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--- |
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library_name: transformers |
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tags: |
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- merge |
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language: |
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- en |
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- es |
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- ru |
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- zh |
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- de |
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- fr |
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- th |
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- ca |
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- it |
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- ja |
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- pl |
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- eo |
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- eu |
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- vi |
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- fi |
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- hu |
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- ar |
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- nl |
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- da |
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- tr |
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- ko |
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- he |
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- id |
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- cs |
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- bn |
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- sv |
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widget: |
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- text: | |
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<|im_start|>system |
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You are a helpful AI assistant.<|im_end|> |
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<|im_start|>user |
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podrias escribir un codigo de ejemplo en Python<|im_end|> |
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<|im_start|>assistant |
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license: apache-2.0 |
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--- |
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# Model Card for Model MixLlama |
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<!--  --> |
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<!--  --> |
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<!-- Provide a quick summary of what the model is/does. --> |
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```Python |
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experts: |
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- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_1_V1 |
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positive_prompts: |
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- "" |
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- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_2_V1 |
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positive_prompts: |
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- "" |
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- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_3_V1 |
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positive_prompts: |
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- "" |
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base_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_1_V1 |
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gate_mode: random # one of "hidden", "cheap_embed", or "random" |
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dtype: bfloat16 # output dtype (float32, float16, or bfloat16) |
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``` |
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```Python |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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BitsAndBytesConfig, |
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HfArgumentParser, |
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TrainingArguments, |
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pipeline, |
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logging, |
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GenerationConfig, |
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TextIteratorStreamer, |
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) |
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import torch |
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new_model= "NickyNicky/Mix_TinyLlama-3x1B_oasst2_chatML_Cluster_3_2_1_V1" |
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model = AutoModelForCausalLM.from_pretrained(#f'NickyNicky/{new_model}', |
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new_model, |
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device_map="auto", |
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trust_remote_code=True, |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage= True, |
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# use_flash_attention_2=False, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(new_model, |
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max_length=2048, |
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trust_remote_code=True, |
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use_fast = True, |
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) |
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tokenizer.pad_token = tokenizer.eos_token |
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# tokenizer.padding_side = 'left' |
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tokenizer.padding_side = 'right' |
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prompt= """<|im_start|>system |
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You are a helpful AI assistant.<|im_end|> |
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<|im_start|>user |
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escribe una historia de amor.<|im_end|> |
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<|im_start|>assistant |
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""" |
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inputs = tokenizer.encode(prompt, |
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return_tensors="pt", |
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add_special_tokens=False).cuda()#.to("cuda") # False # True |
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generation_config = GenerationConfig( |
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max_new_tokens=700, |
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temperature=0.5, |
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top_p=0.9, |
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top_k=40, |
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repetition_penalty=1.1, #1.1, # 1.0 means no penalty, > 1.0 means penalty, 1.2 from CTRL paper |
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do_sample=True, |
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pad_token_id=tokenizer.eos_token_id, |
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eos_token_id=tokenizer.eos_token_id, |
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) |
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outputs = model.generate( |
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generation_config=generation_config, |
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input_ids=inputs,) |
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# tokenizer.decode(outputs[0], skip_special_tokens=False) #True |
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print(tokenizer.decode(outputs[0], skip_special_tokens=False)) |
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``` |
