README.md

---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- base_model:adapter:codellama/CodeLlama-7b-hf
- lora
- transformers
datasets:
- darwinkernelpanic/luau-reasoning-normalized
pipeline_tag: text-generation
model-index:
- name: outputs/luau-codellama-h200-fast
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.13.0.dev0`
```yaml
base_model: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

# Keep full precision weights (fast on Hopper)
load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: llama3

datasets:
  - path: darwinkernelpanic/luau-reasoning-normalized
    type: chat_template
    conversation: llama3
    field_messages: messages
    add_generation_prompt: true

# Preprocessing workers (CPU). Fine as-is.
num_proc: 16

output_dir: ./outputs/luau-codellama-h200-fast

# ===== LoRA =====
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj

# ===== Precision =====
bf16: true
fp16: false
tf32: true

# ===== Sequence / batching =====
sequence_len: 4096
# Keep packing for throughput, but enable length grouping to cut padding
sample_packing: true
group_by_length: true

# Lower micro-batch a bit to kill peak VRAM while staying fast
micro_batch_size: 5
gradient_accumulation_steps: 1

# ===== Training =====
num_epochs: 3
optimizer: adamw_torch
learning_rate: 2e-4
lr_scheduler_type: cosine
warmup_steps: 100

train_on_inputs: false

# Turn on checkpointing — tiny speed hit, big memory win
gradient_checkpointing: true
gradient_clipping: 1.0

# ===== Dataloader =====
# Keep pin_memory, but avoid too many loader workers in Accelerate
dataloader_num_workers: 2
dataloader_pin_memory: true
# Optional: avoid insanely large host->device prefetch
# dataloader_prefetch_factor: 2

# ===== Logging / eval =====
logging_steps: 25
val_set_size: 0.05
# Reduce eval/save frequency to avoid spikes
eval_steps: 1000
save_strategy: steps
save_steps: 1000
save_total_limit: 3

seed: 42

# ===== DeepSpeed =====
# Off for single H200 — overhead not worth it for 7B
```

</details><br>

# outputs/luau-codellama-h200-fast

This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the darwinkernelpanic/luau-reasoning-normalized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4927
- Ppl: 1.6368
- Memory/max Active (gib): 19.1
- Memory/max Allocated (gib): 19.1
- Memory/device Reserved (gib): 139.06

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 3996

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Ppl    | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:------:|:----:|:---------------:|:------:|:------------:|:---------------:|:--------------:|
| No log        | 0      | 0    | 1.6888          | 5.4129 | 18.94        | 18.94           | 139.12         |
| 0.5511        | 0.7502 | 1000 | 0.5410          | 1.7177 | 19.1         | 19.1            | 139.02         |
| 0.5052        | 1.5004 | 2000 | 0.5064          | 1.6593 | 19.1         | 19.1            | 139.06         |
| 0.4733        | 2.2506 | 3000 | 0.4927          | 1.6368 | 19.1         | 19.1            | 139.06         |


### Framework versions

- PEFT 0.18.0
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1