Update README.md

README.md

@@ -5,200 +5,95 @@ datasets:
 - PJMixers-Dev/dolphin-deepseek-1k-think-1k-response-filtered-ShareGPT
 ---
 
-# Model Card for Model ID
+# Mamba-790M Reasoning Faithfulness Model
 
 Fine-tuned Mamba-790M model for evaluating reasoning trace faithfulness in language models.
 
+## Model Description
 
+This is a LoRA-adapted version of [Mamba-790M](https://huggingface.co/state-spaces/mamba-790m-hf) trained to generate responses that faithfully follow provided reasoning traces.
 
-## Model Details
+- **Base Model**: state-spaces/mamba-790m-hf (790M parameters)
+- **Adapter Type**: LoRA (Low-Rank Adaptation)
+- **Training Data**: [Dolphin-DeepSeek Reasoning Dataset](https://huggingface.co/datasets/PJMixers-Dev/dolphin-deepseek-1k-think-1k-response-filtered-ShareGPT)
 
-### Model Description
+## Intended Use
 
-<!-- Provide a longer summary of what this model is. -->
+This model is designed for research on:
+- **Reasoning faithfulness**: Testing if model outputs align with stated reasoning
+- **AI interpretability**: Understanding how models follow (or deviate from) reasoning traces
+- **Alignment research**: Measuring consistency between reasoning and conclusions
 
+### Example Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
 
+# Load model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "state-spaces/mamba-790m-hf",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
 
-- **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 = PeftModel.from_pretrained(base_model, "NakshJain/mamba-790m-resoning")
+tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-790m-hf", trust_remote_code=True)
 
-### Model Sources [optional]
+# Format: <user>question</user><think>reasoning</think><answer>
+prompt = "<user>What is 15 + 27?</user><think>Let me add: 15 + 27 = 42</think><answer>"
 
-<!-- Provide the basic links for the model. -->
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+print(response)
+# Output: 42
+```
 
 ## Training Details
 
 ### 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[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]
+- **Source**: Dolphin-DeepSeek filtered ShareGPT conversations
+- **Training Set**: 18500 reasoning examples
+- **Validation Set**: 1500 examples
+- **Test Set**: 500 examples
+- **Format**: Structured as `<user>query</user><think>reasoning</think><answer>response</answer>`
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Training Configuration
 
-[More Information Needed]
+- **LoRA Rank (r)**: 32
+- **LoRA Alpha**: 32
+- **LoRA Dropout**: 0.05
+- **Target Modules**: `in_proj`, `x_proj`, `dt_proj`
+- **Learning Rate**: 1.5e-5
+- **Batch Size**: 4 (effective: 8 with gradient accumulation)
+- **Epochs**: 1
+- **Optimizer**: AdamW
+- **LR Schedule**: Cosine with 3% warmup
 
-## More Information [optional]
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model, please cite:
+```bibtex
+@misc{mamba-reasoning-faithfulness-2024,
+  author = {Naksh Jain},
+  title = {Mamba-790M Reasoning Faithfulness Model},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/NakshJain/mamba-790m-resoning}
+}
+```
 
-[More Information Needed]
+## Acknowledgments
 
-## Model Card Contact
+- **Base Model**: [Mamba](https://github.com/state-spaces/mamba) by Tri Dao and Albert Gu
+- **Training Dataset**: Dolphin-DeepSeek filtered by PJMixers-Dev
+- **Framework**: HuggingFace Transformers, PEFT
 
-[More Information Needed]
-### Framework versions
+## License
 
-- PEFT 0.13.2
+Apache 2.0 (inherits from base Mamba model)