Scadiva/nd-solitude-7b-adapter

Qwen2.5-7B-Instruct — Fine-Tuned LoRA Adapter

Model Details

Model Description

This model is a parameter-efficient fine-tuning (PEFT) adapter built on top of Qwen/Qwen2.5-7B-Instruct. It uses the LoRA (Low-Rank Adaptation) approach for supervised fine-tuning (SFT), allowing the base model to be adapted without updating all of its parameters.

The repository provides the adapter for use with the compatible base model and the Hugging Face Transformers and PEFT ecosystem.

This release should be treated as an adapter rather than a standalone, fully merged model. Its behavior depends on the base model, the fine-tuning data, and the training configuration.

• Developed by: Soham Mukherjee
• Model type: LoRA adapter for a causal language model
• Base model: Qwen/Qwen2.5-7B-Instruct
• Fine-tuning method: Supervised fine-tuning (SFT) with LoRA
• Language(s): Inherited from the base model; fine-tuning language coverage has not been independently documented.
• License: Adapter-specific licensing has not been specified. Review the base model and training-data licenses before redistribution or commercial use.
• Framework: Hugging Face Transformers and PEFT
• PEFT version: 0.21.2

Model Sources

• Base model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
• Adapter repository: Add the URL of this model repository.
• Training code: https://github.com/Asterioxer/Neurosolit-salt

Uses

Direct Use

The adapter can be loaded alongside its compatible base model for text generation and experimentation with the behavior learned during fine-tuning.

Potential uses include:

• Experimenting with parameter-efficient fine-tuning of instruction-following language models.
• Comparing a fine-tuned adapter with the original base model.
• Evaluating model responses on task-specific prompts.
• Integrating the model into research prototypes and local inference workflows.

The exact task specialization and expected improvements should be documented once the training dataset and evaluation results are available.

Downstream Use

The adapter may be incorporated into applications that support Hugging Face PEFT models, including local inference and research workflows.

Before production use, downstream developers should independently assess response quality, latency, memory requirements, robustness, and suitability for their intended application.

Out-of-Scope Use

This model has not been validated for:

• Medical, legal, financial, or other high-stakes decision-making.
• Making consequential decisions about individuals.
• Generating authoritative information without independent verification.
• Safety-critical or fully autonomous systems.
• Applications requiring guaranteed factual accuracy or consistent behavior.

Bias, Risks, and Limitations

This adapter inherits many of the capabilities and limitations of Qwen2.5-7B-Instruct and may introduce additional behavior associated with its fine-tuning data.

Known considerations include:

• Hallucinations: Responses may contain inaccurate or fabricated information.
• Bias: The base model and fine-tuning data may reflect social, cultural, or representational biases.
• Generalization: Performance may vary on prompts and tasks not represented in the training data.
• Training-data uncertainty: The dataset composition, provenance, and filtering details have not been provided in this model card.
• Evaluation limitations: No independently verified benchmark results or quantitative improvement claims are reported here.
• Hardware requirements: The 7B-parameter base model can require substantial memory. Actual requirements depend on quantization, inference framework, context length, and runtime configuration.

Recommendations

Users should validate outputs before relying on them, especially in consequential settings. Evaluate the adapter against the original base model using representative prompts and a held-out test set.

For responsible deployment:

• Inspect the training data and its licensing.
• Test for bias, unsafe responses, and regressions.
• Record the inference configuration when comparing results.
• Avoid presenting unmeasured improvements as established performance gains.

How to Get Started with the Model

Install the required libraries:

pip install torch transformers peft accelerate

Load the base model and adapter using the following example. Replace YOUR_USERNAME/YOUR_ADAPTER_REPO with the actual Hugging Face repository ID.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
ADAPTER_REPO = "YOUR_USERNAME/YOUR_ADAPTER_REPO"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype="auto",
    device_map="auto",
)

model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_REPO,
)

messages = [
    {
        "role": "user",
        "content": "Explain how LoRA fine-tuning works."
    }
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(
    prompt,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
    )

new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Note: The example assumes that the repository contains a compatible PEFT adapter and its configuration files. The base model must be accessible, and the available system memory must be sufficient for the selected loading configuration.

Training Details

Training Data

The training dataset name, source, size, composition, licensing, and preprocessing details have not been specified in this card.

Before publishing a final version, document:

• The source and purpose of the training data.
• The number of training and validation examples.
• Data filtering, deduplication, and quality-control procedures.
• Any synthetic-data generation or transformations.
• Dataset licensing and privacy considerations.

Do not include private, confidential, or unauthorized data in the training set.

Training Procedure

The model is identified as a LoRA adapter fine-tuned using supervised fine-tuning (SFT). The exact training run configuration should be verified against the saved training configuration and logs before publication.

Preprocessing

Preprocessing details have not been reported. The final documentation should specify how prompts and responses were formatted, whether the base model's chat template was used, and how examples were filtered or tokenized.

Training Hyperparameters

• Training method: Supervised fine-tuning (SFT)
• Adaptation method: LoRA
• Base model: Qwen/Qwen2.5-7B-Instruct
• PEFT version: 0.21.2
• LoRA rank and alpha: Not reported
• Learning rate and scheduler: Not reported
• Batch size and gradient accumulation: Not reported
• Number of epochs and training steps: Not reported
• Training precision: Not reported
• Random seed: Not reported

These fields should be completed from the actual training configuration rather than inferred from the model architecture.

Speeds, Sizes, and Times

Training duration, peak memory usage, throughput, adapter size, and checkpoint size have not been reported.

Evaluation

Testing Data, Factors, and Metrics

Testing Data

The evaluation dataset and test-set size have not been specified.

Factors

Future evaluations should consider:

• Prompt complexity and task type.
• In-domain versus out-of-domain prompts.
• Response correctness and instruction following.
• Robustness to ambiguous or adversarial inputs.
• Performance relative to the unmodified base model.

Metrics

No quantitative evaluation metrics are reported in this card.

Recommended metrics include task-specific accuracy, human-rated response quality, instruction-following success, and inference latency. Any reported metrics should include the test methodology, sample size, and comparison baseline.

Results

No verified quantitative results are currently reported.

Summary

This release provides a Qwen2.5-7B-Instruct LoRA/SFT adapter intended for experimentation and evaluation. Its task-specific strengths and any improvement over the base model should be established through reproducible testing before being claimed.

Environmental Impact

The energy consumption and carbon emissions associated with training have not been measured or reported.

• Hardware type: Not reported
• Hours used: Not reported
• Cloud provider: Not reported
• Compute region: Not reported
• Carbon emitted: Not measured

Carbon emissions may be estimated using the Machine Learning Impact calculator, provided that the necessary compute and energy information is available.

Technical Specifications

Model Architecture and Objective

• Architecture: Causal language model, inherited from Qwen2.5-7B-Instruct
• Objective: Autoregressive text generation
• Fine-tuning approach: LoRA-based supervised fine-tuning
• Adapter framework: Hugging Face PEFT
• Base-model parameters: Approximately 7 billion, as indicated by the model designation

Compute Infrastructure

Hardware

The training hardware and configuration for the published adapter have not been confirmed in this card. Training and inference requirements should be documented separately because they can differ significantly.

Software

• PEFT: 0.21.2
• Transformers: Version not reported
• PyTorch: Version not reported
• TRL: Version not reported
• CUDA: Version not reported

Citation

No associated paper or formal publication has been specified.

If this adapter is used in research or derivative work, cite the base model and provide a link to this adapter repository.

More Information

This model card documents the model's known identity and fine-tuning approach while distinguishing verified information from details that still need to be supplied. Training logs, dataset documentation, and reproducible evaluation results should be added as they become available.

Model Card Authors

Soham Mukherjee

Model Card Contact

Use the contact information or discussion channel associated with the model repository.

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