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README.md

@@ -19,106 +19,64 @@ tags:
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
-This model is a fine-tuned coding assistant built on top of Qwen2.5-Coder using LoRA (Low-Rank Adaptation).  
-It is designed to improve performance in:
 
-- Code generation  
-- Debugging  
-- Code explanation  
-- Code optimization  
 
-The model also incorporates structured outputs including explanation, confidence, and relevancy signals.
-
----
 
+- **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 Sources [optional]
 
+<!-- Provide the basic links for the model. -->
 
-- **Developed by:** GIRISH KUMAR DEWANGAN]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [Causal Language Model (Code Generation & Debugging)]
-- **Language(s) (NLP):** [Python, general programming concepts]
-- **License:** [Apache 2.0]
-- **Finetuned from model [optional]:** [Qwen/Qwen2.5-Coder-0.5B-Instruct]
+- **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. -->
 
-[- Fix buggy Python code  
-- Explain code logic  
-- Optimize code  
-- Generate small functions  ]
+[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 -->
 
-[- Integration in coding assistants (VS Code extension, chatbots)  
-- Educational tools for learning programming  
-- AI-powered debugging tools  
-]
+[More Information Needed]
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[- Production-critical code without validation  
-- Security-sensitive code generation  
-- Large-scale system design  ]
+[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[- May generate incorrect or incomplete code  
-- May hallucinate fixes for ambiguous inputs  
-- Limited to training dataset scope  
-- Confidence scores are heuristic (not calibrated)
-]
+[More Information Needed]
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-- Always validate generated code before use  
-- Use human review for critical applications  
-- Combine with test cases for reliability  
+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.
 
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-
-# Base model
-base_model = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
-
-# ConicAI LLM model
-adapter_model = "girish00/ConicAI_LLM_model"
-
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained(base_model)
-
-# Load base model
-model = AutoModelForCausalLM.from_pretrained(base_model)
-
-# Load fine-tuned adapter
-model = PeftModel.from_pretrained(model, adapter_model)
-
-# Test prompt
-prompt = "Fix this code: def add(a,b) return a+b"
-
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=200)
-
-print(tokenizer.decode(outputs[0]))
-```
+[More Information Needed]
 
 ## Training Details
 
@@ -126,31 +84,20 @@ print(tokenizer.decode(outputs[0]))
 
 <!-- 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. -->
 
-[Synthetic dataset (~8K–10K samples)
-Includes:
-Bug fixing tasks
-Code explanation
-Optimization tasks
-Structured outputs (explanation, confidence, relevancy)]
+[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. -->
-Method: LoRA fine-tuning
-Framework: Transformers + PEFT
 
 #### Preprocessing [optional]
 
-
+[More Information Needed]
 
 
 #### Training Hyperparameters
 
-- **Training regime:** [Batch size: 2
-Epochs: 1–2
-Learning rate: 2e-4
-Max sequence length: 512
-Quantization: 4-bit (for efficient training)] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **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]
 
@@ -161,10 +108,6 @@ Quantization: 4-bit (for efficient training)] <!--fp32, fp16 mixed precision, bf
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
-Metrics
-Qualitative evaluation (manual testing)
-Relevancy score (embedding similarity)
-Hallucination detection (syntax validation)
 
 ### Testing Data, Factors & Metrics
 
@@ -178,20 +121,17 @@ Hallucination detection (syntax validation)
 
 <!-- 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. -->
 
-[Qualitative evaluation (manual testing)
-Relevancy score (embedding similarity)
-Hallucination detection (syntax validation)]
+[More Information Needed]
 
 ### Results
 
-Improved code correctness compared to base model
-Better explanation quality
-Reduced syntax errors
+[More Information Needed]
 
 #### Summary
 
@@ -207,23 +147,23 @@ Reduced syntax errors
 
 <!-- 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:** Google Colab GPU (T4)
-- **Hours used:** ~30–60 minutes
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
 - **Cloud Provider:** [More Information Needed]
 - **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** Low (small-scale training)
+- **Carbon Emitted:** [More Information Needed]
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[Base: Qwen2.5-Coder
-Fine-tuning: LoRA adapter]
+[More Information Needed]
 
 ### Compute Infrastructure
 
-
+[More Information Needed]
 
 #### Hardware
 
@@ -231,19 +171,12 @@ Fine-tuning: LoRA adapter]
 
 #### Software
 
-[Transformers
-PEFT (v0.19.0)
-Datasets]
+[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. -->
-@misc{coding-llm-2026,
-  author = {Girish},
-  title = {Coding LLM Model},
-  year = {2026},
-  publisher = {Hugging Face}
-}
+
 **BibTeX:**
 
 [More Information Needed]