--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B tags: - generated_from_trainer datasets: - trl-lib/tldr --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl) # Llama-3.1-8B-tldr ## Model Overview - **Model Architecture:** LlamaForCausalLM - **Input:** Text - **Output:** Text - **Release Date:** 05/29/2025 - **Version:** 1.0 - **Intended Use Cases:** This model is finetuned to summarize text in the style of Reddit posts. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. - **Model Developers:** Red Hat (Neural Magic) This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the [trl-lib/tldr](https://huggingface.co/datasets/trl-lib/tldr) dataset. This model obtains 0.366 BERTScore on the test set of trl-lib/tldr. ## Deployment This model can be deployed efficiently using [vLLM](https://docs.vllm.ai/en/latest/), as shown in the example below. Run the following command to start the vLLM server: ```bash vllm serve RedHatAI/Llama-3.1-8B-tldr ``` Once your server is started, you can query the model using the OpenAI API: ```python from openai import OpenAI openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) post=""" SUBREDDIT: r/AI TITLE: Training sparse LLMs POST: Now you can use the llm-compressor integration to axolotl to train sparse LLMs! It's super easy to use. See the example in https://huggingface.co/RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4. And there's more. You can run 2:4 sparse models on vLLM and get significant speedupts on Hopper GPUs! """ prompt = f"Give a TL;DR of the following Reddit post.\n<|user|>{post}\nTL;DR:\n<|assistant|>\n" completion = client.completions.create( model="RedHatAI/Llama-3.1-8B-tldr", prompt=prompt, max_tokens=256, ) print("Completion result:", completion) ``` ## Training
See axolotl config axolotl version: `0.10.0.dev0` ```yaml base_model: meta-llama/Llama-3.1-8B load_in_8bit: false load_in_4bit: false strict: false datasets: - path: trl-lib/tldr type: system_prompt: "Give a TL;DR of the following Reddit post." field_system: system field_instruction: prompt field_output: completion format: "<|user|>\n{instruction}\n<|assistant|>\n" no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n" split: train sequence_len: 4096 sample_packing: true pad_to_sequence_len: true eval_sample_packing: true torch.compile: true gradient_accumulation_steps: 1 micro_batch_size: 4 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 1e-5 max_grad_norm: 1 gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false train_on_inputs: false bf16: auto fp16: tf32: false early_stopping_patience: resume_from_checkpoint: logging_steps: 1 flash_attention: true warmup_ratio: 0.05 evals_per_epoch: 4 val_set_size: 0.05 save_strategy: "best" save_total_limit: 1 metric_for_best_model: "loss" debug: deepspeed: weight_decay: 0.0 special_tokens: pad_token: "<|end_of_text|>" seed: 0 plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_glu_activation: true liger_layer_norm: true liger_fused_linear_cross_entropy: true ```

## Training
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 0 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Use adamw_bnb_8bit 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: 49 - num_epochs: 3.0

Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.2572 | 0.0031 | 1 | 2.2288 | | 1.7865 | 0.2508 | 82 | 1.7680 | | 1.7257 | 0.5015 | 164 | 1.7567 | | 1.7343 | 0.7523 | 246 | 1.7489 | | 1.7688 | 1.0031 | 328 | 1.7441 | | 1.6822 | 1.2538 | 410 | 1.7493 | | 1.6085 | 1.5046 | 492 | 1.7480 | | 1.6627 | 1.7554 | 574 | 1.7444 | | 1.729 | 2.0061 | 656 | 1.7426 | | 1.6149 | 2.2569 | 738 | 1.7540 | | 1.6002 | 2.5076 | 820 | 1.7537 | | 1.6573 | 2.7584 | 902 | 1.7526 |

Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1

## Evaluation The model was evaluated on the test split of [trl-lib/tldr](https://huggingface.co/datasets/trl-lib/tldr) using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/tldr) (tldr branch). One can reproduce these results by using the following command: ```bash lm_eval --model vllm --model_args "pretrained=RedHatAI/Llama-3.1-8B-tldr,dtype=auto,add_bos_token=True" --batch-size auto --tasks tldr ```
Metric Llama-3.1-8B-Instruct Llama-3.1-8B-tldr Sparse-Llama-3.1-8B-tldr-2of4
(this model)
BERTScore -0.230 0.366 0.366
ROUGE-1 0.059 0.362 0.357
ROUGE-2 0.018 0.144 0.141
ROUGE-Lsum 0.051 0.306 0.304
## Inference Performance We evaluated the inference performance of this model using the first 1,000 samples from the training set of the [trl-lib/tldr](https://huggingface.co/datasets/trl-lib/tldr) dataset. Benchmarking was conducted with [vLLM](https://docs.vllm.ai/en/latest/) version `0.9.0.1` and [GuideLLM](https://github.com/neuralmagic/guidellm) version `0.2.1`. The figure below presents the **mean end-to-end latency per request** across varying request rates. Results are shown for this model, as well as two variants: - **Dense-quantized:** [Llama-3.1-8B-tldr-FP8-dynamic](https://huggingface.co/RedHatAI/Llama-3.1-8B-tldr-FP8-dynamic) - **Sparse-quantized:** [Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic](https://huggingface.co/RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic) ![Latency](./inference_performance/latency.png)
Reproduction instructions To replicate the benchmark: 1. Generate a JSON file containing the first 1,000 training samples: ```python from datasets import load_dataset ds = load_dataset("trl-lib/tldr", split="train").take(1000) ds.to_json("tldr_1000.json") ``` 2. Start a vLLM server using your target model: ```bash vllm serve RedHatAI/Llama-3.1-8B-tldr ``` 3. Run the benchmark with GuideLLM: ``` GUIDELLM__OPENAI__MAX_OUTPUT_TOKENS=128 guidellm benchmark --target "http://localhost:8000" --rate-type sweep --data tldr_1000.json ``` > The average output length is approximately 30 tokens per sample. We capped the generation at 128 tokens to reduce performance skew from rare, unusually verbose completions.