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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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tags: |
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- finetuned |
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inference: |
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parameters: |
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temperature: 0.01 |
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--- |
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A Mistral7B Instruct (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) |
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Finetune using QLoRA on the docs available in https://docs.modular.com/mojo/ |
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The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. |
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## Instruction format |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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import torch |
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device = "cuda" # the device to load the model onto |
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model_name = "mcysqrd/MODULARMOJO_Mistral_V1" |
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model = AutoModelForCausalLM.from_pretrained(model_name, |
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use_flash_attention_2=True, |
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max_memory={0: "24GB"}, |
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device_map="auto", |
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trust_remote_code=True, |
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low_cpu_mem_usage=True, |
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return_dict=True, |
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torch_dtype=torch.bfloat16, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name,add_bos_token=True,trust_remote_code=True) |
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model.config.use_cache = True |
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def stream(user_prompt): |
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runtimeFlag = "cuda:0" |
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system_prompt = 'MODULAR_MOJO' |
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B_INST, E_INST = "[INST]", "[/INST]" |
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prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n{E_INST}" |
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inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag) |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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_ = model.generate(**inputs, streamer=streamer, max_new_tokens=1600) |
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stream("""can you translate this python code to mojo to make more performant making T as struct? |
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class T(): |
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self.init(v:float): |
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self.value=v |
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def sum_objects(a:T,b:T)->T: |
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return T(a.v+b.v)""") |
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``` |
