasoning systems. +- Mitigate adversarial misuse through advanced data selection and response optimization techniques. + +#### Methodology: +- Incorporate adversarial prompts during training to refine model refusals and response tone. +- Combine human‑curated data with synthetic data. +- Utilize iterative fine‑tuning using supervised learning, rejection sampling, and preference optimization. + +--- + +### Evaluations and Red Teaming + +#### Scaled Evaluations: +- Dedicated adversarial datasets were used to rigorously test the model’s robustness. Developers should perform context‑specific evaluations. + +#### Red Teaming: +- Experts in cybersecurity, adversarial machine learning, and responsible AI conducted recurring red team exercises to identify vulnerabilities and improve both performance and safety. + +--- + +### Critical Risk Mitigations + +- **CBRNE:** + The model has been evaluated to ensure it does not enhance capabilities for harmful activities involving chemical, biological, radiological, nuclear, or explosive materials. + +- **Child Safety:** + Expert assessments were conducted to evaluate and mitigate potential child safety risks. + +- **Cyber Attacks:** + Measures were taken to ensure the model cannot autonomously facilitate cyber‑offensive operations. + +--- + +### Ethical Considerations and Limitations + +#### Core Values: +- **ReasoningCore‑3B** is built on the values of openness, inclusivity, and helpfulness. It is designed to respect user autonomy and foster free thought and expression while mitigating potential harm. + +#### Testing and Limitations: +- Despite extensive testing across diverse scenarios, the model may occasionally produce inaccurate, biased, or objectionable outputs. Developers must perform additional safety testing and integrate further safeguards as needed. + +#### Resources for Safe Deployment, with Meta Safety Deployment: +- [Responsible Use Guide](https://llama.meta.com/responsible-use-guide) +- [Trust and Safety Resources](https://llama.meta.com/trust-and-safety) +- [Getting Started Guide](https://llama.meta.com/docs/get-started) + +--- + +### Conclusion + +**ReasoningCore‑3B** represents a significant advancement in multilingual, reasoning‑enhanced language models. Optimized for tasks requiring deep reasoning, contextual understanding, and safe, helpful interactions, it offers a powerful tool for both commercial and research applications. We invite developers and researchers to explore its capabilities and contribute to building secure, innovative AI systems. + +For further details, questions, or feedback, please email episteme.ai@proton.me + +# Uploaded model + +- **Developed by:** EpistemeAI +- **License:** apache-2.0 +- **Finetuned from model :** EpistemeAI/ReasoningCore-3B-RE1-V2B + +This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. + +[](https://github.com/unslothai/unsloth) \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..a0764d8 --- /dev/null +++ b/config.json @@ -0,0 +1,39 @@ +{ + "_name_or_path": "EpistemeAI/ReasoningCore-3B-RE1-V2B", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128009, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 3072, + "initializer_range": 0.02, + 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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 {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\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 #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\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 {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\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": {}, + "max_length": null, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_to_multiple_of": null, + "pad_token": "<|finetune_right_pad_id|>", + "pad_token_type_id": 0, + "padding_side": "left", + "tokenizer_class": "PreTrainedTokenizerFast", + "unk_token": null +}