Llama 3+
Collection
Meta's open Llama models • 4 items • Updated
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)GGUF models for llama3.java
Pure .gguf Q4_0 and Q8_0 quantizations of Llama 3 8B instruct, ready to consume by llama3.java.
In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the output.weights tensor is quantized with Q6_K, instead of Q4_0.
A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the quantize utility from llama.cpp as follows:
./quantize --pure ./Meta-Llama-3-8B-Instruct-F32.gguf ./Meta-Llama-3-8B-Instruct-Q4_0.gguf Q4_0
Meta-Llama-3-8B-Instruct-GGUF
- This is GGUF quantized version of meta-llama/Meta-Llama-3-8B-Instruct created using llama.cpp
- Re-uploaded with new end token
Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
Model developers Meta
Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
Input Models input text only.
Output Models generate text and code only.
Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| Training Data | Params | Context length | GQA | Token count | Knowledge cutoff | |
| Llama 3 | A new mix of publicly available online data. | 8B | 8k | Yes | 15T+ | March, 2023 |
| 70B | 8k | Yes | December, 2023 |
Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
Model Release Date April 18, 2024.
Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
License A custom commercial license is available at: https://llama.meta.com/llama3/license
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go here.
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mukel/Meta-Llama-3-8B-Instruct-GGUF", filename="", )