Update README metadata and details for r2 release

README.md

@@ -1,210 +1,161 @@
 ---
-base_model: unsloth/Ministral-3-3B-Instruct-2512
+license: apache-2.0
+language:
+- en
+base_model:
+- unsloth/Ministral-3-3B-Instruct-2512
+base_model_relation: adapter
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/Ministral-3-3B-Instruct-2512
+- canis-teach
+- ministral
+- education
 - lora
-- sft
 - transformers
-- trl
-- unsloth
+- tutoring
+- generalist
+- math
+- science
+- humanities
+- language
+pipeline_tag: text-generation
+datasets:
+- CanisAI/teach-generalist-v1
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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. -->
-
-- **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
+# Canis.teach - Ministral-3B Instruct (Generalist)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Generalist LoRA adapters for the Canis.teach suite, capable of tutoring across Math, Science, Humanities, and Language Arts.
 
-[More Information Needed]
+- **Base Model**: unsloth/Ministral-3-3B-Instruct-2512
+- **Release**: CanisAI/teach-generalist-ministral-3b-r2
+- **Project**: Canis.teach - Learning that fits.
+- **Subject**: Generalist (All Subjects)
 
-### Downstream Use [optional]
+## What is this?
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This repository provides LoRA adapters fine-tuned on Generalist (All Subjects) tutoring dialogues. Apply these adapters to the base model to enable subject-aware, didactic behavior without downloading a full merged checkpoint.
 
-[More Information Needed]
+The model is designed to **teach, not just answer** - providing step-by-step explanations, hints, and pedagogically structured responses.
 
-### Out-of-Scope Use
+For ready-to-run merged models or Ollama-friendly GGUF quantizations, see the "Related Models" section.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+## Quick Start
 
-[More Information Needed]
+### Installation
+```bash
+pip install transformers peft torch
+```
 
-## Bias, Risks, and Limitations
+### Usage (LoRA)
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+base = "unsloth/Ministral-3-3B-Instruct-2512"
+adapter = "CanisAI/teach-generalist-ministral-3b-r2"
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(base, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    base, 
+    device_map="auto",
+    torch_dtype="auto"
+)
+model = PeftModel.from_pretrained(model, adapter)
 
-### Recommendations
+# Example prompt
+prompt = "Explain the concept of entropy in simple terms."
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
 
-<!-- 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]
+outputs = model.generate(
+    inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.8,
+    top_k=40,
+    do_sample=True
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## 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]
+- **Base Model**: unsloth/Ministral-3-3B-Instruct-2512
+- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA
+- **Framework**: Unsloth + TRL/PEFT
+- **Data**: Canis.lab-curated Generalist (All Subjects) tutoring dialogues
+- **Target Modules**: Query, Key, Value, Output projections, MLP gates (gate, up, down)
+- **Rank**: 32
+- **Alpha**: 32
 
-## Evaluation
+## Intended Use
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+- **Primary**: Subject-aware tutoring for Generalist (All Subjects) education
+- **Applications**: Educational prototypes, tutoring systems, research
+- **Approach**: Stepwise explanations, pedagogical hints, rubric-aligned responses
+- **Target Audience**: Students, educators, researchers
 
-### Testing Data, Factors & Metrics
+## Model Behavior
 
-#### Testing Data
+The model is optimized for:
+- Clear, step-by-step explanations
+- Appropriate difficulty progression  
+- Encouraging learning through hints rather than direct answers
+- Subject-specific pedagogical approaches
+- Maintaining educational standards and accuracy
 
-<!-- This should link to a Dataset Card if possible. -->
+## Recommended Settings
 
-[More Information Needed]
+For optimal tutoring behavior:
+- **Temperature**: 0.6-0.8
+- **Top-p**: 0.8-0.9
+- **Top-k**: 20-40
+- **Max tokens**: 512-1024
 
-#### Factors
+## Safety and Limitations
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+**Important Considerations**:
+- Human oversight required for educational use
+- May occasionally hallucinate or oversimplify complex topics
+- For fact-critical applications, consider RAG with verified curriculum sources
+- Follow your institution's data privacy and AI usage policies
+- Not a replacement for qualified human instruction
 
-[More Information Needed]
+## Related Models
 
-#### Metrics
+| Type | Repository | Description |
+|------|------------|-------------|
+| **LoRA Adapters** | `CanisAI/teach-generalist-ministral-3b-r2` | This repository (lightweight) |
+| **Merged Model** | (Coming Soon) | Ready-to-use full model | 
+| **GGUF Quantized** | (Coming Soon) | Ollama/llama.cpp compatible | 
+| **Dataset** | `CanisAI/teach-generalist-v1` | Training data |
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+## License
 
-[More Information Needed]
+This model inherits the license from the base model (unsloth/Ministral-3-3B-Instruct-2512). Please review the base model's license terms before use.
 
-### Results
+## Citation
 
-[More Information Needed]
+```bibtex
+@misc{canis-teach-teach-generalist,
+  title={Canis.teach Generalist Tutor},
+  author={CanisAI},
+  year={2026},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/CanisAI/teach-generalist-ministral-3b-r2}}
+}
+```
 
-#### Summary
+## Acknowledgments
 
+- **MistralAI/Ministral Team** for the excellent base model
+- **Unsloth** for efficient training tools
+- **Hugging Face** ecosystem (Transformers, PEFT, TRL)
+- Educators and contributors supporting the Canis.teach project
 
-
-## 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]
-### Framework versions
-
-- PEFT 0.18.0
+---
+**Canis.teach** - Learning that fits.