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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).
It could generate amateur, but more or less adequate output as well (in respect of training tokens).
The work can be used as a checkpoint for the further training or for experiments.
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))
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