--- license: other license_name: llama-3 license_link: https://llama.meta.com/llama3/license/ tags: - text-generation-inference - transformers - unsloth - llama datasets: - Replete-AI/code_bagel_hermes-2.5 - Replete-AI/code_bagel - Replete-AI/OpenHermes-2.5-Uncensored - teknium/OpenHermes-2.5 - layoric/tiny-codes-alpaca - glaiveai/glaive-code-assistant-v3 - ajibawa-2023/Code-290k-ShareGPT - TIGER-Lab/MathInstruct - chargoddard/commitpack-ft-instruct-rated - iamturun/code_instructions_120k_alpaca - ise-uiuc/Magicoder-Evol-Instruct-110K - cognitivecomputations/dolphin-coder - nickrosh/Evol-Instruct-Code-80k-v1 - coseal/CodeUltraFeedback_binarized - glaiveai/glaive-function-calling-v2 - CyberNative/Code_Vulnerability_Security_DPO - jondurbin/airoboros-2.2 - camel-ai - lmsys/lmsys-chat-1m - CollectiveCognition/chats-data-2023-09-22 - CoT-Alpaca-GPT4 - WizardLM/WizardLM_evol_instruct_70k - WizardLM/WizardLM_evol_instruct_V2_196k - teknium/GPT4-LLM-Cleaned - GPTeacher - OpenGPT - meta-math/MetaMathQA - Open-Orca/SlimOrca - garage-bAInd/Open-Platypus - anon8231489123/ShareGPT_Vicuna_unfiltered - Unnatural-Instructions-GPT4 model-index: - name: Replete-Coder-llama3-8b results: - task: name: HumanEval type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: .64683835842678326 verified: True - task: name: AI2 Reasoning Challenge type: text-generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: accuracy value: name: normalized accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: accuracy value: name: normalized accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: accuracy value: name: accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: multiple_choice_accuracy value: source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: accuracy value: name: accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard - task: name: Text Generation type: text-generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: accuracy value: name: accuracy source: url: https://www.placeholderurl.com name: Open LLM Leaderboard quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Llama3-8B-Instruct-Replete-Adapted Using turboderp's ExLlamaV2 v0.1.6 for quantization. The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/Replete-AI/Llama3-8B-Instruct-Replete-Adapted ## Prompt format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Available sizes | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ | | [8_0](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | [6_5](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | | [5_0](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | | [4_25](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. | ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2 Llama3-8B-Instruct-Replete-Adapted-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2 --revision 6_5 --local-dir Llama3-8B-Instruct-Replete-Adapted-exl2-6_5 ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Llama3-8B-Instruct-Replete-Adapted-exl2 --revision 6_5 --local-dir Llama3-8B-Instruct-Replete-Adapted-exl2-6.5 ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski