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Quant for 3.5

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README.md CHANGED
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  ---
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  license: apache-2.0
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- quantized_by: bartowski
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- pipeline_tag: text-generation
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  ---
 
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- ## Exllama v2 Quantizations of DrKlaus-7B
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- Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
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- <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
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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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- Original model: https://huggingface.co/macadeliccc/DrKlaus-7B
 
 
 
 
 
 
 
 
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- | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
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- | ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
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- | [8_0](https://huggingface.co/bartowski/DrKlaus-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
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- | [6_5](https://huggingface.co/bartowski/DrKlaus-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
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- | [5_0](https://huggingface.co/bartowski/DrKlaus-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
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- | [4_25](https://huggingface.co/bartowski/DrKlaus-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
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- | [3_5](https://huggingface.co/bartowski/DrKlaus-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
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- ## Download instructions
 
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- With git:
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- ```shell
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- git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/DrKlaus-7B-exl2 DrKlaus-7B-exl2-6_5
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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 `DrKlaus-7B-exl2`:
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-
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- ```shell
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- mkdir DrKlaus-7B-exl2
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- huggingface-cli download bartowski/DrKlaus-7B-exl2 --local-dir DrKlaus-7B-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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- Linux:
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-
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- ```shell
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- mkdir DrKlaus-7B-exl2-6_5
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- huggingface-cli download bartowski/DrKlaus-7B-exl2 --revision 6_5 --local-dir DrKlaus-7B-exl2-6_5 --local-dir-use-symlinks False
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- ```
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- Windows (which apparently doesn't like _ in folders sometimes?):
 
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- ```shell
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- mkdir DrKlaus-7B-exl2-6.5
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- huggingface-cli download bartowski/DrKlaus-7B-exl2 --revision 6_5 --local-dir DrKlaus-7B-exl2-6.5 --local-dir-use-symlinks False
 
 
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  ```
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- Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
 
 
 
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  ---
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  license: apache-2.0
 
 
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  ---
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+ # DrKlaus-7B
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+ ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/E0UeNsU-zKRAwySfeCWf8.webp)
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+ DrKlaus-7B is a SFT model made with [AutoSloth](https://colab.research.google.com/drive/1Zo0sVEb2lqdsUm9dy2PTzGySxdF9CNkc#scrollTo=MmLkhAjzYyJ4) by [macadeliccc](https://huggingface.co/macadeliccc)
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+ ## Process
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+ - Original Model: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
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+ - Datatset: [medalpaca/medical_meadow_wikidoc_patient_information](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information)
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+ - Learning Rate: 3e-05
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+ - Steps: 80
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+ - Warmup Steps: 8
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+ - Per Device Train Batch Size: 24
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+ - Gradient Accumulation Steps 12
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+ - Optimizer: adamw_8bit
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+ - Max Sequence Length: 1024
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+ - Max Prompt Length: 512
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+ - Max Length: 1024
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+ ## 💻 Usage
 
 
 
 
 
 
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+ ```python
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+ !pip install -qU transformers
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+ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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+ model = "macadeliccc/DrKlaus-7B"
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+ tokenizer = AutoTokenizer.from_pretrained(model)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Example prompt
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+ prompt = "Your example prompt here"
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+ # Generate a response
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+ model = AutoModelForCausalLM.from_pretrained(model)
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+ pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
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+ outputs = pipeline(prompt, max_length=50, num_return_sequences=1)
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+ print(outputs[0]["generated_text"])
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  ```
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+ <div align="center">
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+ <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png" height="50" align="center" />
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+ </div>
config.json ADDED
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+ {
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+ "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
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+ "architectures": [
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+ "MistralForCausalLM"
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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": "mistral",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "pad_token_id": 2,
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 1000000.0,
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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": "4.37.2",
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+ "unsloth_version": "2024.2",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
generation_config.json ADDED
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+ "_from_model_config": true,
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+ "bos_token_id": 1,
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+ "transformers_version": "4.37.2"
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+ }
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