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weightix-labs/Capable-1.0

sourceHugging Faceupdated 25d agoView on Hugging Face
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Model Card

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`, 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

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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]

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  • —Repository: https://huggingface.co/weightix-labs/Capable-1.0
  • —Paper [optional]: [More Information Needed]
  • —Demo [optional]: [More Information Needed]

Uses

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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

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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 presented in Lacoste et al. (2019).

  • —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]