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+ ---
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ tags:
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+ - shining-valiant
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+ - shining-valiant-2
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+ - valiant
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+ - valiant-labs
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+ - llama
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+ - llama-3.1
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+ - llama-3.1-instruct
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+ - llama-3.1-instruct-8b
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+ - llama-3
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+ - llama-3-instruct
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+ - llama-3-instruct-8b
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+ - 8b
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+ - science
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+ - physics
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+ - biology
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+ - chemistry
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+ - compsci
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+ - computer-science
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+ - engineering
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+ - technical
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+ - conversational
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+ - chat
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+ - instruct
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+ - TensorBlock
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+ - GGUF
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+ base_model: ValiantLabs/Llama3.1-8B-ShiningValiant2
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+ datasets:
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+ - sequelbox/Celestia
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+ - sequelbox/Spurline
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+ - sequelbox/Supernova
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+ model_type: llama
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+ license: llama3.1
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+ model-index:
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+ - name: Llama3.1-8B-ShiningValiant2
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+ results:
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: Winogrande (5-Shot)
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+ type: Winogrande
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+ args:
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+ num_few_shot: 5
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+ metrics:
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+ - type: acc
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+ value: 75.85
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+ name: acc
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: MMLU College Biology (5-Shot)
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+ type: MMLU
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+ args:
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+ num_few_shot: 5
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+ metrics:
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+ - type: acc
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+ value: 68.75
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+ name: acc
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+ - type: acc
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+ value: 73.23
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+ name: acc
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+ - type: acc
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+ value: 46.0
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+ name: acc
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+ - type: acc
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+ value: 44.33
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+ name: acc
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+ - type: acc
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+ value: 53.19
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+ name: acc
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+ - type: acc
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+ value: 37.25
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+ name: acc
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+ - type: acc
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+ value: 42.38
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+ name: acc
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+ - type: acc
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+ value: 56.0
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+ name: acc
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+ - type: acc
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+ value: 63.0
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+ name: acc
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+ - type: acc
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+ value: 63.16
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+ name: acc
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: IFEval (0-Shot)
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+ type: HuggingFaceH4/ifeval
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+ args:
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+ num_few_shot: 0
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+ metrics:
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+ - type: inst_level_strict_acc and prompt_level_strict_acc
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+ value: 65.24
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+ name: strict accuracy
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+ source:
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+ url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
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+ name: Open LLM Leaderboard
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: BBH (3-Shot)
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+ type: BBH
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+ args:
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+ num_few_shot: 3
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+ metrics:
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+ - type: acc_norm
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+ value: 26.35
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+ name: normalized accuracy
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+ source:
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+ url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
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+ name: Open LLM Leaderboard
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: MATH Lvl 5 (4-Shot)
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+ type: hendrycks/competition_math
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+ args:
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+ num_few_shot: 4
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+ metrics:
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+ - type: exact_match
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+ value: 11.63
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+ name: exact match
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+ source:
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+ url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
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+ name: Open LLM Leaderboard
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: GPQA (0-shot)
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+ type: Idavidrein/gpqa
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+ args:
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+ num_few_shot: 0
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+ metrics:
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+ - type: acc_norm
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+ value: 8.95
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+ name: acc_norm
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+ source:
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+ url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
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+ name: Open LLM Leaderboard
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
156
+ name: MuSR (0-shot)
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+ type: TAUR-Lab/MuSR
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+ args:
159
+ num_few_shot: 0
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+ metrics:
161
+ - type: acc_norm
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+ value: 7.19
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+ name: acc_norm
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+ source:
165
+ url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
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+ name: Open LLM Leaderboard
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+ - task:
168
+ type: text-generation
169
+ name: Text Generation
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+ dataset:
171
+ name: MMLU-PRO (5-shot)
172
+ type: TIGER-Lab/MMLU-Pro
173
+ config: main
174
+ split: test
175
+ args:
176
+ num_few_shot: 5
177
+ metrics:
178
+ - type: acc
179
+ value: 26.38
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+ name: accuracy
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+ source:
182
+ url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
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+ name: Open LLM Leaderboard
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+ ---
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+
186
+ <div style="width: auto; margin-left: auto; margin-right: auto">
187
+ <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
188
+ </div>
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+ <div style="display: flex; justify-content: space-between; width: 100%;">
190
+ <div style="display: flex; flex-direction: column; align-items: flex-start;">
191
+ <p style="margin-top: 0.5em; margin-bottom: 0em;">
192
+ Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
193
+ </p>
194
+ </div>
195
+ </div>
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+
197
+ ## ValiantLabs/Llama3.1-8B-ShiningValiant2 - GGUF
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+
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+ This repo contains GGUF format model files for [ValiantLabs/Llama3.1-8B-ShiningValiant2](https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2).
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+
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+ 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).
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+
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+ ## Prompt template
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+
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+ ```
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+ <|begin_of_text|><|start_header_id|>system<|end_header_id|>
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+
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+ Cutting Knowledge Date: December 2023
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+ Today Date: 26 Jul 2024
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+
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+ {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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+
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+ {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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+ ```
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+
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+ ## Model file specification
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+
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+ | Filename | Quant type | File Size | Description |
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+ | -------- | ---------- | --------- | ----------- |
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+ | [Llama3.1-8B-ShiningValiant2-Q2_K.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
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+ | [Llama3.1-8B-ShiningValiant2-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
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+ | [Llama3.1-8B-ShiningValiant2-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
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+ | [Llama3.1-8B-ShiningValiant2-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
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+ | [Llama3.1-8B-ShiningValiant2-Q4_0.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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+ | [Llama3.1-8B-ShiningValiant2-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
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+ | [Llama3.1-8B-ShiningValiant2-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
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+ | [Llama3.1-8B-ShiningValiant2-Q5_0.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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+ | [Llama3.1-8B-ShiningValiant2-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
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+ | [Llama3.1-8B-ShiningValiant2-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
230
+ | [Llama3.1-8B-ShiningValiant2-Q6_K.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
231
+ | [Llama3.1-8B-ShiningValiant2-Q8_0.gguf](https://huggingface.co/tensorblock/Llama3.1-8B-ShiningValiant2-GGUF/tree/main/Llama3.1-8B-ShiningValiant2-Q8_0.gguf) | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended |
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+
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+
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+ ## Downloading instruction
235
+
236
+ ### Command line
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+
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+ Firstly, install Huggingface Client
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+
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+ ```shell
241
+ pip install -U "huggingface_hub[cli]"
242
+ ```
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+
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+ Then, downoad the individual model file the a local directory
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+
246
+ ```shell
247
+ huggingface-cli download tensorblock/Llama3.1-8B-ShiningValiant2-GGUF --include "Llama3.1-8B-ShiningValiant2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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+ ```
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+
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+ If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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+
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+ ```shell
253
+ huggingface-cli download tensorblock/Llama3.1-8B-ShiningValiant2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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+ ```