RichardErkhov/hyokwan_-_hkcode_llama3_8b-gguf

Quantization made by Richard Erkhov.

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hkcode_llama3_8b - GGUF

• Model creator: https://huggingface.co/hyokwan/
• Original model: https://huggingface.co/hyokwan/hkcode_llama3_8b/

Name	Quant method	Size
hkcode_llama3_8b.Q2_K.gguf	Q2_K	2.96GB
hkcode_llama3_8b.IQ3_XS.gguf	IQ3_XS	3.28GB
hkcode_llama3_8b.IQ3_S.gguf	IQ3_S	3.43GB
hkcode_llama3_8b.Q3_K_S.gguf	Q3_K_S	3.41GB
hkcode_llama3_8b.IQ3_M.gguf	IQ3_M	3.52GB
hkcode_llama3_8b.Q3_K.gguf	Q3_K	3.74GB
hkcode_llama3_8b.Q3_K_M.gguf	Q3_K_M	3.74GB
hkcode_llama3_8b.Q3_K_L.gguf	Q3_K_L	4.03GB
hkcode_llama3_8b.IQ4_XS.gguf	IQ4_XS	4.18GB
hkcode_llama3_8b.Q4_0.gguf	Q4_0	4.34GB
hkcode_llama3_8b.IQ4_NL.gguf	IQ4_NL	4.38GB
hkcode_llama3_8b.Q4_K_S.gguf	Q4_K_S	4.37GB
hkcode_llama3_8b.Q4_K.gguf	Q4_K	4.58GB
hkcode_llama3_8b.Q4_K_M.gguf	Q4_K_M	4.58GB
hkcode_llama3_8b.Q4_1.gguf	Q4_1	4.78GB
hkcode_llama3_8b.Q5_0.gguf	Q5_0	5.21GB
hkcode_llama3_8b.Q5_K_S.gguf	Q5_K_S	5.21GB
hkcode_llama3_8b.Q5_K.gguf	Q5_K	5.34GB
hkcode_llama3_8b.Q5_K_M.gguf	Q5_K_M	5.34GB
hkcode_llama3_8b.Q5_1.gguf	Q5_1	5.65GB
hkcode_llama3_8b.Q6_K.gguf	Q6_K	6.14GB
hkcode_llama3_8b.Q8_0.gguf	Q8_0	7.95GB

Original model description:

license: mit datasets: - hyokwan/llama3data_hkcode language: - ko - en metrics: - accuracy library_name: transformers pipeline_tag: text-generation tags: - education - fintech

Model Details hyokwan/hkcode_llama3_8b

hyokwan/hkcode_llama3_8b is continued pretrained language model based on meta-llama/Meta-Llama-3-8B-Instruct

This model is trained with specific department of university (Korea Plytechnics Fintech) .

License: https://llama.meta.com/llama3/license

Intended Use TBD

How to use TBD

Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.

Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.

Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.

As part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.

Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.

Misuse

If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at https://llama.meta.com/llama3/use-policy/.

Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.

But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.

Please see the Responsible Use Guide available at http://llama.meta.com/responsible-use-guide

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license: mit