inferenceillusionist/euryale-1.3-longlora-70b-rope8-32k-imat-gguf IQ2_XXS GGUF - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.
inferenceillusionist/euryale-1.3-longlora-70b-rope8-32k-imat-gguf overview
Special request. Quantized from fp16 with love. Please note I have not tested context to the full 32k, but these quants have all passed the standard suite of coherence and KL-divergence benchmark tests. Any feedback is welcomed. * Quantizations made possible using .imatrix file from this repo (special thanks to ikawrakow again) For a brief rundown of iMatrix quant performance please see this PR All quants are verified working prior to uploading to repo for your safety and convenience. Importance matrix quantizations are a work in progress, IQ3 and above is recommended for best results. Tip: Pick a size that can fit in your GPU while still allowing some room for context for best speed. You may need to pad this further depending on if you are running image gen or TTS as well. Original model card can be found here
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ1_M.gguf | GGUF | IQ1_M | 14.85 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ2_M.gguf | GGUF | IQ2_M | 21.64 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ2_S.gguf | GGUF | IQ2_S | 19.89 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ2_XS.gguf | GGUF | IQ2_XS | 18.94 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ2_XXS.gguf | GGUF | IQ2_XXS | 17.03 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ3_M.gguf | GGUF | IQ3_M | 28.82 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ3_S.gguf | GGUF | IQ3_S | 27.86 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ3_XS.gguf | GGUF | IQ3_XS | 26.37 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ3_XXS.gguf | GGUF | IQ3_XXS | 24.76 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-IQ4_XS.gguf | GGUF | IQ4_XS | 34.30 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-Q2_K.gguf | GGUF | Q2_K | 23.71 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-Q3_K_M.gguf | GGUF | Q3_K_M | 30.99 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-Q4_K_M.gguf | GGUF | Q4_K_M | 38.58 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-Q4_K_S.gguf | GGUF | Q4_K_S | 36.55 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-Q5_K_M.gguf | GGUF | Q5_K_M | 45.41 GB | Download |
| Euryale-1.3-longLORA-70b-rope8-32k-iMat-Q5_K_S.gguf | GGUF | Q5_K_S | 44.20 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
{
"metadata": {},
"card_data": {
"tags": [
"merge",
"gguf",
"storywriting",
"text adventure",
"iMat"
],
"frontmatter": {
"tags": [
"merge",
"gguf",
"storywriting",
"text adventure",
"iMat"
]
},
"hero_image_url": "https://i.imgur.com/P68dXux.png",
"summary": "Special request. Quantized from fp16 with love. Please note I have not tested context to the full 32k, but these quants have all passed the standard suite of coherence and KL-divergence benchmark tests. Any feedback is welcomed. * Quantizations made possible using .imatrix file from this repo (special thanks to ikawrakow again) For a brief rundown of iMatrix quant performance please see this PR All quants are verified working prior to uploading to repo for your safety and convenience. Importance matrix quantizations are a work in progress, IQ3 and above is recommended for best results. Tip: Pick a size that can fit in your GPU while still allowing some room for context for best speed. You may need to pad this further depending on if you are running image gen or TTS as well. Original model card can be found here",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "---\ntags:\n- merge\n- gguf\n- storywriting\n- text adventure\n- iMat\n---\n<img src=\"https://i.imgur.com/P68dXux.png\" width=\"400\"/>\n\n# Euryale-1.3-longLORA-70b-rope8-32k-iMat-GGUF\n\n\n<b>Special request.</b> Quantized from fp16 with love. Please note I have not tested context to the full 32k, but these quants have all passed the standard suite of coherence and KL-divergence benchmark tests. Any feedback is welcomed.\n* Quantizations made possible using .imatrix file from [this](https://huggingface.co/datasets/ikawrakow/imatrix-from-wiki-train) repo (special thanks to [ikawrakow](https://huggingface.co/ikawrakow) again)\n\nFor a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747)\n\n<i>All quants are verified working prior to uploading to repo for your safety and convenience. </i>\n\nImportance matrix quantizations are a work in progress, IQ3 and above is recommended for best results. \n\n<b>Tip:</b> Pick a size that can fit in your GPU while still allowing some room for context for best speed. You may need to pad this further depending on if you are running image gen or TTS as well.\n\nOriginal model card can be found [here](https://huggingface.co/grimulkan/Euryale-1.3-longLORA-70b-rope8-32k-fp16)",
"related_quantizations": []
},
"tags": [
"gguf",
"merge",
"storywriting",
"text adventure",
"iMat",
"endpoints_compatible",
"region:us"
],
"likes": 0,
"downloads": 421,
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"last_modified": "2024-04-18T07:28:35.000Z",
"created_at": "2024-04-02T01:28:57.000Z",
"pipeline_tag": "",
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Source payload excerpt (from Hugging Face API)
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