commit 066eac3161a876d8b2b49728b2281e261176ffa1 Author: ModelHub XC Date: Thu May 7 20:46:38 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: ai4bharat/hercule-de-lora Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..94fe7f5 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,56 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 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 +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack 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 +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* 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 +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text + +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +model-00002-of-00007.safetensors filter=lfs diff=lfs merge=lfs -text +model-00007-of-00007.safetensors filter=lfs diff=lfs merge=lfs -text +model-00005-of-00007.safetensors filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +model-00004-of-00007.safetensors filter=lfs diff=lfs merge=lfs -text +model-00003-of-00007.safetensors filter=lfs diff=lfs merge=lfs -text +model-00001-of-00007.safetensors filter=lfs diff=lfs merge=lfs -text +model-00006-of-00007.safetensors filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..31ad039 --- /dev/null +++ b/README.md @@ -0,0 +1,99 @@ +--- +library_name: transformers +license: mit +language: +- de +metrics: +- pearsonr +- spearmanr +- accuracy +base_model: +- meta-llama/Llama-3.1-8B-Instruct +pipeline_tag: text-generation +--- + +# Model Card for Hercule +Hercule is a cross-lingual evaluation model introduced as part of the CIA Suite to assess multilingual Large Language Models (LLMs). It addresses the challenge of evaluating multilingual LLMs by using English reference responses to score multilingual outputs. + +Fine-tuned on the INTEL dataset, Hercule demonstrates better alignment with human judgments compared to zero-shot evaluations by proprietary models like GPT-4, on the RECON test set. It excels particularly in low-resource scenarios and supports zero-shot evaluations on unseen languages. The model employs reference-based evaluation, providing feedback and scores on a 1-5 scale, and highlights the effectiveness of lightweight fine-tuning methods (like LoRA) for efficient multilingual evaluation. All FFT models and LoRA weights are available [here](https://huggingface.co/collections/ai4bharat/cia-suite-66ea9a7e18a6c70bd8de27a1). + + +# Model Details + +## Model Description + +- **Model type:** Evaluator Language model +- **Language(s) (NLP):** German +- **Related Models:** [Hercule Models](https://huggingface.co/collections/ai4bharat/cia-suite-66ea9a7e18a6c70bd8de27a1) +- **Resources for more information:** + - [Research paper](https://arxiv.org/abs/2410.13394) + - [GitHub Repo](https://github.com/AI4Bharat/CIA) + + +Hercule in fine-tuned on [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) using Intel training data and evaluated on Recon test set. Models for other languages are available in [CIA Suite](https://huggingface.co/collections/ai4bharat/cia-suite-66ea9a7e18a6c70bd8de27a1). + +## Prompt Format + +We’ve developed wrapper functions and classes to make it easy to work with Hercule. Check them out on our [github repository](https://github.com/AI4Bharat/CIA) – we highly recommend using them! + +If you only need to use the model for your specific use case, please follow the prompt format provided below. + +### Reference Guided Direct Assessment +The Hercule model expects four input components: an evaluation instruction (multilingual), a response to evaluate (multilingual), a scoring rubric (English), and a reference answer (English). Use the prompt format provided below, ensuring that you include the instruction, response, reference answer, evaluation criteria, and a detailed score rubric for each score from 1 to 5. + +After running inference with HERCULE, the output will include feedback and a score, separated by the phrase ```[RESULT]```. +``` +###Task Description: +An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. +1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. +2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. +3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)\" +4. Please do not generate any other opening, closing, and explanations. + +###The instruction to evaluate: +{instruction} + +###Response to evaluate: +{response} + +###Reference Answer (Score 5): +{reference_answer} + +###Score Rubrics: +[{criteria}] +Score 1: {score1_rubric} +Score 2: {score2_rubric} +Score 3: {score3_rubric} +Score 4: {score4_rubric} +Score 5: {score5_rubric} + +###Feedback: +``` + +We use the same evaluation prompt as used in [Prometheus 2](https://huggingface.co/prometheus-eval/prometheus-7b-v2.0). + +## Links for Reference + +- **Repository**: https://github.com/AI4Bharat/CIA +- **Paper**: https://arxiv.org/abs/2410.13394 +- **Point of Contact**: sumanthd@cse.iitm.ac.in, safikhan@ai4bharat.org + +## License +Intel training data is created from [Feedback Collection](https://huggingface.co/datasets/prometheus-eval/Feedback-Collection) which is subject to OpenAI's Terms of Use for the generated data. If you suspect any violations, please reach out to us. + + +# Citation + + +If you find the following model helpful, please consider citing our paper! + +**BibTeX:** + +```bibtex +@article{doddapaneni2024crosslingual, + title = {Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs}, + author = {Sumanth Doddapaneni and Mohammed Safi Ur Rahman Khan and Dilip Venkatesh and Raj Dabre and Anoop Kunchukuttan and Mitesh M. Khapra}, + year = {2024}, + journal = {arXiv preprint arXiv: 2410.13394} +} +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..0eccdef --- /dev/null +++ b/config.json @@ -0,0 +1,39 @@ +{ + "_name_or_path": "de-llama-8b-it-lora-v0.5-merged", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "hidden_act": "silu", + "hidden_size": 4096, + "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": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + "rope_theta": 500000.0, + 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+ "128252": { + "content": "<|reserved_special_token_244|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128253": { + "content": "<|reserved_special_token_245|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128254": { + "content": "<|reserved_special_token_246|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128255": { + "content": "<|reserved_special_token_247|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "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|>", + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|eot_id|>", + "tokenizer_class": "PreTrainedTokenizerFast" +}