Update README.md

README.md

@@ -6,202 +6,163 @@ tags:
 - base_model:adapter:meta-llama/Llama-3.2-3B-Instruct
 - lora
 - transformers
+- configuration-management
+- secrets-management
+- devops
+- multi-cloud
+license: mit
+language:
+- en
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# AnySecret Assistant
 
+A specialized AI assistant for AnySecret configuration management, fine-tuned on Llama-3.2-3B-Instruct to help with multi-cloud secrets and parameters management.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+This is a LoRA fine-tuned version of Llama-3.2-3B-Instruct, specifically trained to assist with AnySecret configuration management across AWS, GCP, Azure, and Kubernetes environments. The model can help with CLI commands, configuration setup, CI/CD integration, and Python SDK usage.
 
-- **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]
+- **Developed by:** anysecret-io
+- **Model type:** Causal Language Model (LoRA Adapter)
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/anysecret-io/anysecret
+- **Documentation:** https://anysecret.io
+- **Demo:** Coming soon
 
 ## 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. -->
+This model is designed to provide expert assistance with:
+- AnySecret CLI commands and usage patterns
+- Multi-cloud configuration (AWS, GCP, Azure, Kubernetes)
+- Secrets vs parameters classification and management
+- CI/CD pipeline integration
+- Python SDK implementation guidance
 
-[More Information Needed]
+### Example Usage
 
-### Downstream Use [optional]
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
+model = PeftModel.from_pretrained(base_model, "anysecret-io/anysecret-assistant")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
 
-[More Information Needed]
+prompt = "### Instruction:\nHow do I configure AnySecret for AWS?\n\n### Response:\n"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+```
 
 ### 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]
+This model is specifically trained for AnySecret configuration management and may not perform well on:
+- General programming questions unrelated to configuration management
+- Other secrets management tools or platforms
+- Security vulnerabilities or exploitation techniques
 
 ## 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]
+The model was trained on 43 curated examples across 7 categories:
+- **CLI Commands** (9 examples) - Command usage patterns
+- **AWS Configuration** (6 examples) - AWS Secrets Manager integration
+- **GCP Configuration** (6 examples) - Google Secret Manager setup
+- **Azure Configuration** (6 examples) - Azure Key Vault integration
+- **Kubernetes** (6 examples) - K8s secrets and ConfigMaps
+- **CI/CD Integration** (5 examples) - GitHub Actions, Jenkins workflows
+- **Python Integration** (5 examples) - SDK usage patterns
 
 ### 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
+- **Base Model:** meta-llama/Llama-3.2-3B-Instruct
+- **LoRA Rank:** 16
+- **LoRA Alpha:** 32
+- **Target Modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Learning Rate:** 2e-4
+- **Batch Size:** 1 (with gradient accumulation)
+- **Epochs:** 2-3
+- **Training regime:** fp16 mixed precision with 4-bit quantization
 
-<!-- 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
+## How to Get Started with the Model
 
-[More Information Needed]
+```python
+# Install requirements
+pip install torch transformers peft
 
-#### Summary
+# Load and use the model
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.2-3B-Instruct",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
 
-## Model Examination [optional]
+model = PeftModel.from_pretrained(base_model, "anysecret-io/anysecret-assistant")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
 
-<!-- Relevant interpretability work for the model goes here -->
+def ask_anysecret(question):
+    prompt = f"### Instruction:\n{question}\n\n### Response:\n"
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("### Response:\n")[-1].strip()
 
-[More Information Needed]
+# Example usage
+print(ask_anysecret("How do I set a secret using anysecret CLI?"))
+```
 
 ## 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]
+- **Hardware Type:** NVIDIA RTX 3090 / A6000
+- **Hours used:** ~2-4 hours per training run
+- **Training Framework:** PyTorch with PEFT and BitsAndBytes
+- **Quantization:** 4-bit NF4 for memory efficiency
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- **Architecture:** Llama-3.2 with LoRA adapters
+- **Objective:** Causal language modeling for instruction following
+- **LoRA Configuration:** Rank 16, Alpha 32, targeting attention and MLP layers
+- **Quantization:** 4-bit NF4 with double quantization
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
-
-[More Information Needed]
+- NVIDIA RTX 3090 (24GB VRAM) for 3B models
+- NVIDIA A6000 (48GB VRAM) for 13B models
 
 #### Software
+- PyTorch 2.0+
+- Transformers 4.35+
+- PEFT 0.6+
+- BitsAndBytes 0.41+
 
-[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]
-### Framework versions
+## Framework versions
 
-- PEFT 0.17.1
+- PEFT 0.17.1
+- Transformers 4.35.0
+- PyTorch 2.0.0
+- BitsAndBytes 0.41.0