aiuser3993/GLM-Edge-1.5B-Chat-Greek

GLM-Edge-1.5B-Chat-Greek

Quarduple fine-tuned version of this.

Improved Greek language skills, lack of coherence.

Extremely prone to hallunication and to surreal rhetoric.

1st finetune - CausalLM/GPT-4-Self-Instruct-Greek (1 epoch, 0,0002 learning rate)

2nd finetune - The first 10000 rows of enchatted/alpaca-cleaned-gpt4-el (1 epoch, 0,0002 learning rate)

3rd finetune - Rows 10500-23500 of enchatted/alpaca-cleaned-gpt4-el (1 epoch, 0,0001 learning rate)

4th finetune - aiuser3993/Llama-Krikri-8B-Instruct-Chat (1 epoch, 0,00005 learning rate)

Inference with Transformers

Installation

Install the transformers library from the source code:

pip install git+https://github.com/huggingface/transformers.git

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "aiuser3993/GLM-Edge-1.5B-Chat-Greek"

tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")

message = [{"role": "user", "content": "hello!"}]

inputs = tokenizer.apply_chat_template(
    message,
    return_tensors="pt",
    add_generation_prompt=True,
    return_dict=True,
).to(model.device)

generate_kwargs = {
    "input_ids": inputs["input_ids"],
    "attention_mask": inputs["attention_mask"],
    "max_new_tokens": 128,
    "do_sample": False,
}
out = model.generate(**generate_kwargs)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

License

The usage of this model’s weights is subject to the terms outlined in the LICENSE.