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RichardErkhov/MadKFC_-_MergedCPsyModel-gguf
MadKFC_-_MergedCPsyModel-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
856
43% of all-time downloads
All-time downloads
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.gguf108 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
MergedCPsyModel - GGUF
| Name | Quant method | Size |
|---|---|---|
| MergedCPsyModel.Q2_K.gguf | Q2_K | 2.96GB |
| MergedCPsyModel.IQ3_XS.gguf | IQ3_XS | 3.28GB |
| MergedCPsyModel.IQ3_S.gguf | IQ3_S | 3.43GB |
| MergedCPsyModel.Q3_K_S.gguf | Q3_K_S | 3.41GB |
| MergedCPsyModel.IQ3_M.gguf | IQ3_M | 3.52GB |
| MergedCPsyModel.Q3_K.gguf | Q3_K | 3.74GB |
| MergedCPsyModel.Q3_K_M.gguf | Q3_K_M | 3.74GB |
| MergedCPsyModel.Q3_K_L.gguf | Q3_K_L | 4.03GB |
| MergedCPsyModel.IQ4_XS.gguf | IQ4_XS | 4.18GB |
| MergedCPsyModel.Q4_0.gguf | Q4_0 | 4.34GB |
| MergedCPsyModel.IQ4_NL.gguf | IQ4_NL | 4.38GB |
| MergedCPsyModel.Q4_K_S.gguf | Q4_K_S | 4.37GB |
| MergedCPsyModel.Q4_K.gguf | Q4_K | 4.58GB |
| MergedCPsyModel.Q4_K_M.gguf | Q4_K_M | 4.58GB |
| MergedCPsyModel.Q4_1.gguf | Q4_1 | 4.78GB |
| MergedCPsyModel.Q5_0.gguf | Q5_0 | 5.21GB |
| MergedCPsyModel.Q5_K_S.gguf | Q5_K_S | 5.21GB |
| MergedCPsyModel.Q5_K.gguf | Q5_K | 5.34GB |
| MergedCPsyModel.Q5_K_M.gguf | Q5_K_M | 5.34GB |
| MergedCPsyModel.Q5_1.gguf | Q5_1 | 5.65GB |
| MergedCPsyModel.Q6_K.gguf | Q6_K | 6.14GB |
| MergedCPsyModel.Q8_0.gguf | Q8_0 | 7.95GB |
tags:
This model was trained using AutoTrain. For more information, please visit AutoTrain.
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)