commit 7fda465668ed690a6407717b155adb14d3d55f2c Author: ModelHub XC Date: Sat May 30 14:44:31 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: rishanthrajendhran/VeriFastScore Source: Original Platform 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..36ea793 --- /dev/null +++ b/README.md @@ -0,0 +1,225 @@ +--- +library_name: transformers +license: apache-2.0 +language: +- en +base_model: +- meta-llama/Llama-3.1-8B-Instruct +datasets: +- rishanthrajendhran/VeriFastScore +--- + +# Model Card for Model ID + + +VeriFastScore is a factuality evaluation model designed for long-form LLM outputs. It jointly extracts and verifies factual claims in a single model pass, providing a faster alternative to pipeline-based evaluators like VeriScore. + + +## Model Details + +### Model Description + + +This is a fine-tuned LLaMA 3.1 8B Instruct model trained to extract and verify factual claims in long-form text, given associated retrieved evidence. The model is designed to reduce inference latency and cost while maintaining high agreement with more expensive pipeline-based factuality metrics. + +- **Developed by:** NGRAM at UMD, Lambda Labs +- **Model type:** Factuality evaluation model (joint claim extraction and verification) (Causal LM) +- **Language(s) (NLP):** English +- **License:** Apache 2.0 +- **Finetuned from model:** meta-llama/Llama-3.1-8B-Instruct + +### Model Sources + + + +- **Repository:** github.com/RishanthRajendhran/VeriFastScore +- **Paper:** arxiv.org/abs/2505.16973 + +## Uses + + + +### Direct Use + + +The model takes as input a generated long-form response and a consolidated set of retrieved evidence sentences. It outputs a list of verifiable claims and corresponding factuality labels (Supported or Unsupported). + +### Downstream Use + + +Can be used to score factuality in evaluation pipelines (e.g., RLHF supervision), dataset filtering, or system-level benchmarking of LLM factuality. + +### Out-of-Scope Use + + +- Not intended for use without retrieved evidence. + +## Bias, Risks, and Limitations + + +The model inherits potential biases from its teacher supervision (VeriScore) and the base language model. It may underperform on ambiguous claims, noisy evidence, or non-English text. + +### Recommendations + + + +Use caution in high-stakes domains and supplement with human review if used for system-level feedback or alignment. Avoid use cases without explicit, relevant evidence input. + +## How to Get Started with the Model + +Use the code below to get started with the model. +```python +from transformers import AutoTokenizer, AutoModelForCausalLM + +tokenizer = AutoTokenizer.from_pretrained("rishanthrajendhran/VeriFastScore") +model = AutoModelForCausalLM.from_pretrained("rishanthrajendhran/VeriFastScore") + +system_prompt = '''You are trying to verify how factual a response is by extracting fine-grained, verifiable claims. Each claim must describe one single event or one single state (for example, “Nvidia was founded in 1993 in Sunnyvale, California, U.S.”) in one sentence with at most one embedded clause. Each fact should be understandable on its own and require no additional context. This means that all entities must be referred to by name but not by pronoun. Use the name of entities rather than definite noun phrases (e.g., “the teacher”) whenever possible. If a definite noun phrase is used, be sure to add modifiers (e.g., an embedded clause or a prepositional phrase). Each fact must be situated within relevant temporal and location details whenever needed. + +All necessary specific details—including entities, dates, and locations—must be explicitly named, and verify here means that every detail of a claim is directly confirmed by the provided evidence. The verification process involves cross-checking each detail against the evidence; a detail is considered verified if it is clearly confirmed by the evidence. + +Avoid extracting stories, personal experiences, hypotheticals (e.g., those using “would be” or the subjunctive mood), subjective opinions, suggestions, advice, instructions, or similarly non-factual content; however, biographical, historical, scientific, and similar texts are acceptable. Also, ignore any listed references. + +For each extracted claim, classify it as follows: + +Supported: Every detail of the claim (including entities, dates, and locations) is directly confirmed by the provided evidence with no contradictions. +Unsupported: One or more details of the claim are either missing from or contradicted by the provided evidence, even though the claim remains verifiable using external sources. + +You do not need to justify what you extract. + +Output format: +: +: +… +: + +If no verifiable claim can be extracted, simply output "No verifiable claim."''' + +prompt = "### Response\n{response}\n### Evidence\n{evidence}".format( + response=response, + evidence=evidence +) + +conversation_history = [ + { + "role": "system", + "content": system_prompt, + }, { + "role": "user", + "content": prompt + } +] +inputs = tokenizer.apply_chat_template( + conversation=conversation_history, + add_generation_prompt=True, + tokenize=True, + truncation=False, + padding="do_not_pad", + return_tensors="pt" +).to(model.device) + +outputs = model.generate(inputs, max_new_tokens=2048) +print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) +``` + +## Training Details + +### Training Data + + +Synthetic (response, evidence, claim, label) examples generated via VeriScore applied to long-form prompts from datasets like Tulu3-Personas. The dataset is available on HuggingFace. See paper for more details. + +### Training Procedure + + +Two-stage fine-tuning: + +- Stage 1: Supervision with claim-level evidence. +- Stage 2: Supervision with a mixture of claim- and sentence-level evidence. + +#### Preprocessing + +In the original VeriFastScore pipeline, evidence is aggregated at the sentence level per response, tokenized, and paired with output claims using a structured prompt template. However, the \VeriFastScore model is agnostic to the provenance of the provided evidence. + + +#### Training Hyperparameters + +- **Training regime:** : bf16 mixed precision +- **Optimizer**: AdamW +- **Scheduler**: Cosine decay +- **Batch size**: 8 (effective) +- **Epochs**: 10 (5+5) + +#### Speeds, Sizes, Times + + +- Training Time: ~24*4 GPU hours (roughly 2 sec per training instance) +- Model Size: 8B parameters + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + +- ~9k test instances using both claim-level and sentence-level evidence +- Model rankings: 100 prompts from the Tulu3-Personas test set with responses from 12 LLMs + +#### Metrics + + +- Claim-level accuracy, precision, recall (automatic judgements using GPT-4o-mini) +- Pearson correlation with factuality scores from VeriScore + +### Results +- (Claim-level evidence) Pearson r with VeriScore: 0.86, p<0.001 +- (Sentence-level evidence) Pearson r with VeriScore: 0.80, p<0.001 +- Model rankings: + - System-level Pearson r: 0.94, p<0.001 + - Speedup: 6.6× (9.9× if excluding retrieval) +See paper for more details. + +#### Summary +VeriFastScore delivers fast, interpretable factuality scores that closely track a strong multi-step baseline, while reducing cost and latency for large-scale evaluation. + + + +## Model Examination + + +Future work could explore explainability or rationale generation via mode-switching techniques or chain-of-thought prompting. + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** A100 (Training), GH200 (Evaluation, Testing) +- **Hours used:** 96 (Training) +- **Cloud Provider:** Lambda Labs +- **Compute Region:** us-central1 +- **Carbon Emitted:** 10.37 (Training) + +## Citation [optional] + +**BibTeX:** + +
+  @misc{rajendhran2025verifastscorespeedinglongformfactuality,
+      title={VeriFastScore: Speeding up long-form factuality evaluation}, 
+      author={Rishanth Rajendhran and Amir Zadeh and Matthew Sarte and Chuan Li and Mohit Iyyer},
+      year={2025},
+      eprint={2505.16973},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.16973}, 
+}
+
+ +## Model Card Contact + +rishanth@umd.edu \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..237140f --- /dev/null +++ b/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "/home/ubuntu/iyyerlab/rishanth/multiFactRM/model_outputs/Meta-Llama-3.1-8B-Instruct-FastFact-Mixture-FT", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "head_dim": 128, + "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, + 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"<|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 + }, + "128256": { + "content": "[PAD]", + "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|>", + "extra_special_tokens": {}, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 128000, + "pad_token": "[PAD]", + "tokenizer_class": "PreTrainedTokenizer" +}