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Model Free Nvfp4a16 E2e

The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started...

TipoText modelVersãomodelUnratednota

Fonte do modelo

Tipo
Text model
Tamanho do arquivo
770 MB
Versão
vmodel
Atualizado
29 de out. de 2026
Downloads
303
Fonte
Hugging Face

Descrição da fonte

The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens . With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.

Fontes

1 fonte
Hugging FacePrimárioVersão ebff91b3e7096b851e883a908e4a0a2ca6c82553
huggingface.co/nm-testing/model_free_nvfp4a16-e2e/tree/ebff91b3e7096b851e883a908e4a0a2ca6c82553

Verificado 29 de out.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 770 MB · SHA-256 55eb47c25d44…ae09 · Hugging Face

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Trechos de fonte

3 trechos

--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en widget: - example_title: Fibonacci (Python) messages: - role: system content: You are a chatbot who can help code! - role: user content: Write me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI. --- <div align="center"> # TinyLlama-1.1B </div> https://github.com/jzhang38/TinyLlama The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### This Model This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4." #### How to use You will need the transformers>=4.34 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenize...

Source context: 303 downloads · 0 likes · Repo nm-testing/model_free_nvfp4a16-e2e

Source context: 239 downloads · 0 likes · Repo nm-testing/model_free_nvfp4a16-e2e