ModelHub XC 9542215901 初始化项目,由ModelHub XC社区提供模型
Model: RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf
Source: Original Platform
2026-05-26 23:36:21 +08:00

Quantization made by Richard Erkhov.

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gpt2-autotrain-text-HrPolicy-aug-v2 - GGUF

Name Quant method Size
gpt2-autotrain-text-HrPolicy-aug-v2.Q2_K.gguf Q2_K 0.08GB
gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_XS.gguf IQ3_XS 0.08GB
gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_S.gguf IQ3_S 0.08GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K_S.gguf Q3_K_S 0.08GB
gpt2-autotrain-text-HrPolicy-aug-v2.IQ3_M.gguf IQ3_M 0.09GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q3_K.gguf Q3_K 0.09GB
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 Q3_K_L 0.1GB
gpt2-autotrain-text-HrPolicy-aug-v2.IQ4_XS.gguf IQ4_XS 0.1GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q4_0.gguf Q4_0 0.1GB
gpt2-autotrain-text-HrPolicy-aug-v2.IQ4_NL.gguf IQ4_NL 0.1GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K_S.gguf Q4_K_S 0.1GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K.gguf Q4_K 0.11GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q4_K_M.gguf Q4_K_M 0.11GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q4_1.gguf Q4_1 0.11GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q5_0.gguf Q5_0 0.11GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K_S.gguf Q5_K_S 0.11GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K.gguf Q5_K 0.12GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q5_K_M.gguf Q5_K_M 0.12GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q5_1.gguf Q5_1 0.12GB
gpt2-autotrain-text-HrPolicy-aug-v2.Q6_K.gguf Q6_K 0.13GB
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.

Usage


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)
Description
Model synced from source: RichardErkhov/ishmanish_-_gpt2-autotrain-text-HrPolicy-aug-v2-gguf
Readme 28 KiB