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--- |
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base_model: mistralai/Mistral-Nemo-Instruct-2407 |
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language: |
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- en |
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pipeline_tag: text-generation |
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license: apache-2.0 |
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model_creator: Mistral AI |
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model_name: Mistral-Nemo-Instruct-2407 |
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model_type: mistral |
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quantized_by: CISC |
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--- |
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# Mistral-Nemo-Instruct-2407 - SOTA GGUF |
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- Model creator: [Mistral AI](https://huggingface.co/mistralai) |
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- Original model: [Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) |
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<!-- description start --> |
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## Description |
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This repo contains State Of The Art quantized GGUF format model files for [Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407). |
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Quantization was done with an importance matrix that was trained for ~1M tokens (256 batches of 4096 tokens) of [groups_merged.txt](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) and [wiki.train.raw](https://raw.githubusercontent.com/pytorch/examples/main/word_language_model/data/wikitext-2/train.txt) concatenated. |
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The embedded chat template is the updated one with correct Tekken tokenization and function calling support via OpenAI-compatible `tools` parameter, see [example](#simple-llama-cpp-python-example-function-calling-code). |
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<!-- description end --> |
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<!-- prompt-template start --> |
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## Prompt template: Mistral Tekken |
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``` |
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[AVAILABLE_TOOLS][{"name": "function_name", "description": "Description", "parameters": {...}}, ...][/AVAILABLE_TOOLS][INST]{prompt}[/INST] |
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``` |
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<!-- prompt-template end --> |
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<!-- compatibility_gguf start --> |
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## Compatibility |
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These quantised GGUFv3 files are compatible with llama.cpp from July 22nd 2024 onwards, as of commit [50e0535](https://github.com/ggerganov/llama.cpp/commit/50e05353e88d50b644688caa91f5955e8bdb9eb9) |
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They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp. |
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## Explanation of quantisation methods |
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<details> |
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<summary>Click to see details</summary> |
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The new methods available are: |
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* GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw) |
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* GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw |
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* GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw |
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* GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw |
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* GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw |
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* GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw |
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* GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw |
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* GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw |
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* GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw |
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* GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw |
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* GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw |
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* GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw |
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Refer to the Provided Files table below to see what files use which methods, and how. |
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</details> |
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<!-- compatibility_gguf end --> |
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<!-- README_GGUF.md-provided-files start --> |
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## Provided files |
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| Name | Quant method | Bits | Size | Max RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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| [Mistral-Nemo-Instruct-2407.IQ1_S.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ1_S.gguf) | IQ1_S | 1 | 2.8 GB| 3.4 GB | smallest, significant quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ1_M.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ1_M.gguf) | IQ1_M | 1 | 3.0 GB| 3.6 GB | very small, significant quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ2_XXS.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ2_XXS.gguf) | IQ2_XXS | 2 | 3.3 GB| 3.9 GB | very small, high quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ2_XS.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ2_XS.gguf) | IQ2_XS | 2 | 3.6 GB| 4.2 GB | very small, high quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ2_S.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ2_S.gguf) | IQ2_S | 2 | 3.9 GB| 4.4 GB | small, substantial quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ2_M.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ2_M.gguf) | IQ2_M | 2 | 4.1 GB| 4.7 GB | small, greater quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ3_XXS.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ3_XXS.gguf) | IQ3_XXS | 3 | 4.6 GB| 5.2 GB | very small, high quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ3_XS.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ3_XS.gguf) | IQ3_XS | 3 | 4.9 GB| 5.5 GB | small, substantial quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ3_S.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ3_S.gguf) | IQ3_S | 3 | 5.2 GB| 5.8 GB | small, greater quality loss | |
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| [Mistral-Nemo-Instruct-2407.IQ3_M.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ3_M.gguf) | IQ3_M | 3 | 5.3 GB| 5.9 GB | medium, balanced quality - recommended | |
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| [Mistral-Nemo-Instruct-2407.IQ4_XS.gguf](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.IQ4_XS.gguf) | IQ4_XS | 4 | 6.3 GB| 6.9 GB | small, substantial quality loss | |
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Generated importance matrix file: [Mistral-Nemo-Instruct-2407.imatrix.dat](https://huggingface.co/CISCai/Mistral-Nemo-Instruct-2407-SOTA-GGUF/blob/main/Mistral-Nemo-Instruct-2407.imatrix.dat) |
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**Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. |
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<!-- README_GGUF.md-provided-files end --> |
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<!-- README_GGUF.md-how-to-run start --> |
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## Example `llama.cpp` command |
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Make sure you are using `llama.cpp` from commit [50e0535](https://github.com/ggerganov/llama.cpp/commit/50e05353e88d50b644688caa91f5955e8bdb9eb9) or later. |
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```shell |
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./llama-cli -ngl 41 -m Mistral-Nemo-Instruct-2407.IQ4_XS.gguf --color -c 131072 --temp 0.3 --repeat-penalty 1.1 -p "[AVAILABLE_TOOLS]{tools}[/AVAILABLE_TOOLS][INST]{prompt}[/INST]" |
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``` |
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This model is very temperature sensitive, keep it between 0.3 and 0.4 for best results! Also note the lack of spaces between special tokens and input in the prompt; this model is not using the regular Mistral chat template. |
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Change `-ngl 41` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. |
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Change `-c 131072` to the desired sequence length. |
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If you are low on V/RAM try quantizing the K-cache with `-ctk q8_0` or even `-ctk q4_0` for big memory savings (depending on context size). |
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There is a similar option for V-cache (`-ctv`), however that is [not working yet](https://github.com/ggerganov/llama.cpp/issues/4425) unless you enable Flash Attention (`-fa`) too. |
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For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) |
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## How to run from Python code |
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You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) module. |
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### How to load this model in Python code, using llama-cpp-python |
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For full documentation, please see: [llama-cpp-python docs](https://llama-cpp-python.readthedocs.io/en/latest/). |
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#### First install the package |
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Run one of the following commands, according to your system: |
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```shell |
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# Prebuilt wheel with basic CPU support |
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pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu |
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# Prebuilt wheel with NVidia CUDA acceleration |
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pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.) |
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# Prebuilt wheel with Metal GPU acceleration |
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pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal |
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# Build base version with no GPU acceleration |
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pip install llama-cpp-python |
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# With NVidia CUDA acceleration |
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CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python |
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# Or with OpenBLAS acceleration |
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CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python |
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# Or with AMD ROCm GPU acceleration (Linux only) |
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CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python |
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# Or with Metal GPU acceleration for macOS systems only |
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CMAKE_ARGS="-DGGML_METAL=on" pip install llama-cpp-python |
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# Or with Vulkan acceleration |
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CMAKE_ARGS="-DGGML_VULKAN=on" pip install llama-cpp-python |
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# Or with SYCL acceleration |
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CMAKE_ARGS="-DGGML_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install llama-cpp-python |
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# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: |
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$env:CMAKE_ARGS = "-DGGML_CUDA=on" |
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pip install llama-cpp-python |
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``` |
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#### Simple llama-cpp-python example code |
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```python |
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from llama_cpp import Llama |
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# Chat Completion API |
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llm = Llama(model_path="./Mistral-Nemo-Instruct-2407.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072) |
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print(llm.create_chat_completion( |
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messages = [ |
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{ |
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"role": "user", |
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"content": "Pick a LeetCode challenge and solve it in Python." |
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} |
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] |
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)) |
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``` |
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#### Simple llama-cpp-python example function calling code |
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```python |
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from llama_cpp import Llama |
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# Chat Completion API |
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grammar = LlamaGrammar.from_json_schema(json.dumps({ |
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"type": "array", |
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"items": { |
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"type": "object", |
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"required": [ "name", "arguments" ], |
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"properties": { |
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"name": { |
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"type": "string" |
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}, |
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"arguments": { |
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"type": "object" |
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} |
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} |
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} |
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})) |
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llm = Llama(model_path="./Mistral-Nemo-Instruct-2407.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072) |
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response = llm.create_chat_completion( |
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temperature = 0.0, |
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repeat_penalty = 1.1, |
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messages = [ |
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{ |
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"role": "user", |
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"content": "What's the weather like in Oslo and Stockholm?" |
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} |
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], |
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tools=[{ |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, CA" |
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}, |
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"unit": { |
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"type": "string", |
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"enum": [ "celsius", "fahrenheit" ] |
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} |
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}, |
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"required": [ "location" ] |
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} |
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} |
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}], |
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grammar = grammar |
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) |
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print(json.loads(response["choices"][0]["text"])) |
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print(llm.create_chat_completion( |
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temperature = 0.0, |
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repeat_penalty = 1.1, |
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messages = [ |
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{ |
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"role": "user", |
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"content": "What's the weather like in Oslo?" |
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}, |
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{ # The tool_calls is from the response to the above with tool_choice active |
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"role": "assistant", |
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"content": None, |
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"tool_calls": [ |
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{ |
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"id": "call__0_get_current_weather_cmpl-..."[:9], # Make sure to truncate ID (chat template requires it) |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"arguments": '{ "location": "Oslo, NO" ,"unit": "celsius"} ' |
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} |
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} |
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] |
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}, |
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{ # The tool_call_id is from tool_calls and content is the result from the function call you made |
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"role": "tool", |
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"content": "20", |
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"tool_call_id": "call__0_get_current_weather_cmpl-..."[:9] # Make sure to truncate ID (chat template requires it) |
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} |
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], |
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tools=[{ |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, CA" |
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}, |
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"unit": { |
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"type": "string", |
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"enum": [ "celsius", "fahrenheit" ] |
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} |
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}, |
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"required": [ "location" ] |
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} |
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} |
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}], |
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#tool_choice={ |
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# "type": "function", |
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# "function": { |
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# "name": "get_current_weather" |
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# } |
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#} |
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)) |
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``` |
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<!-- README_GGUF.md-how-to-run end --> |
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