---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: transformers
tags:
- rust
- rust-programming
- code-generation
- qlora
- lora
- peft
- llama
- meta-llama-3.1
- instruction-tuned
- text-generation
- sigilderg
datasets:
- ammarnasr/the-stack-rust-clean
license: mit
language:
- en
pipeline_tag: text-generation
model-index:
- name: llama8b-rust-qlora-phase1-step-2000
  results:
  - task:
      type: text-generation
    dataset:
      name: rust-code-evaluation
      type: code-generation
    metrics:
    - name: Compilation Rate
      type: compilation_rate
      value: 0.4545
    - name: Clippy Warnings (avg)
      type: clippy_warnings
      value: 0.0
    - name: Idiomatic Score
      type: idiomatic_score
      value: 0.1523
    - name: Documentation Rate
      type: doc_comment_rate
      value: 0.0
    - name: Avg Functions
      type: avg_functions
      value: 1.38
    - name: Avg Structs
      type: avg_structs
      value: 0.3091
    - name: Avg Traits
      type: avg_traits
      value: 0.1091
    - name: Test Rate
      type: test_rate
      value: 0.0
    - name: Prompt Match Score
      type: prompt_match
      value: 0.1746
    source:
      name: SigilDERG Evaluation
      url: https://github.com/Superuser666-Sigil/SigilDERG-Finetuner
---

# llama8b-rust-qlora-phase1 (checkpoint 2000 / 12000)

> This card describes **checkpoint 2000** of the Phase 1 Rust QLoRA run.  
> For the full training plan, governance details, and final recommended checkpoints, see the **root model card** in the repository.

## Model Description

This is a QLoRA fine-tuned version of **meta-llama/Meta-Llama-3.1-8B-Instruct** specifically trained on Rust code. The model uses 4-bit quantization with LoRA (Low-Rank Adaptation) adapters for efficient training and inference.

The primary modality is **Rust code with English comments and explanations**.

## Training Details

### Training Configuration

- **Base Model**: `meta-llama/Meta-Llama-3.1-8B-Instruct`
- **Training Steps**: 2,000 / 12,000 (this checkpoint)
- **Learning Rate**: 9.468007641482094e-05 (peak)
- **Batch Size**: 16 × 4 (effective: 64)
- **Sequence Length**: 4096
- **Optimizer**: `paged_adamw_8bit`
- **LR Scheduler**: cosine
- **Warmup Steps**: 250
- **Weight Decay**: 0.0
- **Gradient Checkpointing**: True
- **BF16**: True

### LoRA Configuration

- **Rank (r)**: 16
- **Alpha**: 16
- **Dropout**: 0.05
- **Target Modules**: `q_proj, k_proj, v_proj, o_proj, up_proj, down_proj, gate_proj`

### Quantization

- **Method**: 4-bit NF4 (BitsAndBytes)
- **Compute Dtype**: bfloat16
- **Double Quantization**: True

### Datasets

The model was trained on the following dataset:

- `ammarnasr/the-stack-rust-clean`

**Dataset Configuration:**

- **Min Length**: 64
- **Max Length**: 200000
- **Exclude Tests**: True
- **Exclude Examples**: False
- **Exclude Benches**: True
- **Prefer Idiomatic**: False
- **Prefer Documented**: False

## Training Metrics

Latest logged training metrics around this checkpoint:

- **loss**: 0.764700
- **grad_norm**: 0.125130
- **learning_rate**: 0.000095
- **entropy**: 0.780214
- **num_tokens**: 352709516
- **mean_token_accuracy**: 0.816762
- **epoch**: 0.614862
- **log_step**: 1,992
- **checkpoint_step**: 2,000
- **step**: 1,992

(Logging is done every few steps, so `log_step` reflects the nearest logged step to the checkpoint.)

## Evaluation Results

- **Compilation Rate**: 45.45% (55 samples evaluated)
- **Average Clippy Warnings**: 0.00
- **Idiomatic Score**: 0.1523
- **Documentation Rate**: 0.00%
- **Test Rate**: 0.00%

**Functionality Coverage:**
- Average Functions: 1.38
- Average Structs: 0.31
- Average Traits: 0.11
- Average Impls: 0.16

**Detailed Evaluation Data:**
- [Metrics (JSONL)](https://huggingface.co/Superuser666-Sigil/Llama-3.1-8B-Instruct-Rust-QLora/blob/main/checkpoint-2000/metrics.jsonl) - Full evaluation metrics
- [Error Logs (JSONL)](https://huggingface.co/Superuser666-Sigil/Llama-3.1-8B-Instruct-Rust-QLora/blob/main/checkpoint-2000/errors.jsonl) - Compilation and runtime errors

*Evaluation completed: 2025-11-20T00:41:47.995913*
## Governance and Intended Use

This checkpoint is part of the **SigilDERG** ecosystem and follows **Rule Zero** principles.

- Intended primarily for **Rust code generation, explanation, refactoring, and review**.
- Not intended as a general-purpose advisor for medical, legal, financial, or other high-stakes domains.

## Usage

### Loading the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "out/llama8b-rust-qlora-phase1/checkpoint-2000")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
```

### Generation

```python
# Format prompt for instruct model
messages = [
    {"role": "system", "content": "You are a helpful Rust programming assistant."},
    {"role": "user", "content": "Write a function that calculates fibonacci numbers"}
]

# Apply chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Limitations

- This model is fine-tuned specifically for Rust code generation and may not perform well on other programming languages or general text tasks.
- The model inherits any limitations and biases from the base model.
- Generated code should always be reviewed and tested before use in production.

## Citation

If you use this model, please cite:

```bibtex
@software{sigilderg_finetuner,
  title = {SigilDERG Rust Code Fine-tuned Model},
  author = {Superuser666-Sigil/Dave Tofflemire},
  year = {2025},
  url = {https://github.com/Superuser666-Sigil/SigilDERG-Finetuner}
}
```

## License

This model is released under the MIT License.