Update README.md

README.md

@@ -7,4 +7,289 @@ tags:
   - lora
   - transformers
   - peft
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+---
+# Model Card for Capable-1.0
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Capable-1.0 is a LoRA fine-tuned adapter built on top of [`Qwen/Qwen3.5-0.8B`](https://huggingface.co/Qwen/Qwen3.5-0.8B), developed and released by Weightix Labs. It is designed to enhance the capabilities of the base model while keeping the relatively small footprint and efficiency of the underlying 0.8B-parameter model.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+Capable-1.0 is a LoRA adapter, not a standalone full model — the base model is required for inference.
+
+- **Developed by:** Weightix Labs
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** Weightix Labs
+- **Model type:** LoRA adapter for text generation
+- **Language(s) (NLP):** Primarily English; multilingual capabilities depend on the base model and training data
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** Qwen/Qwen3.5-0.8B
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://huggingface.co/weightix-labs/Capable-1.0
+- **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. -->
+
+Capable-1.0 can be used as a parameter-efficient adapter for text-generation applications, loaded on top of the Qwen3.5-0.8B base model using the Hugging Face PEFT library. Potential applications include:
+
+- General text generation
+- Conversational AI
+- Prototyping small language-model applications
+- Experimentation with parameter-efficient fine-tuning
+- Local and resource-constrained inference
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+The adapter can be incorporated into applications that use the Qwen3.5-0.8B model architecture, provided that the applicable model and dataset licenses are respected. Users may also merge the adapter with the base model when appropriate, or continue fine-tuning it for specialized downstream tasks.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+Capable-1.0 should not be relied upon as an authoritative source of factual information. It should not be used as the sole basis for:
+
+- Medical, legal, or financial decisions
+- High-impact decisions about individuals
+- Autonomous actions with significant real-world consequences
+- Generating or distributing harmful or illegal content
+- Circumventing safety, security, or access controls
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Capable-1.0 inherits many of the limitations of its base model and may also introduce behaviors associated with its fine-tuning data, including:
+
+- Hallucinated or incorrect information
+- Biases present in the base or training data
+- Sensitivity to prompting and conversation context
+- Reduced performance on tasks outside the fine-tuning distribution
+- Inconsistent reasoning or generation on difficult tasks
+- Potentially different behavior from the original Qwen3.5-0.8B model
+
+Performance should be evaluated on the specific tasks and domains for which the adapter is intended to be used.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users should evaluate Capable-1.0 before deploying it in production environments. Human oversight is recommended for consequential applications. Generated content should be independently verified when accuracy is important.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+Install the required libraries:
+
+```bash
+pip install -U transformers peft accelerate
+```
+
+Then load the base model and adapter:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model_name = "Qwen/Qwen3.5-0.8B"
+adapter_name = "weightix-labs/Capable-1.0"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    device_map="auto"
+)
+
+model = PeftModel.from_pretrained(
+    base_model,
+    adapter_name
+)
+
+prompt = "Hello! What can you help me with?"
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256
+)
+
+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. -->
+
+Capable-1.0 was fine-tuned using a training dataset selected by Weightix Labs. Specific dataset composition, preprocessing procedures, and dataset licensing information should be documented here when available.
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+Capable-1.0 uses Low-Rank Adaptation (LoRA) through the Hugging Face PEFT framework. LoRA fine-tunes a relatively small number of additional parameters rather than updating all parameters of the base model.
+
+#### Preprocessing [optional]
+
+Training examples were prepared for use with the Qwen3.5-0.8B model and its tokenizer. Additional preprocessing details are not currently documented.
+
+#### Training Hyperparameters
+
+- **Training regime:** [Not specified] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Fine-tuning method:** LoRA
+- **Base model:** Qwen/Qwen3.5-0.8B
+- **Framework:** PEFT
+- **PEFT version:** 0.20.0
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+Training hardware, duration, throughput, and checkpoint size have not been formally documented.
+
+## 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. -->
+
+A formal evaluation dataset has not yet been published for Capable-1.0.
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+Evaluation should consider factors such as:
+
+- General instruction following
+- Response quality
+- Factual accuracy
+- Reasoning performance
+- Coding performance, where applicable
+- Robustness to different prompts
+- Performance relative to the Qwen3.5-0.8B base model
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+Recommended metrics include task-specific accuracy, instruction-following quality, generation quality, and human evaluation. No official benchmark results are currently reported.
+
+### Results
+
+Formal benchmark results for Capable-1.0 are not currently available.
+
+#### Summary
+
+Capable-1.0 should be evaluated against the original Qwen3.5-0.8B model to determine the effect of the LoRA fine-tuning.
+
+## 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:** Not documented
+- **Hours used:** Not documented
+- **Cloud Provider:** Not documented
+- **Compute Region:** Not documented
+- **Carbon Emitted:** Not calculated
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+Capable-1.0 uses the architecture of the Qwen3.5-0.8B base model with a LoRA adapter trained using the PEFT framework. The objective of LoRA is to adapt the behavior of the base model by training a comparatively small set of low-rank parameters while leaving the original model weights largely unchanged.
+
+### Compute Infrastructure
+
+Training infrastructure details have not been formally documented.
+
+#### Hardware
+
+Not documented.
+
+#### Software
+
+- Transformers
+- PEFT 0.20.0
+- Python
+- PyTorch
+
+## 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:**
+
+```bibtex
+@misc{weightixlabs_capable_1_0,
+  author = {Weightix Labs},
+  title = {Capable-1.0},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/weightix-labs/Capable-1.0}
+}
+```
+
+**APA:**
+
+Weightix Labs. (2026). *Capable-1.0*. Hugging Face. https://huggingface.co/weightix-labs/Capable-1.0
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+- **LoRA:** Low-Rank Adaptation, a parameter-efficient fine-tuning method.
+- **PEFT:** Parameter-Efficient Fine-Tuning, a framework for adapting pretrained models using fewer trainable parameters.
+- **Adapter:** Additional learned parameters that modify the behavior of a pretrained base model.
+- **Base model:** The pretrained model on which the adapter is applied.
+
+## More Information [optional]
+
+Capable-1.0 is an experimental Weightix Labs model release focused on parameter-efficient adaptation of a small language model. For the underlying model, see the Qwen3.5-0.8B model card.
+
+## Model Card Authors [optional]
+
+Weightix Labs
+
+## Model Card Contact
+
+Weightix Labs
+
+### Framework versions
+
+- PEFT 0.20.0
+- Transformers: [Not specified]
+- PyTorch: [Not specified]