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Model: RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf
Source: Original Platform

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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+gpt2-autotrain-text-HrPolicy-aug-v2 - GGUF
+- Model creator: https://huggingface.co/ishmanish/
+- Original model: https://huggingface.co/ishmanish/gpt2-autotrain-text-HrPolicy-aug-v2/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q2_K.gguf) | Q2_K | 0.08GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_XS.gguf) | IQ3_XS | 0.08GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_S.gguf) | IQ3_S | 0.08GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K_S.gguf) | Q3_K_S | 0.08GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_M.gguf) | IQ3_M | 0.09GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K.gguf) | Q3_K | 0.09GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K_M.gguf) | Q3_K_M | 0.09GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K_L.gguf) | Q3_K_L | 0.1GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.IQ4_XS.gguf) | IQ4_XS | 0.1GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q4_0.gguf) | Q4_0 | 0.1GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.IQ4_NL.gguf) | IQ4_NL | 0.1GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K_S.gguf) | Q4_K_S | 0.1GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K.gguf) | Q4_K | 0.11GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K_M.gguf) | Q4_K_M | 0.11GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q4_1.gguf) | Q4_1 | 0.11GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q5_0.gguf) | Q5_0 | 0.11GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K_S.gguf) | Q5_K_S | 0.11GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K.gguf) | Q5_K | 0.12GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K_M.gguf) | Q5_K_M | 0.12GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q5_1.gguf) | Q5_1 | 0.12GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q6_K.gguf) | Q6_K | 0.13GB |
+| [gpt2-autotrain-text-HrPolicy-aug-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf/blob/main/gpt2-autotrain-text-HrPolicy-aug-v2.Q8_0.gguf) | Q8_0 | 0.17GB |
+
+
+
+
+Original model description:
+---
+tags:
+- autotrain
+- text-generation-inference
+- text-generation
+library_name: transformers
+widget:
+  - messages:
+      - role: user
+        content: What is your favorite condiment?
+license: other
+---
+
+# Model Trained Using AutoTrain
+
+This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+
+# Usage
+
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "PATH_TO_THIS_REPO"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    torch_dtype='auto'
+).eval()
+
+# Prompt content: "hi"
+messages = [
+    {"role": "user", "content": "hi"}
+]
+
+input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
+output_ids = model.generate(input_ids.to('cuda'))
+response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
+
+# Model response: "Hello! How can I assist you today?"
+print(response)
+```
+

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