bartowski commited on
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Quant for 4.0

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README.md CHANGED
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  license: mit
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  language:
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  - en
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- quantized_by: bartowski
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- pipeline_tag: text-generation
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  ---
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-
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- ## Exllama v2 Quantizations of FusionNet_7Bx2_MoE_14B
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-
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- Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
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-
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- # The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
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-
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- Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
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-
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- Conversion was done using the default calibration dataset.
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-
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- Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
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-
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- Original model: https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_14B
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-
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-
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-
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- <a href="https://huggingface.co/bartowski/FusionNet_7Bx2_MoE_14B-exl2/tree/8_0">8.0 bits per weight</a>
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-
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- <a href="https://huggingface.co/bartowski/FusionNet_7Bx2_MoE_14B-exl2/tree/6_5">6.5 bits per weight</a>
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-
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- <a href="https://huggingface.co/bartowski/FusionNet_7Bx2_MoE_14B-exl2/tree/5_0">5.0 bits per weight</a>
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-
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- <a href="https://huggingface.co/bartowski/FusionNet_7Bx2_MoE_14B-exl2/tree/4_0">4.0 bits per weight</a>
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-
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- <a href="https://huggingface.co/bartowski/FusionNet_7Bx2_MoE_14B-exl2/tree/3_5">3.5 bits per weight</a>
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-
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- ## Download instructions
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-
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- With git:
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-
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- ```shell
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- git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/FusionNet_7Bx2_MoE_14B-exl2
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- ```
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-
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- With huggingface hub (credit to TheBloke for instructions):
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-
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- ```shell
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- pip3 install huggingface-hub
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- ```
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-
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- To download the `main` (only useful if you only care about measurement.json) branch to a folder called `FusionNet_7Bx2_MoE_14B-exl2`:
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-
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- ```shell
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- mkdir FusionNet_7Bx2_MoE_14B-exl2
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- huggingface-cli download bartowski/FusionNet_7Bx2_MoE_14B-exl2 --local-dir FusionNet_7Bx2_MoE_14B-exl2 --local-dir-use-symlinks False
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- ```
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-
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- To download from a different branch, add the `--revision` parameter:
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-
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- ```shell
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- mkdir FusionNet_7Bx2_MoE_14B-exl2
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- huggingface-cli download bartowski/FusionNet_7Bx2_MoE_14B-exl2 --revision 4_0 --local-dir FusionNet_7Bx2_MoE_14B-exl2 --local-dir-use-symlinks False
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- ```
 
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  license: mit
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  language:
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  - en
 
 
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  ---
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+ # FusionNet
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+ Fine-tuned model on English language using MoE method.
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+ ## Model description
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+ The FusionNet is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The FusionNet has 12.9B parameters, and this model is fine-tuned. Enjoy!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
config.json ADDED
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+ {
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+ "_name_or_path": "TomGrc/FusionNet_7Bx2_MoE_14B",
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+ "architectures": [
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+ "MixtralForCausalLM"
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+ ],
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 32768,
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+ "model_type": "mixtral",
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+ "num_attention_heads": 32,
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+ "num_experts_per_tok": 2,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "num_local_experts": 2,
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+ "output_router_logits": false,
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+ "pad_token_id": 2,
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 10000.0,
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+ "router_aux_loss_coef": 0.001,
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+ "sliding_window": null,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.37.0.dev0",
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+ "unsloth_version": "2024.1",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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