demetera_-_llama-600M-rus-gguf

RichardErkhov/demetera_-_llama-600M-rus-gguf

demetera_-_llama-600M-rus-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.

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6.6 GB

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Hugging Face

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.gguf7.7 GB · 100%

At a glance

Access: Public
Created: Jul 3, 2024
Updated: Jul 3, 2024
SHA: 1165d08b

Model card

From the Hugging Face model README

Quantization made by Richard Erkhov.

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llama-600M-rus - GGUF

Model creator: https://huggingface.co/demetera/
Original model: https://huggingface.co/demetera/llama-600M-rus/

Name	Quant method	Size
llama-600M-rus.Q2_K.gguf	Q2_K	0.22GB
llama-600M-rus.IQ3_XS.gguf	IQ3_XS	0.24GB
llama-600M-rus.IQ3_S.gguf	IQ3_S	0.25GB
llama-600M-rus.Q3_K_S.gguf	Q3_K_S	0.25GB
llama-600M-rus.IQ3_M.gguf	IQ3_M	0.26GB
llama-600M-rus.Q3_K.gguf	Q3_K	0.27GB
llama-600M-rus.Q3_K_M.gguf	Q3_K_M	0.27GB
llama-600M-rus.Q3_K_L.gguf	Q3_K_L	0.28GB
llama-600M-rus.IQ4_XS.gguf	IQ4_XS	0.29GB
llama-600M-rus.Q4_0.gguf	Q4_0	0.3GB
llama-600M-rus.IQ4_NL.gguf	IQ4_NL	0.3GB
llama-600M-rus.Q4_K_S.gguf	Q4_K_S	0.31GB
llama-600M-rus.Q4_K.gguf	Q4_K	0.33GB
llama-600M-rus.Q4_K_M.gguf	Q4_K_M	0.33GB
llama-600M-rus.Q4_1.gguf	Q4_1	0.33GB
llama-600M-rus.Q5_0.gguf	Q5_0	0.36GB
llama-600M-rus.Q5_K_S.gguf	Q5_K_S	0.36GB
llama-600M-rus.Q5_K.gguf	Q5_K	0.38GB
llama-600M-rus.Q5_K_M.gguf	Q5_K_M	0.38GB
llama-600M-rus.Q5_1.gguf	Q5_1	0.39GB
llama-600M-rus.Q6_K.gguf	Q6_K	0.45GB
llama-600M-rus.Q8_0.gguf	Q8_0	0.54GB

Original model description:

license: mit language:

ru library_name: transformers

llama-600M-rus

Simple and customized amateur experimental model pretrained on the text fiction books from the scratch (updating the model regularly).<br> It could generate amateur, but more or less adequate output as well (in respect of training tokens).<br> The work can be used as a checkpoint for the further training or for experiments.<br>

Simple usage example:

from transformers import LlamaTokenizerFast, LlamaForCausalLM
model = LlamaForCausalLM.from_pretrained('demetera/llama-600M-rus')
tokenizer = LlamaTokenizerFast.from_pretrained('demetera/llama-600M-rus')

prompt = "Я вышел и улицу и"
inputs = tokenizer(prompt, return_tensors='pt')
outputs = model.generate(inputs.input_ids, attention_mask = inputs.attention_mask, max_new_tokens=250, do_sample=True, top_k=50, top_p=0.95)

print (tokenizer.decode(outputs[0], skip_special_tokens=True))

Created: Jul 3, 2024
Updated: Jul 3, 2024