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RichardErkhov/
sunwu51_-_autotrain-q971d-3x945-gguf

Name: sunwu51_-_autotrain-q971d-3x945-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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autotrain-q971d-3x945 - GGUF

Model creator: https://huggingface.co/sunwu51/
Original model: https://huggingface.co/sunwu51/autotrain-q971d-3x945/

Name	Quant method	Size
autotrain-q971d-3x945.Q2_K.gguf	Q2_K	0.63GB
autotrain-q971d-3x945.IQ3_XS.gguf	IQ3_XS	0.68GB
autotrain-q971d-3x945.IQ3_S.gguf	IQ3_S	0.71GB
autotrain-q971d-3x945.Q3_K_S.gguf	Q3_K_S	0.71GB
autotrain-q971d-3x945.IQ3_M.gguf	IQ3_M	0.72GB
autotrain-q971d-3x945.Q3_K.gguf	Q3_K	0.77GB
autotrain-q971d-3x945.Q3_K_M.gguf	Q3_K_M	0.77GB
autotrain-q971d-3x945.Q3_K_L.gguf	Q3_K_L	0.82GB
autotrain-q971d-3x945.IQ4_XS.gguf	IQ4_XS	0.84GB
autotrain-q971d-3x945.Q4_0.gguf	Q4_0	0.87GB
autotrain-q971d-3x945.IQ4_NL.gguf	IQ4_NL	0.88GB
autotrain-q971d-3x945.Q4_K_S.gguf	Q4_K_S	0.88GB
autotrain-q971d-3x945.Q4_K.gguf	Q4_K	0.92GB
autotrain-q971d-3x945.Q4_K_M.gguf	Q4_K_M	0.92GB
autotrain-q971d-3x945.Q4_1.gguf	Q4_1	0.95GB
autotrain-q971d-3x945.Q5_0.gguf	Q5_0	1.02GB
autotrain-q971d-3x945.Q5_K_S.gguf	Q5_K_S	1.02GB
autotrain-q971d-3x945.Q5_K.gguf	Q5_K	1.05GB
autotrain-q971d-3x945.Q5_K_M.gguf	Q5_K_M	1.05GB
autotrain-q971d-3x945.Q5_1.gguf	Q5_1	1.1GB
autotrain-q971d-3x945.Q6_K.gguf	Q6_K	1.19GB
autotrain-q971d-3x945.Q8_0.gguf	Q8_0	1.53GB

Original model description:

base_model: Qwen/Qwen2-1.5B-Instruct datasets:

sunwu51/chinese_braille_3k_rows library_name: transformers license: other tags:
autotrain
text-generation-inference
text-generation
peft widget:
messages:
- role: user content: What is your favorite condiment?

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit 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)

RichardErkhov/sunwu51_-_autotrain-q971d-3x945-gguf

Original model description:

Model Trained Using AutoTrain

Usage

RichardErkhov/
sunwu51_-_autotrain-q971d-3x945-gguf