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CodeLlama3-8B-Python-GGUF

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  <!-- ### vocab_type: -->
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  static quants of https://huggingface.co/Markhit/CodeLlama3-8B-Python
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  <!-- provided-files -->
 
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  ## Usage
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  If you are unsure how to use GGUF files, refer to one of [TheBloke's
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  | Link | Type | Size/GB | Notes |
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  |:-----|:-----|--------:|:------|
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- | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.Q2_K.gguf) | Q2_K | 3.3 | |
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- | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
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- | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.IQ3_M.gguf) | IQ3_M | 3.9 | |
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- | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
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- | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
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- | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
 
 
 
 
 
 
 
 
 
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  Here is a handy graph by ikawrakow comparing some lower-quality quant
 
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  <!-- ### vocab_type: -->
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  static quants of https://huggingface.co/Markhit/CodeLlama3-8B-Python
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  <!-- provided-files -->
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+ weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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  ## Usage
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  If you are unsure how to use GGUF files, refer to one of [TheBloke's
 
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  | Link | Type | Size/GB | Notes |
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  |:-----|:-----|--------:|:------|
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q2_K.gguf) | Q2_K | 3.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.IQ3_M.gguf) | IQ3_M | 3.9 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
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+ | [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-GGUF/resolve/main/CodeLlama3-8B-Python.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
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  Here is a handy graph by ikawrakow comparing some lower-quality quant