README.md

---
base_model: Qwen/Qwen3.5-9B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3_5
license: apache-2.0
language:
- en
---

![fred-9b](https://cdn-uploads.huggingface.co/production/uploads/66e629438ce4fc3270d9a910/MiJDlfdjO9XX5PAUQ2UJ4.png)

# Fred-9B

**Fred-9B** is a fine-tuned 9B-parameter language model based on **Qwen
3.5 9B** and finetunned with this dataset, https://huggingface.co/datasets/CrowdMind/caveman-fred.

Fred-9B was trained with **LoRA/QLoRA-style parameter-efficient
fine-tuning** and is intended as an experimental conversational and
reasoning model.

> **Note:** Fred-9B is a fine-tune of Qwen 3.5 9B, not a model trained
> from scratch.

## Model Details

  Property                 Value
  ------------------------ -----------------
  Model name               **Fred-9B**
  Base model               **Qwen 3.5 9B**
  Fine-tuning method       **LoRA**
  LoRA rank                128
  LoRA alpha               256
  LoRA dropout             0.05
  Batch size               4
  Context length           2,048 tokens
  Learning rate            2e-4
  Optimizer                AdamW 8-bit
  Warmup steps             30
  Maximum training steps   300
  Training tokens          \~3.21M
  Final training loss      0.0833
  Final gradient norm      0.470
  Training time            \~1h 33m

## Training

Fred-9B was fine-tuned for **300 steps**. The training run used a
2,048-token context length and a batch size of 4.

The learning-rate schedule used a short warmup followed by decay across
the training run.

### Final Training Metrics

-   **Training loss:** `0.0833`
-   **Learning rate:** `7.41e-7`
-   **Gradient norm:** `0.470`
-   **Tokens processed:** `3,214,912`
-   **Steps:** `300 / 300`
-   **Training time:** `1h 32m 45s`

No evaluation dataset was configured for this training run, so an
evaluation loss was not recorded.

## Example Behavior

One of the training checkpoints was tested with simple arithmetic and
order-of-operations questions.

### Example 1

**Prompt**

``` text
Please solve this for me i need help: 2+1*3(1+1)+3
```

**Fred-9B**

``` text
The final answer is 11.
```

The model correctly handles the parentheses and multiplication before
addition:

``` text
1 + 1 = 2
3 × 2 = 6
2 + 6 + 3 = 11
```

![3](https://cdn-uploads.huggingface.co/production/uploads/66e629438ce4fc3270d9a910/jRRZRtqjcDQOd9lkCJKJ0.png)

### Example 2

**Prompt**

``` text
Please solve this for me i need help: 3+1+1*4(1+1)+3*1
```

**Fred-9B**

``` text
The final answer is 15.
```

The model breaks the expression down using standard order of operations
and reaches the correct result.

![4](https://cdn-uploads.huggingface.co/production/uploads/66e629438ce4fc3270d9a910/eMQH7YwKhwjeyMiiduY6p.png)

## Intended Use

Fred-9B is intended for:

-   Conversational AI experiments
-   Local LLM experimentation
-   Coding and reasoning experiments
-   Mathematical/problem-solving prompts
-   Testing fine-tuning workflows
-   Personal/local AI assistants

Because Fred-9B is an experimental fine-tune, its behavior may vary
substantially depending on the prompt and task.

## Limitations

Fred-9B has several important limitations:

-   The model has only been fine-tuned for a relatively small number of
    steps.
-   The reported run did not use an evaluation dataset.
-   The examples shown above are not a comprehensive benchmark.
-   Correct answers on simple arithmetic do not guarantee reliable
    mathematical reasoning.
-   The model may hallucinate or produce incorrect information.
-   Fine-tuning can alter some of the capabilities and behaviors of the
    original Qwen model.
-   Performance will depend on the inference engine, quantization,
    prompt format, and hardware.

For serious evaluation, use a held-out evaluation dataset and
established benchmarks rather than relying only on training loss.

## Recommended Evaluation

For future versions of Fred, consider evaluating:

-   General instruction following
-   Mathematical reasoning
-   Coding
-   Knowledge/retrieval
-   Multi-step reasoning
-   Hallucination resistance
-   Long-context behavior
-   Conversational consistency

A separate validation/evaluation dataset should be configured for future
training runs so that training loss can be compared against evaluation
loss.

## Running Fred-9B

Fred-9B can be used with an inference stack that supports the model's
exported format.

If the model is exported to GGUF, it can be used with compatible local
inference tools such as `llama.cpp`-based applications.

Example concept:

``` bash
# Example only — replace with the actual exported model filename
./llama-cli -m Fred-9B.gguf
```

The exact command depends on the exported model format and inference
framework.

## Quantization

For local deployment, quantized versions can significantly reduce memory
requirements.

Possible variants include:

-   4-bit
-   5-bit
-   6-bit
-   8-bit
-   Full/half precision

The best choice depends on available VRAM/RAM and the desired balance
between speed, memory usage, and model quality.

## Training Configuration

The training run used approximately the following configuration:

``` yaml
model: Qwen 3.5 9B

method: LoRA

training:
  batch_size: 4
  learning_rate: 0.0002
  optimizer: AdamW 8-bit
  context_length: 2048
  warmup_steps: 30
  max_steps: 300

lora:
  rank: 128
  alpha: 256
  dropout: 0.05
  variant: lora
```

![1](https://cdn-uploads.huggingface.co/production/uploads/66e629438ce4fc3270d9a910/Q537gegY5Tjqr7CA345C6.png)

## Acknowledgements

Fred-9B is based on **Qwen 3.5 9B**.

All original model licensing, attribution, usage restrictions, and terms
from the Qwen base model remain important when using or redistributing
Fred-9B. Check the official Qwen model license and documentation before
publishing or deploying the model.

## Project Status

**Experimental --- first training run**

Fred-9B is currently an experimental fine-tune. Future versions can
improve the dataset, evaluation process, training duration, instruction
formatting, and inference quality.

------------------------------------------------------------------------

**Fred-9B** --- a personal 9B fine-tune built on Qwen 3.5 9B.