Quant for 3.5

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README.md +31 -46
config.json +28 -0
generation_config.json +6 -0
model.safetensors.index.json +298 -0
original_repo_url.txt +1 -0
output.safetensors +3 -0
special_tokens_map.json +24 -0
tokenizer.json +0 -0
tokenizer.model +3 -0
tokenizer_config.json +44 -0

README.md CHANGED Viewed

@@ -1,62 +1,47 @@
 ---
 license: apache-2.0
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
-## Exllama v2 Quantizations of DrKlaus-7B
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
-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/macadeliccc/DrKlaus-7B
-| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
-| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
-| [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. |
-| [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**. |
-| [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. |
-| [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. |
-| [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. |
-## Download instructions
-With git:
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/DrKlaus-7B-exl2 DrKlaus-7B-exl2-6_5
-```
-With huggingface hub (credit to TheBloke for instructions):
-```shell
-pip3 install huggingface-hub
-```
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `DrKlaus-7B-exl2`:
-```shell
-mkdir DrKlaus-7B-exl2
-huggingface-cli download bartowski/DrKlaus-7B-exl2 --local-dir DrKlaus-7B-exl2 --local-dir-use-symlinks False
-```
-To download from a different branch, add the `--revision` parameter:
-Linux:
-```shell
-mkdir DrKlaus-7B-exl2-6_5
-huggingface-cli download bartowski/DrKlaus-7B-exl2 --revision 6_5 --local-dir DrKlaus-7B-exl2-6_5 --local-dir-use-symlinks False
-```
-Windows (which apparently doesn't like _ in folders sometimes?):
-```shell
-mkdir DrKlaus-7B-exl2-6.5
-huggingface-cli download bartowski/DrKlaus-7B-exl2 --revision 6_5 --local-dir DrKlaus-7B-exl2-6.5 --local-dir-use-symlinks False
 ```
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

 ---
 license: apache-2.0
 ---
+# DrKlaus-7B
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/E0UeNsU-zKRAwySfeCWf8.webp)
+DrKlaus-7B is a SFT model made with [AutoSloth](https://colab.research.google.com/drive/1Zo0sVEb2lqdsUm9dy2PTzGySxdF9CNkc#scrollTo=MmLkhAjzYyJ4) by [macadeliccc](https://huggingface.co/macadeliccc)
+## Process
+- Original Model: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- Datatset: [medalpaca/medical_meadow_wikidoc_patient_information](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information)
+- Learning Rate: 3e-05
+- Steps: 80
+- Warmup Steps: 8
+- Per Device Train Batch Size: 24
+- Gradient Accumulation Steps 12
+- Optimizer: adamw_8bit
+- Max Sequence Length: 1024
+- Max Prompt Length: 512
+- Max Length: 1024
+## 💻 Usage
+```python
+!pip install -qU transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+model = "macadeliccc/DrKlaus-7B"
+tokenizer = AutoTokenizer.from_pretrained(model)
+# Example prompt
+prompt = "Your example prompt here"
+# Generate a response
+model = AutoModelForCausalLM.from_pretrained(model)
+pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+outputs = pipeline(prompt, max_length=50, num_return_sequences=1)
+print(outputs[0]["generated_text"])
 ```
+<div align="center">
+<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png" height="50" align="center" />
+</div>

config.json ADDED Viewed

	@@ -0,0 +1,28 @@

+{
+  "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
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+    "MistralForCausalLM"
+  ],
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+  "model_type": "mistral",
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+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "unsloth_version": "2024.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "_from_model_config": true,
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+  "transformers_version": "4.37.2"
+}

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