Update README.md

README.md

@@ -11,9 +11,9 @@ tags:
 - code verification
 ---
 
-# Model Card for ThinkPRM-14B
+# Model Card for ThinkPRM-1.5B
 
-ThinkPRM-14B is a generative Process Reward Model (PRM) based on the R1-Distill-Qwen-14B architecture. It is fine-tuned to perform step-by-step verification of reasoning processes (like mathematical solutions) by generating an explicit verification chain-of-thought (CoT) that involves labeling every step. It is designed to be highly data-efficient, requiring significantly less supervision data than traditional discriminative PRMs while achieving strong performance.
+ThinkPRM-1.5B is a generative Process Reward Model (PRM) based on the R1-Distill-Qwen-1.5B architecture. It is fine-tuned to perform step-by-step verification of reasoning processes (like mathematical solutions) by generating an explicit verification chain-of-thought (CoT) that involves labeling every step. It is designed to be highly data-efficient, requiring significantly less supervision data than traditional discriminative PRMs while achieving strong performance.
 
 Here's an example of the model output: 
 
@@ -22,11 +22,11 @@ Here's an example of the model output:
 
 ### Model Description
 
-ThinkPRM-14B provides step-level verification scores by generating natural language critiques and correctness judgments for each step in a given solution prefix. It leverages the underlying reasoning capabilities of the base Large Reasoning Model (LRM) and enhances them through fine-tuning on a small (1K examples) dataset of synthetically generated verification CoTs. These synthetic CoTs were produced by prompting QwQ-32B-Preview and filtered against ground-truth step labels from the PRM800K dataset to ensure quality.
+ThinkPRM-1.5B provides step-level verification scores by generating natural language critiques and correctness judgments for each step in a given solution prefix. It leverages the underlying reasoning capabilities of the base Large Reasoning Model (LRM) and enhances them through fine-tuning on a small (1K examples) dataset of synthetically generated verification CoTs. These synthetic CoTs were produced by prompting QwQ-32B-Preview and filtered against ground-truth step labels from the PRM800K dataset to ensure quality.
 
-The model uses a standard language modeling objective, making it interpretable and allowing it to scale process verification compute by generating longer or multiple verification CoTs. It demonstrated superior performance compared to LLM-as-a-judge and discriminative PRM baselines (based on the same R1-Distill-Qwen-14B model but trained on ~100x more labels) on benchmarks including ProcessBench, MATH-500, AIME '24, GPQA-Diamond, and LiveCodeBench.
+The model uses a standard language modeling objective, making it interpretable and allowing it to scale process verification compute by generating longer or multiple verification CoTs. It demonstrated superior performance compared to LLM-as-a-judge and discriminative PRM baselines (based on the same R1-Distill-Qwen-1.5B model but trained on ~100x more labels) on benchmarks including ProcessBench, MATH-500, AIME '24, GPQA-Diamond, and LiveCodeBench.
 
-- **Finetuned from model [optional]:** [R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
+- **Finetuned from model [optional]:** [R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
 
 ### Model Sources [optional]
 
@@ -36,7 +36,7 @@ The model uses a standard language modeling objective, making it interpretable a
 
 ### Direct Use
 
-ThinkPRM-14B is intended for verifying the correctness of step-by-step reasoning processes. Primary uses include:
+ThinkPRM-1.5B is intended for verifying the correctness of step-by-step reasoning processes. Primary uses include:
 - **Scoring Solutions:** Assigning step-level or overall scores to candidate solutions for ranking in Best-of-N sampling or guiding tree search in reasoning tasks.
 - **Generating Verification Rationales/CoTs:** Producing detailed chain-of-thought verifications that explain *why* a particular step is correct or incorrect, aiding interpretability.
 - **Standalone Verification:** Evaluating the correctness of a given problem-solution pair.
@@ -54,7 +54,7 @@ The model has been evaluated on mathematical reasoning (MATH, AIME), scientific
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from vllm import LLM, SamplingParams
 
-model_id = "launch/ThinkPRM-14B" # Replace with actual model ID on Hub
+model_id = "launch/ThinkPRM-1.5B" # Replace with actual model ID on Hub
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 llm = LLM(model=model_id, max_model_len=16384)