Initial commit

.gitattributes

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.gitignore

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+.idea
+models

README.md

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+---
+license: unknown
+---
+
+[ehartford/WizardLM-7B-Uncensored](https://huggingface.co/ehartford/WizardLM-7B-Uncensored) quantized to **8bit GPTQ** with group size 128 + true sequential, no act order.
+
+*For most uses this probably isn't what you want.* \
+*For 4bit GPTQ quantizations see [TheBloke/WizardLM-7B-uncensored-GPTQ](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)*
+
+Quantized using AutoGPTQ with the following config:
+```python
+config: dict = dict(
+    quantize_config=dict(model_file_base_name='WizardLM-7B-Uncensored',
+                         bits=8, desc_act=False, group_size=128, true_sequential=True),
+    use_safetensors=True
+)
+```
+See `quantize.py` for the full script.
+
+Tested for compatibility with:
+- WSL with GPTQ-for-Llama `triton` branch.
+
+AutoGPTQ loader should read configuration from `quantize_config.json`.\
+For GPTQ-for-Llama use the following configuration when loading:\
+wbits: 8\
+groupsize: 128\
+model_type: llama
+

config.json

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+{
+  "_name_or_path": "models/WizardLM-7B-Uncensored",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.2",
+  "use_cache": true,
+  "vocab_size": 32001
+}

quantize.py

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+from transformers import AutoTokenizer, TextGenerationPipeline
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+import logging
+
+pretrained_model_dir: str = "models/WizardLM-7B-Uncensored"
+quantized_model_dir: str = "./"
+config: dict = dict(
+    quantize_config=dict(model_file_base_name='WizardLM-7B-Uncensored',
+                         bits=8, desc_act=False, group_size=128, true_sequential=True),
+    use_safetensors=True
+)
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
+examples: list[dict[str, list[int]]] = [tokenizer("It was a cold night")]
+
+model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, BaseQuantizeConfig(**config['quantize_config']))
+model.quantize(examples)
+
+model.save_quantized(quantized_model_dir, use_safetensors=config['use_safetensors'])

quantize_config.json

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+{
+  "bits": 8,
+  "group_size": 128,
+  "damp_percent": 0.01,
+  "desc_act": false,
+  "sym": true,
+  "true_sequential": true,
+  "model_name_or_path": null,
+  "model_file_base_name": "WizardLM-7B-Uncensored"
+}

tokenizer.model

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