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Llama-3-8B-Instruct-1048k-i1-GGUF

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  <!-- ### quantize_version: 2 -->
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  <!-- ### output_tensor_quantised: 1 -->
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  <!-- ### convert_type: -->
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  <!-- ### vocab_type: -->
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  weighted/imatrix quants of https://huggingface.co/DevsDoCode/Llama-3-8B-Instruct-1048k
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: DevsDoCode/Llama-3-8B-Instruct-1048k
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+ language:
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+ - en
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+ library_name: transformers
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+ quantized_by: mradermacher
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+ ---
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+ ## About
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+
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  <!-- ### quantize_version: 2 -->
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  <!-- ### output_tensor_quantised: 1 -->
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  <!-- ### convert_type: -->
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  <!-- ### vocab_type: -->
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  weighted/imatrix quants of https://huggingface.co/DevsDoCode/Llama-3-8B-Instruct-1048k
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+
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+
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+ <!-- provided-files -->
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+ static quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-GGUF
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+ ## Usage
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+
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+ If you are unsure how to use GGUF files, refer to one of [TheBloke's
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+ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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+ more details, including on how to concatenate multi-part files.
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+
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+ ## Provided Quants
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+
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+ (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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+
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+ | Link | Type | Size/GB | Notes |
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+ |:-----|:-----|--------:|:------|
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | for the desperate |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
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+ | [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-1048k-i1-GGUF/resolve/main/Llama-3-8B-Instruct-1048k.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
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+
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+
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+ Here is a handy graph by ikawrakow comparing some lower-quality quant
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+ types (lower is better):
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+
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+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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+
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+ And here are Artefact2's thoughts on the matter:
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+ https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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+
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+ ## FAQ / Model Request
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+
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+ See https://huggingface.co/mradermacher/model_requests for some answers to
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+ questions you might have and/or if you want some other model quantized.
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+
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+ ## Thanks
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+
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+ I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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+ me use its servers and providing upgrades to my workstation to enable
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+ this work in my free time.
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+
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+ <!-- end -->