Upload Qwen2ForCausalLM

README.md

@@ -1,152 +1,199 @@
-# II-Medical-7B-Preview
+---
+library_name: transformers
+tags: []
+---
 
-## I. Model Overview
+# Model Card for Model ID
 
-II-Medical-7B-Preview is a medical reasoning model trained on a comprehensive dataset of medical knowledge. The model is designed to enhance AI capabilities in medical reasoning. 
-## II. Training Methodology
+<!-- Provide a quick summary of what the model is/does. -->
 
-We collected and generated a comprehensive set of reasoning datasets for the medical domain and performed SFT fine-tuning on the Qwen/Qwen2.5-7B-Instruct model. Following this, we further optimized the SFT model by training DAPO on a hard-reasoning dataset to boost performance.
 
-For SFT stage we using the hyperparameters: 
 
-- Max Length: 16378.
-- Batch Size: 128.
-- Learning-Rate: 5e-5.
-- Number Of Epoch: 4.
+## Model Details
 
-For RL stage we setup training with:
+### Model Description
 
-- Max prompt length: 2048 tokens.
-- Max response length: 12288 tokens.
-- Overlong buffer: Enabled, 4096 tokens, penalty factor 1.0.
-- Clip ratios: Low 0.2, High 0.28.
-- Batch sizes: Train prompt 512, Generation prompt 1536, Mini-batch 32.
-- Responses per prompt: 16.
-- Temperature: 1.0, Top-p: 1.0, Top-k: -1 (vLLM rollout).
-- Learning rate: 1e-6, Warmup steps: 10, Weight decay: 0.1.
-- Loss aggregation: Token-mean.
-- Gradient clipping: 1.0.
-- Entropy coefficient: 0.
+<!-- Provide a longer summary of what this model is. -->
 
-## III. Evaluation Results
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-We evaluate on ten medical QA benchmarks include MedMCQA, MedQA, PubMedQA, medical related questions from MMLU-Pro and GPQA, small QA sets from Lancet and the New England
-Journal of Medicine,  4 Options  and 5 Options splits from the MedBullets platform and MedXpertQA.
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-| Model                   | MedMC | MedQA | PubMed | MMLU-P | GPQA | Lancet | MedB-4 | MedB-5 | MedX  | NEJM  | Avg   |
-|--------------------------|-------|-------|--------|--------|------|--------|--------|--------|------|-------|-------|
-| QWQ 32B                  | 69.73 | 87.03 | 88.5   | 79.86  | 69.17| 71.3   | 72.07  | 69.01  |24.98 |75.12  | 70.68 |
-| Qwen2.5-7B-IT            | 56.56 | 61.51 | 71.3   | 61.17  | 42.56| 61.17  | 46.75  | 40.58  |13.26 |59.04  | 51.39 |
-| HuatuoGPT-o1-8B          | 63.97 | 74.78 | **80.10**  | 63.71  | 55.38| 64.32  | 58.44  | 51.95  |15.79 |64.84  | 59.32 |
-| Med-reason               | 61.67 | 71.87 | 77.4   | 64.1   | 50.51| 59.7   | 60.06  | 54.22  |22.87 |66.8   | 59.92 |
-| M1                       | 62.54 | 75.81 | 75.80  | 65.86  | 53.08| 62.62  | 63.64  | 59.74  |19.59 |64.34  | 60.3  |
-| II-Medical-7B-Preview-Wo-RL | 69.13 | 84.05 | 77.5   | 73.49  | 55.12| **67.71**  | 69.48  | 64.28  |19.51 |**70.64**  | 65.1  |
-| II-Medical-7B-Preview-RL | **69.42** | **85.15** | 77.9   | **77.26**  | **55.90**| **65.29**  | **72.72**  | **68.50**  |**22.97** |68.66  | **66.4**  |
+### Model Sources [optional]
 
+<!-- Provide the basic links for the model. -->
 
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-## IV. Dataset Curation
+## Uses
 
-The training dataset comprises 581,204 samples from the following sources:
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### 1. Public Medical Reasoning Datasets (103,031 samples)
-- General Medical Reasoning: 40,544 samples
-- Medical-R1-Distill-Data: 22,000 samples
-- Medical-R1-Distill-Data-Chinese: 17,000 samples
-- UCSC-VLAA/m23k-tokenized: 23,487 samples
+### Direct Use
 
-### 2. Synthetic Medical QA Data with QwQ Data (225,700 samples)
-Generated from established medical datasets:
-- MedMcQA (from openlifescienceai/medmcqa): 183,000 samples
-- MedQA: 10,000 samples
-- MedReason: 32,700 samples
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-### 3. Curated Medical R1 Traces (338,055 samples)
+[More Information Needed]
 
-First we gather all the public R1 traces from:
+### Downstream Use [optional]
 
-- PrimeIntellect/SYNTHETIC-1
-- GeneralReasoning/GeneralThought-430K
-- a-m-team/AM-DeepSeek-R1-Distilled-1.4M
-- open-thoughts/OpenThoughts2-1M
-- nvidia/Llama-Nemotron-Post-Training-Dataset: Science subset only
-- Other resources: cognitivecomputations/dolphin-r1, ServiceNow-AI/R1-Distill-SFT,...
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-All R1 reasoning traces were processed through a domain-specific pipeline as follows:
+[More Information Needed]
 
-1. Embedding Generation: Prompts are embedded using sentence-transformers/all-MiniLM-L6-v2.
+### Out-of-Scope Use
 
-2. Clustering: Perform K-means clustering with 50,000 clusters.
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-3. Domain Classification:
+[More Information Needed]
 
-    - For each cluster, select the 10 prompts nearest to the cluster center.
-    - Classify the domain of each selected prompt using Qwen2.5-32b-Instruct.
-    - Assign the cluster's domain based on majority voting among the classified prompts.
+## Bias, Risks, and Limitations
 
-4. Domain Filtering: Keep only clusters labeled as Medical or Biology for the final dataset.
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
+[More Information Needed]
 
-### 4. Supplementary Math Dataset
-- Added 15,000 samples of reasoning traces from light-r1
-- Purpose: Enhance general reasoning capabilities of the model
+### Recommendations
 
-### Preprocessing Data
-1. Filtering for Complete Generation
-   - Retained only traces with complete generation outputs
-   - Removed incomplete or truncated samples
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-2. Length-based Filtering
-   - Minimum threshold: Keep only the prompt with more than three words.
-   - Maximum threshold: Keep only the traces with less than 7,143 words.
-   - Wait Token Filter: Removed traces with has more than 47 occurrences of "Wait" (97th percentile threshold).
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
+## How to Get Started with the Model
 
-### Data Decontamination
+Use the code below to get started with the model.
 
-We using two step decontamination:
-1. Following open-r1 project: We decontaminate a dataset using 10-grams with the evaluation datasets.
-2. After that, we using the fuzzy decontamination from `s1k` method with threshold 90%. 
+[More Information Needed]
 
-**Our pipeline is carefully decontaminated with the evaluation datasets.**
+## Training Details
 
-Finally, we open sources our dataset at [Our Medical Reasoning SFT dataset](https://huggingface.co/datasets/Intelligent-Internet/II-Medical-Reasoning-SFT-V0).
+### Training Data
 
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-## V. How To Use
-Our model can be utilized in the same manner as Qwen or Deepseek-R1-Distill models.
+[More Information Needed]
 
-For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm):
+### Training Procedure
 
-```bash
-vllm serve Intelligent-Internet/II-Medical-7B-Preview
-```
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang):
+#### Preprocessing [optional]
 
-```bash
-python -m sglang.launch_server --model Intelligent-Internet/II-Medical-7B-Preview
-```
+[More Information Needed]
 
-## VI. Usage Guidelines
 
-- Recommended Sampling Parameters: temperature = 0.6, top_p = 0.9
-- When using, explicitly request step-by-step reasoning and format the final answer within \boxed{} (e.g., "Please reason step-by-step, and put your final answer within \boxed{}.").
-## VII. Limitations and Considerations
+#### Training Hyperparameters
 
-- Dataset may contain inherent biases from source materials
-- Medical knowledge requires regular updates
-- Classification accuracy depends on embedding model and clustering parameters
-- Math reasoning traces may introduce domain-mixing effects
-- Please note that **It’s not suitable for medical use.**
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
+#### Speeds, Sizes, Times [optional]
 
-## VII. Citation
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-```bib
-@misc{2025II-Medical-7B-Preview,
-      title={II-Medical-7B-Preview: Medical Reasoning Model}, 
-      author={Intelligent Internet},
-      year={2025}
-}
-```
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "transformers_version": "4.51.0"
+}

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