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--- |
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base_model: meta-llama/Llama-3.2-3B-Instruct |
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language: |
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- en |
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- de |
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- fr |
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- it |
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- pt |
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- hi |
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- es |
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- th |
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license: llama3.2 |
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pipeline_tag: text-generation |
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tags: |
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- facebook |
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- meta |
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- llama |
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- llama-3 |
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quantized_by: bartowski |
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extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ |
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\ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ |
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\ for use, reproduction, distribution and modification of the Llama Materials set\ |
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\ this Agreement. The courts of California shall have exclusive jurisdiction of\ |
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\ any dispute arising out of this Agreement. \n### Llama 3.2 Acceptable Use Policy\n\ |
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Meta is committed to promoting safe and fair use of its tools and features, including\ |
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\ Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy\ |
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#### Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\ |
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\ You agree you will not use, or allow others to use, Llama 3.2 to:\n1. Violate\ |
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\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ |
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\ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ |
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\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ |
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\ or harm to children, including the solicitation, creation, acquisition, or dissemination\ |
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\ of child exploitative content or failure to report Child Sexual Abuse Material\n\ |
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\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ |
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\ illegal distribution of information or materials to minors, including obscene\ |
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\ materials, or failure to employ legally required age-gating in connection with\ |
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\ such information or materials.\n 5. Sexual solicitation\n 6. Any\ |
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\ other criminal activity\n 1. Engage in, promote, incite, or facilitate the\ |
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\ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ |
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\ 2. Engage in, promote, incite, or facilitate discrimination or other unlawful\ |
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\ or harmful conduct in the provision of employment, employment benefits, credit,\ |
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\ Engage in the unauthorized or unlicensed practice of any profession including,\ |
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\ but not limited to, financial, legal, medical/health, or related professional\ |
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\ practices\n 4. Collect, process, disclose, generate, or infer private or sensitive\ |
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\ information about individuals, including information about individuals’ identity,\ |
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\ health, or demographic information, unless you have obtained the right to do so\ |
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\ in accordance with applicable law\n 5. Engage in or facilitate any action or\ |
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\ generate any content that infringes, misappropriates, or otherwise violates any\ |
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\ third-party rights, including the outputs or results of any products or services\ |
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\ overburden, interfere with or impair the proper working, integrity, operation\ |
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\ facilitate any action, to intentionally circumvent or remove usage restrictions\ |
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\ in, promote, incite, facilitate, or assist in the planning or development of activities\ |
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\ that present a risk of death or bodily harm to individuals, including use of Llama\ |
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\ 3.2 related to the following:\n 8. Military, warfare, nuclear industries or\ |
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\ applications, espionage, use for materials or activities that are subject to the\ |
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\ International Traffic Arms Regulations (ITAR) maintained by the United States\ |
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\ Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989\ |
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\ or the Chemical Weapons Convention Implementation Act of 1997\n 9. Guns and\ |
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\ illegal weapons (including weapon development)\n 10. Illegal drugs and regulated/controlled\ |
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\ substances\n 11. Operation of critical infrastructure, transportation technologies,\ |
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\ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\ |
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\ and eating disorders\n 13. Any content intended to incite or promote violence,\ |
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\ abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive\ |
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\ or mislead others, including use of Llama 3.2 related to the following:\n 14.\ |
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\ Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n\ |
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\ 15. Generating, promoting, or furthering defamatory content, including the\ |
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\ creation of defamatory statements, images, or other content\n 16. Generating,\ |
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\ promoting, or further distributing spam\n 17. Impersonating another individual\ |
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\ without consent, authorization, or legal right\n 18. Representing that the\ |
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\ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\ |
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\ false online engagement, including fake reviews and other means of fake online\ |
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\ engagement \n4. Fail to appropriately disclose to end users any known dangers\ |
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\ of your AI system 5. Interact with third party tools, models, or software designed\ |
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\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\ |
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\ that the outputs of such tools, models, or software are associated with Meta or\ |
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\ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\ |
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\ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\ |
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\ not apply to end users of a product or service that incorporates any such multimodal\ |
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\ models.\n\nPlease report any violation of this Policy, software “bug,” or other\ |
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\ problems that could lead to a violation of this Policy through one of the following\ |
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\ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\ |
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* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\ |
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* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\ |
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* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\ |
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\ 3.2: [email protected]" |
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extra_gated_fields: |
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First Name: text |
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Last Name: text |
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Date of birth: date_picker |
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Country: country |
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Affiliation: text |
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Job title: |
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type: select |
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options: |
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- Student |
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geo: ip_location |
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? By clicking Submit below I accept the terms of the license and acknowledge that |
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the information I provide will be collected stored processed and shared in accordance |
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extra_gated_button_content: Submit |
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--- |
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|
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## Llamacpp imatrix Quantizations of Llama-3.2-3B-Instruct |
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Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3821">b3821</a> for quantization. |
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Original model: https://huggingface.co./meta-llama/Llama-3.2-3B-Instruct |
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All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) |
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Run them in [LM Studio](https://lmstudio.ai/) |
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## Prompt format |
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``` |
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<|begin_of_text|><|start_header_id|>system<|end_header_id|> |
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Cutting Knowledge Date: December 2023 |
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Today Date: 26 Jul 2024 |
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> |
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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``` |
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## Download a file (not the whole branch) from below: |
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| Filename | Quant type | File Size | Split | Description | |
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| -------- | ---------- | --------- | ----- | ----------- | |
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| [Llama-3.2-3B-Instruct-f16.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-f16.gguf) | f16 | 6.43GB | false | Full F16 weights. | |
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| [Llama-3.2-3B-Instruct-Q8_0.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q8_0.gguf) | Q8_0 | 3.42GB | false | Extremely high quality, generally unneeded but max available quant. | |
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| [Llama-3.2-3B-Instruct-Q6_K_L.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q6_K_L.gguf) | Q6_K_L | 2.74GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q6_K.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q6_K.gguf) | Q6_K | 2.64GB | false | Very high quality, near perfect, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q5_K_L.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q5_K_L.gguf) | Q5_K_L | 2.42GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q5_K_M.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q5_K_M.gguf) | Q5_K_M | 2.32GB | false | High quality, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q5_K_S.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q5_K_S.gguf) | Q5_K_S | 2.27GB | false | High quality, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q4_K_L.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q4_K_L.gguf) | Q4_K_L | 2.11GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q4_K_M.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf) | Q4_K_M | 2.02GB | false | Good quality, default size for must use cases, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q4_K_S.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q4_K_S.gguf) | Q4_K_S | 1.93GB | false | Slightly lower quality with more space savings, *recommended*. | |
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| [Llama-3.2-3B-Instruct-Q4_0_8_8.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q4_0_8_8.gguf) | Q4_0_8_8 | 1.92GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). | |
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| [Llama-3.2-3B-Instruct-Q4_0_4_8.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q4_0_4_8.gguf) | Q4_0_4_8 | 1.92GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). | |
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| [Llama-3.2-3B-Instruct-Q4_0_4_4.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q4_0_4_4.gguf) | Q4_0_4_4 | 1.92GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. | |
|
| [Llama-3.2-3B-Instruct-Q4_0.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q4_0.gguf) | Q4_0 | 1.92GB | false | Legacy format, generally not worth using over similarly sized formats | |
|
| [Llama-3.2-3B-Instruct-Q3_K_XL.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q3_K_XL.gguf) | Q3_K_XL | 1.91GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. | |
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| [Llama-3.2-3B-Instruct-IQ4_XS.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-IQ4_XS.gguf) | IQ4_XS | 1.83GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | |
|
| [Llama-3.2-3B-Instruct-Q3_K_L.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-Q3_K_L.gguf) | Q3_K_L | 1.82GB | false | Lower quality but usable, good for low RAM availability. | |
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| [Llama-3.2-3B-Instruct-IQ3_M.gguf](https://huggingface.co./bartowski/Llama-3.2-3B-Instruct-GGUF/blob/main/Llama-3.2-3B-Instruct-IQ3_M.gguf) | IQ3_M | 1.60GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. | |
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|
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## Embed/output weights |
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Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. |
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Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. |
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Thanks! |
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## Downloading using huggingface-cli |
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First, make sure you have hugginface-cli installed: |
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``` |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, you can target the specific file you want: |
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``` |
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huggingface-cli download bartowski/Llama-3.2-3B-Instruct-GGUF --include "Llama-3.2-3B-Instruct-Q4_K_M.gguf" --local-dir ./ |
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``` |
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If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: |
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``` |
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huggingface-cli download bartowski/Llama-3.2-3B-Instruct-GGUF --include "Llama-3.2-3B-Instruct-Q8_0/*" --local-dir ./ |
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``` |
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You can either specify a new local-dir (Llama-3.2-3B-Instruct-Q8_0) or download them all in place (./) |
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## Q4_0_X_X |
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These are *NOT* for Metal (Apple) offloading, only ARM chips. |
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If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660) |
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To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!). |
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## Which file should I choose? |
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A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) |
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The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. |
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If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. |
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If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. |
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Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. |
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If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. |
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If you want to get more into the weeds, you can check out this extremely useful feature chart: |
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[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) |
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But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. |
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These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. |
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The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. |
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## Credits |
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Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset |
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Thank you ZeroWw for the inspiration to experiment with embed/output |
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Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
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