--- license: apache-2.0 tags: - LLMs - mistral - math - Intel - TensorBlock - GGUF base_model: Intel/neural-chat-7b-v3-3 model-index: - name: neural-chat-7b-v3-3 results: - task: type: Large Language Model name: Large Language Model dataset: name: meta-math/MetaMathQA type: meta-math/MetaMathQA metrics: - type: ARC (25-shot) value: 66.89 name: ARC (25-shot) verified: true - type: HellaSwag (10-shot) value: 85.26 name: HellaSwag (10-shot) verified: true - type: MMLU (5-shot) value: 63.07 name: MMLU (5-shot) verified: true - type: TruthfulQA (0-shot) value: 63.01 name: TruthfulQA (0-shot) verified: true - type: Winogrande (5-shot) value: 79.64 name: Winogrande (5-shot) verified: true - type: GSM8K (5-shot) value: 61.11 name: GSM8K (5-shot) verified: true - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.89 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.26 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 63.01 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 61.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Intel/neural-chat-7b-v3-3 name: Open LLM Leaderboard ---
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## Intel/neural-chat-7b-v3-3 - GGUF This repo contains GGUF format model files for [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
Run them on the TensorBlock client using your local machine ↗
## Prompt template ``` ### System: {system_prompt} ### User: {prompt} ### Assistant: ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [neural-chat-7b-v3-3-Q2_K.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q2_K.gguf) | Q2_K | 2.532 GB | smallest, significant quality loss - not recommended for most purposes | | [neural-chat-7b-v3-3-Q3_K_S.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q3_K_S.gguf) | Q3_K_S | 2.947 GB | very small, high quality loss | | [neural-chat-7b-v3-3-Q3_K_M.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q3_K_M.gguf) | Q3_K_M | 3.277 GB | very small, high quality loss | | [neural-chat-7b-v3-3-Q3_K_L.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q3_K_L.gguf) | Q3_K_L | 3.560 GB | small, substantial quality loss | | [neural-chat-7b-v3-3-Q4_0.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q4_0.gguf) | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [neural-chat-7b-v3-3-Q4_K_S.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q4_K_S.gguf) | Q4_K_S | 3.856 GB | small, greater quality loss | | [neural-chat-7b-v3-3-Q4_K_M.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q4_K_M.gguf) | Q4_K_M | 4.068 GB | medium, balanced quality - recommended | | [neural-chat-7b-v3-3-Q5_0.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q5_0.gguf) | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [neural-chat-7b-v3-3-Q5_K_S.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q5_K_S.gguf) | Q5_K_S | 4.654 GB | large, low quality loss - recommended | | [neural-chat-7b-v3-3-Q5_K_M.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q5_K_M.gguf) | Q5_K_M | 4.779 GB | large, very low quality loss - recommended | | [neural-chat-7b-v3-3-Q6_K.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q6_K.gguf) | Q6_K | 5.534 GB | very large, extremely low quality loss | | [neural-chat-7b-v3-3-Q8_0.gguf](https://huggingface.co/tensorblock/neural-chat-7b-v3-3-GGUF/blob/main/neural-chat-7b-v3-3-Q8_0.gguf) | Q8_0 | 7.167 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/neural-chat-7b-v3-3-GGUF --include "neural-chat-7b-v3-3-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/neural-chat-7b-v3-3-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```