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| 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 |
1from transformers import LlamaTokenizerFast, LlamaForCausalLM
2model = LlamaForCausalLM.from_pretrained('demetera/llama-600M-rus')
3tokenizer = LlamaTokenizerFast.from_pretrained('demetera/llama-600M-rus')
4
5prompt = "Я вышел и улицу и"
6inputs = tokenizer(prompt, return_tensors='pt')
7outputs = model.generate(inputs.input_ids, attention_mask = inputs.attention_mask, max_new_tokens=250, do_sample=True, top_k=50, top_p=0.95)
8
9print (tokenizer.decode(outputs[0], skip_special_tokens=True))