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--- |
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library_name: peft |
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datasets: |
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- Xilabs/instructmix |
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pipeline_tag: text-generation |
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--- |
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## Model Card for "InstructMix Llama 3B" |
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**Model Name:** InstructMix Llama 3B |
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**Description:** |
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InstructMix Llama 3B is a language model fine-tuned on the InstructMix dataset using parameter-efficient fine-tuning (PEFT), using the base model "openlm-research/open_llama_3b_v2," which can be found at [https://huggingface.co/openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2). |
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An easy way to use InstructMix Llama 3B is via the API: https://replicate.com/ritabratamaiti/instructmix-llama-3b |
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**Usage:** |
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```py |
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import torch |
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from transformers import LlamaForCausalLM, LlamaTokenizer |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig |
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from peft import PeftModel, PeftConfig |
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# Hugging Face model_path |
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model_path = 'openlm-research/open_llama_3b_v2' |
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peft_model_id = 'Xilabs/instructmix-llama-3b' |
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tokenizer = LlamaTokenizer.from_pretrained(model_path) |
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model = LlamaForCausalLM.from_pretrained( |
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model_path, device_map="auto" |
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) |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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def generate_prompt(instruction, input=None): |
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if input: |
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return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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{instruction} |
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### Input: |
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{input} |
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### Response:""" |
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else: |
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return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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{instruction} |
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### Response:""" |
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def evaluate( |
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instruction, |
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input=None, |
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temperature=0.5, |
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top_p=0.75, |
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top_k=40, |
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num_beams=5, |
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max_new_tokens=128, |
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**kwargs, |
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): |
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prompt = generate_prompt(instruction, input) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs["input_ids"].to("cuda") |
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generation_config = GenerationConfig( |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=num_beams, |
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early_stopping=True, |
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repetition_penalty=1.1, |
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**kwargs, |
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) |
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with torch.no_grad(): |
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generation_output = model.generate( |
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input_ids=input_ids, |
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generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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max_new_tokens=max_new_tokens, |
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) |
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s = generation_output.sequences[0] |
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output = tokenizer.decode(s, skip_special_tokens = True) |
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#print(output) |
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return output.split("### Response:")[1] |
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instruction = "What is the meaning of life?" |
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print(evaluate(instruction, num_beams=3, temperature=0.1, max_new_tokens=256)) |
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``` |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: bfloat16 |
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### Framework versions |
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- PEFT 0.4.0 |
