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| Name | Quant method | Size |
|---|---|---|
| homer-bot.Q2_K.gguf | Q2_K | 0.17GB |
| homer-bot.IQ3_XS.gguf | IQ3_XS | 0.18GB |
| homer-bot.IQ3_S.gguf | IQ3_S | 0.19GB |
| homer-bot.Q3_K_S.gguf | Q3_K_S | 0.19GB |
| homer-bot.IQ3_M.gguf | IQ3_M | 0.2GB |
| homer-bot.Q3_K.gguf | Q3_K | 0.21GB |
| homer-bot.Q3_K_M.gguf | Q3_K_M | 0.21GB |
| homer-bot.Q3_K_L.gguf | Q3_K_L | 0.23GB |
| homer-bot.IQ4_XS.gguf | IQ4_XS | 0.22GB |
| homer-bot.Q4_0.gguf | Q4_0 | 0.23GB |
| homer-bot.IQ4_NL.gguf | IQ4_NL | 0.23GB |
| homer-bot.Q4_K_S.gguf | Q4_K_S | 0.23GB |
| homer-bot.Q4_K.gguf | Q4_K | 0.25GB |
| homer-bot.Q4_K_M.gguf | Q4_K_M | 0.25GB |
| homer-bot.Q4_1.gguf | Q4_1 | 0.25GB |
| homer-bot.Q5_0.gguf | Q5_0 | 0.27GB |
| homer-bot.Q5_K_S.gguf | Q5_K_S | 0.27GB |
| homer-bot.Q5_K.gguf | Q5_K | 0.29GB |
| homer-bot.Q5_K_M.gguf | Q5_K_M | 0.29GB |
| homer-bot.Q5_1.gguf | Q5_1 | 0.29GB |
| homer-bot.Q6_K.gguf | Q6_K | 0.32GB |
| homer-bot.Q8_0.gguf | Q8_0 | 0.41GB |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4tokenizer = AutoTokenizer.from_pretrained("DingleyMaillotUrgell/homer-bot")
5model = AutoModelForCausalLM.from_pretrained("DingleyMaillotUrgell/homer-bot")
6
7# Let's chat for 5 lines
8for step in range(5):
9 # encode the new user input, add the eos_token and return a tensor in Pytorch
10 new_user_input_ids = tokenizer.encode(input(">> User: ") + tokenizer.eos_token, return_tensors='pt')
11
12 # append the new user input tokens to the chat history
13 bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
14
15 # generated a response while limiting the total chat history to 1000 tokens,
16 chat_history_ids = model.generate(
17 bot_input_ids,
18 max_length=1000,
19 pad_token_id=tokenizer.eos_token_id,
20 no_repeat_ngram_size=3,
21 do_sample=True,
22 top_k=100,
23 top_p=0.7,
24 temperature = 0.8
25 )
26
27 # print last outpput tokens from bot
28 print("Homer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))