--- base_model: Qwen/Qwen3.5-0.8B library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:Qwen/Qwen3.5-0.8B - lora - transformers - peft --- # Model Card for Capable-1.0 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 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] - **Repository:** https://huggingface.co/weightix-labs/Capable-1.0 - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use 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] 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 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 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 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 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 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] - **Fine-tuning method:** LoRA - **Base model:** Qwen/Qwen3.5-0.8B - **Framework:** PEFT - **PEFT version:** 0.20.0 #### Speeds, Sizes, Times [optional] Training hardware, duration, throughput, and checkpoint size have not been formally documented. ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data A formal evaluation dataset has not yet been published for Capable-1.0. #### Factors 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 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] [More Information Needed] ## Environmental Impact 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] **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] - **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]