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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 stanford_alpaca-main/alpaca_data.json filter=lfs diff=lfs merge=lfs -text
+alpaca-lora-main/alpaca_data_cleaned.json filter=lfs diff=lfs merge=lfs -text
+alpaca-lora-main/alpaca_data.json filter=lfs diff=lfs merge=lfs -text

alpaca-lora-main/.gitignore

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+out/
+7B/
+13B/
+__pycache__/
+checkpoint**
+minimal-llama**
+upload.py
+lora-**
+*ckpt
+wandb

alpaca-lora-main/DATA_LICENSE

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alpaca-lora-main/LICENSE

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alpaca-lora-main/README.md

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+## 🦙🌲🤏 Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning
+
+- Try the pretrained model out on Colab [here](https://colab.research.google.com/drive/1eWAmesrW99p7e1nah5bipn0zikMb8XYC)
+- Share custom LoRA adapters, including adapters for the larger models, [here](https://github.com/tloen/alpaca-lora/issues/52)
+- Users have created a Discord server for discussion and support [here](https://discord.gg/prbq284xX5)
+- `alpaca-lora-30b` can be used like ChatGPT; see [here](https://twitter.com/algo_diver/status/1637851640027041798)
+
+This repository contains code for reproducing the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) results using [low-rank adaptation (LoRA)](https://arxiv.org/pdf/2106.09685.pdf).
+We provide an Instruct model of similar quality to `text-davinci-003` that can run [on a Raspberry Pi](https://twitter.com/miolini/status/1634982361757790209) (for research),
+and the code is easily extended to the `13b`, `30b`, and `65b` models.
+
+In addition to the training code, which runs within five hours on a single RTX 4090,
+we publish a script for downloading and inference on the foundation model and LoRA,
+as well as the resulting [LoRA weights themselves](https://huggingface.co/tloen/alpaca-lora-7b/tree/main).
+To fine-tune cheaply and efficiently, we use Hugging Face's [PEFT](https://github.com/huggingface/peft)
+as well as Tim Dettmers' [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
+
+Without hyperparameter tuning, the LoRA model produces outputs comparable to the Stanford Alpaca model. (Please see the outputs included below.) Further tuning might be able to achieve better performance; I invite interested users to give it a try and report their results.
+
+### Setup
+
+1. Install dependencies
+
+```
+pip install -r requirements.txt
+```
+
+2. If bitsandbytes doesn't work, [install it from source.](https://github.com/TimDettmers/bitsandbytes/blob/main/compile_from_source.md) Windows users can follow [these instructions](https://github.com/tloen/alpaca-lora/issues/17).
+
+### Inference (`generate.py`)
+
+This file reads the foundation model from the Hugging Face model hub and the LoRA weights from `tloen/alpaca-lora-7b`, and runs a Gradio interface for inference on a specified input. Users should treat this as example code for the use of the model, and modify it as needed.
+
+### Training (`finetune.py`)
+
+This file contains a straightforward application of PEFT to the LLaMA model,
+as well as some code related to prompt construction and tokenization.
+Near the top of this file is a set of hardcoded hyperparameters that you should feel free to modify.
+PRs adapting this code to support larger models are always welcome.
+
+### Checkpoint export (`export_*_checkpoint.py`)
+
+These files contain scripts that merge the LoRA weights back into the base model
+for export to Hugging Face format and to PyTorch `state_dicts`.
+They should help users
+who want to run inference in projects like [llama.cpp](https://github.com/ggerganov/llama.cpp)
+or [alpaca.cpp](https://github.com/antimatter15/alpaca.cpp).
+
+### Dataset
+
+In addition to `alpaca_data.json`, which contains the original Stanford Alpaca dataset,
+we also include `alpaca_data_cleaned.json`, which has been [stripped of various tokenization artifacts](https://github.com/tloen/alpaca-lora/pull/32)
+with the help of @gururise.
+This file is now used by default in the training script.
+
+@AndriyMulyar has also provided interactive, embedding-based visualizations of the original dataset's [instructions](https://atlas.nomic.ai/map/alpaca_instructions)
+and [outputs](https://atlas.nomic.ai/map/alpaca_outputs),
+as well as [clusters of bad examples](https://atlas.nomic.ai/map/d2139cc3-bc1c-441c-8d6f-3e6ffbbc2eda/838019ff-8fe2-42ba-809a-d86d2b98cd50/-18.11668742841587/-11.348087116836096/-20.88850316347706/-17.680468640801223/774455612).
+
+### Notes
+
+- We can likely improve our model performance significantly if we had a better dataset. Consider supporting the [LAION Open Assistant](https://open-assistant.io/) effort to produce a high-quality dataset for supervised fine-tuning (or bugging them to release their data).
+- We're continually fixing bugs and conducting training runs, and the weights on the Hugging Face Hub are being updated accordingly. In particular, those facing issues with response lengths should make sure that they have the latest version of the weights and code.
+
+### Example outputs
+
+**Instruction**: Tell me about alpacas.
+
+**Alpaca-LoRA**: Alpacas are members of the camelid family and are native to the Andes Mountains of South America. They are known for their soft, luxurious fleece, which is used to make clothing, blankets, and other items. Alpacas are herbivores and graze on grasses and other plants. They are social animals and live in herds of up to 20 individuals.
+
+**Stanford Alpaca**: Alpacas are small, fluffy animals related to camels and llamas. They are native to Peru and Bolivia, and were first domesticated around 5,000 years ago. They are kept mainly for their fine, soft fleece, which is used to make knitwear and other garments. Alpacas are herd animals and live in small family groups, led by an older male. They are highly social animals and can form strong bonds with their owners.
+
+**text-davinci-003**: Alpacas are a domesticated species of South American camelid, similar to llamas. They are native to the Andes Mountains in South America and are kept as livestock for their soft, luxurious wool. Alpacas are social animals and live in herds of up to 20 individuals. They are typically kept in small herds of two to five animals, and are relatively easy to care for. Alpacas are herbivores and feed on grass, hay, and other vegetation. They are also known for their gentle and friendly nature, making them popular as pets.
+
+---
+
+**Instruction**: Tell me about the president of Mexico in 2019.
+
+**Alpaca-LoRA**: The president of Mexico in 2019 was Andrés Manuel López Obrador, who took office on December 1, 2018. He is a member of the National Regeneration Movement (MORENA) political party and is the first left-wing president of Mexico since 1946. He is known for his anti-corruption and anti-neoliberal policies, as well as his commitment to improving the living conditions of the Mexican people.
+
+**Stanford Alpaca**: In 2019, Andres Manuel Lopez Obrador was sworn in as President of Mexico. He is the country’s 58th president and the first from the left-wing Morena party. He was elected in 2018 on a promise to combat corruption and reduce poverty and has faced criticism for his handling of the economy, crime and the country’s relationship with the United States. He is also known for his ambitious social welfare programs and has been praised for raising the minimum wage and providing aid to low-income families. He is seen as a champion of democracy and human rights in Mexico.
+
+**text-davinci-003**: The president of Mexico in 2019 is Andrés Manuel López Obrador, commonly known as AMLO. He is a Mexican politician who has been the President of Mexico since December 1, 2018. He is a member of the National Regeneration Movement (MORENA) party and previously served as the Head of Government of Mexico City from 2000 to 2005. He is a left-wing populist and has promised to reduce corruption and inequality in Mexico. He has also promised to increase public spending on social programs and infrastructure projects.
+
+---
+
+**Instruction**: Tell me about the king of France in 2019.
+
+**Alpaca-LoRA**: The King of France in 2019 is Emmanuel Macron, who was elected in May 2017. He is the youngest president in the history of the Fifth Republic and the first president to be born after World War II. He is also the first president to have never held elected office before.
+
+**Stanford Alpaca**: In 2019, the King of France was Emmanuel Macron. He was sworn in as President of France on May 14, 2017, and has been leading the country ever since. He is a member of the centrist La République En Marche! Party. He is known for his reformist agenda and has faced a number of protests from both the right and left of the political spectrum. He is the youngest French president in history.
+
+**text-davinci-003**: The King of France is a largely symbolic role, as France is a republic. The current President of France is Emmanuel Macron, who was elected in 2017.
+
+---
+
+**Instruction**: List all Canadian provinces in alphabetical order.
+
+**Alpaca-LoRA**: Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, Quebec, Saskatchewan
+
+**Stanford Alpaca**: Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, Quebec, Saskatchewan, Northwest Territories, Nunavut, Yukon.
+
+**text-davinci-003**:
+
+1. Alberta
+2. British Columbia
+3. Manitoba
+4. New Brunswick
+5. Newfoundland and Labrador
+6. Northwest Territories
+7. Nova Scotia
+8. Nunavut
+9. Ontario
+10. Prince Edward Island
+11. Quebec
+12. Saskatchewan
+13. Yukon
+
+---
+
+**Instruction**: Write a Python program that prints the first 10 Fibonacci numbers.
+
+**Alpaca-LoRA**:
+
+```python
+def fibonacci(n):
+    if n == 0:
+        return 0
+    elif n == 1:
+        return 1
+    else:
+        return fibonacci(n-1) + fibonacci(n-2)
+
+print(fibonacci(10))
+```
+
+**Stanford Alpaca**:
+
+```python
+numbers = [0, 1]
+
+for i in range(2, 11):
+    numbers.append(numbers[i-2] + numbers[i-1])
+
+print(numbers)
+# Output: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
+```
+
+**text-davinci-003**:
+
+```python
+# Fibonacci numbers are the numbers in the following integer sequence, called the Fibonacci sequence, and characterized by the fact that every number after the first two is the sum of the two preceding ones.
+
+# Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...
+
+# Program:
+
+# Initialize the first two numbers of the Fibonacci sequence
+a = 0
+b = 1
+
+# Print the first 10 numbers of the Fibonacci sequence
+print(a)
+print(b)
+
+for i in range(2,10):
+    c = a + b
+    a = b
+    b = c
+    print(c)
+```
+
+---
+
+**Instruction**: Write a program that prints the numbers from 1 to 100. But for multiples of three print 'Fizz' instead of the number and for the multiples of five print 'Buzz'. For numbers which are multiples of both three and five print 'FizzBuzz'.
+
+**Alpaca-LoRA**:
+
+```python
+for i in range(1, 101):
+    if i % 3 == 0 and i % 5 == 0:
+        print("FizzBuzz")
+    elif i % 3 == 0:
+        print("Fizz")
+    elif i % 5 == 0:
+        print("Buzz")
+    else:
+        print(i)
+```
+
+**Stanford Alpaca**:
+
+```python
+for i in range(1, 101):
+    if i % 3 == 0 and i % 5 == 0:
+        print('FizzBuzz')
+    elif i % 3 == 0:
+        print('Fizz')
+    elif i % 5 == 0:
+        print('Buzz')
+    else:
+        print(i)
+```
+
+**text-davinci-003**:
+
+```javascript
+for (let i = 1; i <= 100; i++) {
+  if (i % 3 === 0 && i % 5 === 0) {
+    console.log("FizzBuzz");
+  } else if (i % 3 === 0) {
+    console.log("Fizz");
+  } else if (i % 5 === 0) {
+    console.log("Buzz");
+  } else {
+    console.log(i);
+  }
+}
+```
+
+---
+
+**Instruction**: Tell me five words that rhyme with 'shock'.
+
+**Alpaca-LoRA**: Flock, lock, rock, stock, and sock.
+
+**Stanford Alpaca**: Five words that rhyme with shock are: rock, pop, shock, cook, and snock.
+
+**text-davinci-003**:
+
+1. Block
+2. Lock
+3. Dock
+4. Clock
+5. Rock
+
+---
+
+**Instruction**: Translate the sentence 'I have no mouth but I must scream' into Spanish.
+
+**Alpaca-LoRA**: No tengo boca pero tengo que gritar.
+
+**Stanford Alpaca**: No tengo boca, pero debo gritar.
+
+**text-davinci-003**: No tengo boca pero debo gritar.

alpaca-lora-main/alpaca_data.json

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+oid sha256:2eddafc6b977608d778aaab8dfc7e50e547b3af9826dfb9e909d9fc362e4a419
+size 22773992

alpaca-lora-main/alpaca_data_cleaned.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a1dc6937aadc538aef36f8abaa9abb01bb03adeb59cea28742c035955811dc0
+size 22764890

alpaca-lora-main/export_hf_checkpoint.py

@@ -0,0 +1,56 @@
+import os
+import json
+
+import torch
+from peft import PeftModel, LoraConfig
+
+import transformers
+
+assert (
+    "LlamaTokenizer" in transformers._import_structure["models.llama"]
+), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
+from transformers import LlamaTokenizer, LlamaForCausalLM
+
+tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
+
+base_model = LlamaForCausalLM.from_pretrained(
+    "decapoda-research/llama-7b-hf",
+    load_in_8bit=False,
+    torch_dtype=torch.float16,
+    device_map={"": "cpu"},
+)
+
+first_weight = base_model.model.layers[0].self_attn.q_proj.weight
+first_weight_old = first_weight.clone()
+
+lora_model = PeftModel.from_pretrained(
+    base_model,
+    "tloen/alpaca-lora-7b",
+    device_map={"": "cpu"},
+    torch_dtype=torch.float16,
+)
+
+lora_weight = lora_model.base_model.model.model.layers[0].self_attn.q_proj.weight
+
+assert torch.allclose(first_weight_old, first_weight)
+
+# merge weights
+for layer in lora_model.base_model.model.model.layers:
+    layer.self_attn.q_proj.merge_weights = True
+    layer.self_attn.v_proj.merge_weights = True
+
+lora_model.train(False)
+
+# did we do anything?
+assert not torch.allclose(first_weight_old, first_weight)
+
+lora_model_sd = lora_model.state_dict()
+deloreanized_sd = {
+    k.replace("base_model.model.", ""): v
+    for k, v in lora_model_sd.items()
+    if "lora" not in k
+}
+
+LlamaForCausalLM.save_pretrained(
+    base_model, "./hf_ckpt", state_dict=deloreanized_sd, max_shard_size="400MB"
+)

alpaca-lora-main/export_state_dict_checkpoint.py

@@ -0,0 +1,119 @@
+import os
+import json
+
+import torch
+from peft import PeftModel, LoraConfig
+
+import transformers
+
+assert (
+    "LlamaTokenizer" in transformers._import_structure["models.llama"]
+), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
+from transformers import LlamaTokenizer, LlamaForCausalLM
+
+tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
+
+base_model = LlamaForCausalLM.from_pretrained(
+    "decapoda-research/llama-7b-hf",
+    load_in_8bit=False,
+    torch_dtype=torch.float16,
+    device_map={"": "cpu"},
+)
+
+lora_model = PeftModel.from_pretrained(
+    base_model,
+    "tloen/alpaca-lora-7b",
+    device_map={"": "cpu"},
+    torch_dtype=torch.float16,
+)
+
+# merge weights
+for layer in lora_model.base_model.model.model.layers:
+    layer.self_attn.q_proj.merge_weights = True
+    layer.self_attn.v_proj.merge_weights = True
+
+lora_model.train(False)
+
+lora_model_sd = lora_model.state_dict()
+
+params = {
+    "dim": 4096,
+    "multiple_of": 256,
+    "n_heads": 32,
+    "n_layers": 32,
+    "norm_eps": 1e-06,
+    "vocab_size": -1,
+}
+n_layers = params["n_layers"]
+n_heads = params["n_heads"]
+dim = params["dim"]
+dims_per_head = dim // n_heads
+base = 10000.0
+inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+
+
+def permute(w):
+    return (
+        w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim)
+    )
+
+
+def unpermute(w):
+    return (
+        w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim)
+    )
+
+
+def translate_state_dict_key(k):
+    k = k.replace("base_model.model.", "")
+    if k == "model.embed_tokens.weight":
+        return "tok_embeddings.weight"
+    elif k == "model.norm.weight":
+        return "norm.weight"
+    elif k == "lm_head.weight":
+        return "output.weight"
+    elif k.startswith("model.layers."):
+        layer = k.split(".")[2]
+        if k.endswith(".self_attn.q_proj.weight"):
+            return f"layers.{layer}.attention.wq.weight"
+        elif k.endswith(".self_attn.k_proj.weight"):
+            return f"layers.{layer}.attention.wk.weight"
+        elif k.endswith(".self_attn.v_proj.weight"):
+            return f"layers.{layer}.attention.wv.weight"
+        elif k.endswith(".self_attn.o_proj.weight"):
+            return f"layers.{layer}.attention.wo.weight"
+        elif k.endswith(".mlp.gate_proj.weight"):
+            return f"layers.{layer}.feed_forward.w1.weight"
+        elif k.endswith(".mlp.down_proj.weight"):
+            return f"layers.{layer}.feed_forward.w2.weight"
+        elif k.endswith(".mlp.up_proj.weight"):
+            return f"layers.{layer}.feed_forward.w3.weight"
+        elif k.endswith(".input_layernorm.weight"):
+            return f"layers.{layer}.attention_norm.weight"
+        elif k.endswith(".post_attention_layernorm.weight"):
+            return f"layers.{layer}.ffn_norm.weight"
+        elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
+            return None
+        else:
+            print(layer, k)
+            raise NotImplementedError
+    else:
+        print(k)
+        raise NotImplementedError
+
+
+new_state_dict = {}
+for k, v in lora_model_sd.items():
+    new_k = translate_state_dict_key(k)
+    if new_k is not None:
+        if "wq" in new_k or "wk" in new_k:
+            new_state_dict[new_k] = unpermute(v)
+        else:
+            new_state_dict[new_k] = v
+
+os.makedirs("./ckpt", exist_ok=True)
+
+torch.save(new_state_dict, "./ckpt/consolidated.00.pth")
+
+with open("./ckpt/params.json", "w") as f:
+    json.dump(params, f)

alpaca-lora-main/finetune.py

@@ -0,0 +1,207 @@
+import os
+import sys
+
+import torch
+import torch.nn as nn
+import bitsandbytes as bnb
+from datasets import load_dataset
+import transformers
+
+assert (
+    "LlamaTokenizer" in transformers._import_structure["models.llama"]
+), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
+from transformers import LlamaForCausalLM, LlamaTokenizer
+from peft import (
+    prepare_model_for_int8_training,
+    LoraConfig,
+    get_peft_model,
+    get_peft_model_state_dict,
+)
+
+
+# optimized for RTX 4090. for larger GPUs, increase some of these?
+MICRO_BATCH_SIZE = 4  # this could actually be 5 but i like powers of 2
+BATCH_SIZE = 128
+GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
+EPOCHS = 3  # we don't always need 3 tbh
+LEARNING_RATE = 3e-4  # the Karpathy constant
+CUTOFF_LEN = 256  # 256 accounts for about 96% of the data
+LORA_R = 8
+LORA_ALPHA = 16
+LORA_DROPOUT = 0.05
+VAL_SET_SIZE = 2000
+TARGET_MODULES = [
+    "q_proj",
+    "v_proj",
+]
+DATA_PATH = "alpaca_data_cleaned.json"
+OUTPUT_DIR = "lora-alpaca"
+
+device_map = "auto"
+world_size = int(os.environ.get("WORLD_SIZE", 1))
+ddp = world_size != 1
+if ddp:
+    device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
+    GRADIENT_ACCUMULATION_STEPS = GRADIENT_ACCUMULATION_STEPS // world_size
+
+model = LlamaForCausalLM.from_pretrained(
+    "decapoda-research/llama-7b-hf",
+    load_in_8bit=True,
+    device_map=device_map,
+)
+tokenizer = LlamaTokenizer.from_pretrained(
+    "decapoda-research/llama-7b-hf", add_eos_token=True
+)
+
+model = prepare_model_for_int8_training(model)
+
+config = LoraConfig(
+    r=LORA_R,
+    lora_alpha=LORA_ALPHA,
+    target_modules=TARGET_MODULES,
+    lora_dropout=LORA_DROPOUT,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+model = get_peft_model(model, config)
+tokenizer.pad_token_id = 0  # unk. we want this to be different from the eos token
+data = load_dataset("json", data_files=DATA_PATH)
+
+
+def generate_prompt(data_point):
+    # sorry about the formatting disaster gotta move fast
+    if data_point["input"]:
+        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.
+
+### Instruction:
+{data_point["instruction"]}
+
+### Input:
+{data_point["input"]}
+
+### Response:
+{data_point["output"]}"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{data_point["instruction"]}
+
+### Response:
+{data_point["output"]}"""
+
+
+def tokenize(prompt):
+    # there's probably a way to do this with the tokenizer settings
+    # but again, gotta move fast
+    result = tokenizer(
+        prompt,
+        truncation=True,
+        max_length=CUTOFF_LEN + 1,
+        padding="max_length",
+    )
+    return {
+        "input_ids": result["input_ids"][:-1],
+        "attention_mask": result["attention_mask"][:-1],
+    }
+
+
+def generate_and_tokenize_prompt(data_point):
+    # This function masks out the labels for the input,
+    # so that our loss is computed only on the response.
+    user_prompt = (
+        (
+            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.
+
+### Instruction:
+{data_point["instruction"]}
+
+### Input:
+{data_point["input"]}
+
+### Response:
+"""
+        )
+        if data_point["input"]
+        else (
+            f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{data_point["instruction"]}
+
+### Response:
+"""
+        )
+    )
+    len_user_prompt_tokens = (
+        len(
+            tokenizer(
+                user_prompt,
+                truncation=True,
+                max_length=CUTOFF_LEN + 1,
+            )["input_ids"]
+        )
+        - 1
+    )  # no eos token
+    full_tokens = tokenizer(
+        user_prompt + data_point["output"],
+        truncation=True,
+        max_length=CUTOFF_LEN + 1,
+        padding="max_length",
+    )["input_ids"][:-1]
+    return {
+        "input_ids": full_tokens,
+        "labels": [-100] * len_user_prompt_tokens
+        + full_tokens[len_user_prompt_tokens:],
+        "attention_mask": [1] * (len(full_tokens)),
+    }
+
+
+if VAL_SET_SIZE > 0:
+    train_val = data["train"].train_test_split(
+        test_size=VAL_SET_SIZE, shuffle=True, seed=42
+    )
+    train_data = train_val["train"].shuffle().map(generate_and_tokenize_prompt)
+    val_data = train_val["test"].shuffle().map(generate_and_tokenize_prompt)
+else:
+    train_data = data['train'].shuffle().map(generate_and_tokenize_prompt)
+    val_data = None
+
+trainer = transformers.Trainer(
+    model=model,
+    train_dataset=train_data,
+    eval_dataset=val_data,
+    args=transformers.TrainingArguments(
+        per_device_train_batch_size=MICRO_BATCH_SIZE,
+        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
+        warmup_steps=100,
+        num_train_epochs=EPOCHS,
+        learning_rate=LEARNING_RATE,
+        fp16=True,
+        logging_steps=20,
+        evaluation_strategy="steps" if VAL_SET_SIZE > 0 else "no",
+        save_strategy="steps",
+        eval_steps=200 if VAL_SET_SIZE > 0 else None,
+        save_steps=200,
+        output_dir=OUTPUT_DIR,
+        save_total_limit=3,
+        load_best_model_at_end=True if VAL_SET_SIZE > 0 else False,
+        ddp_find_unused_parameters=False if ddp else None,
+    ),
+    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
+)
+model.config.use_cache = False
+
+old_state_dict = model.state_dict
+model.state_dict = (
+    lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
+).__get__(model, type(model))
+
+if torch.__version__ >= "2" and sys.platform != 'win32':
+    model = torch.compile(model)
+
+trainer.train()
+
+model.save_pretrained(OUTPUT_DIR)
+
+print("\n If there's a warning about missing keys above, please disregard :)")

alpaca-lora-main/generate.py

@@ -0,0 +1,161 @@
+import torch
+from peft import PeftModel
+import transformers
+import gradio as gr
+
+assert (
+    "LlamaTokenizer" in transformers._import_structure["models.llama"]
+), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
+from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
+
+tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
+
+BASE_MODEL = "decapoda-research/llama-7b-hf"
+LORA_WEIGHTS = "tloen/alpaca-lora-7b"
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+try:
+    if torch.backends.mps.is_available():
+        device = "mps"
+except:
+    pass
+
+if device == "cuda":
+    model = LlamaForCausalLM.from_pretrained(
+        BASE_MODEL,
+        load_in_8bit=True,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+    model = PeftModel.from_pretrained(model, LORA_WEIGHTS, torch_dtype=torch.float16)
+elif device == "mps":
+    model = LlamaForCausalLM.from_pretrained(
+        BASE_MODEL,
+        device_map={"": device},
+        torch_dtype=torch.float16,
+    )
+    model = PeftModel.from_pretrained(
+        model,
+        LORA_WEIGHTS,
+        device_map={"": device},
+        torch_dtype=torch.float16,
+    )
+else:
+    model = LlamaForCausalLM.from_pretrained(
+        BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
+    )
+    model = PeftModel.from_pretrained(
+        model,
+        LORA_WEIGHTS,
+        device_map={"": device},
+    )
+
+
+def generate_prompt(instruction, input=None):
+    if input:
+        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.
+
+### Instruction:
+{instruction}
+
+### Input:
+{input}
+
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:"""
+
+
+model.eval()
+if torch.__version__ >= "2":
+    model = torch.compile(model)
+
+
+def evaluate(
+    instruction,
+    input=None,
+    temperature=0.1,
+    top_p=0.75,
+    top_k=40,
+    num_beams=4,
+    max_new_tokens=128,
+    **kwargs,
+):
+    prompt = generate_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to(device)
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Response:")[1].strip()
+
+
+gr.Interface(
+    fn=evaluate,
+    inputs=[
+        gr.components.Textbox(
+            lines=2, label="Instruction", placeholder="Tell me about alpacas."
+        ),
+        gr.components.Textbox(lines=2, label="Input", placeholder="none"),
+        gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
+        gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
+        gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
+        gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"),
+        gr.components.Slider(
+            minimum=1, maximum=2000, step=1, value=128, label="Max tokens"
+        ),
+    ],
+    outputs=[
+        gr.inputs.Textbox(
+            lines=5,
+            label="Output",
+        )
+    ],
+    title="🦙🌲 Alpaca-LoRA",
+    description="Alpaca-LoRA is a 7B-parameter LLaMA model finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).",
+).launch()
+
+# Old testing code follows.
+
+
+if __name__ == "__main__":
+    # testing code for readme
+    for instruction in [
+        "Tell me about alpacas.",
+        "Tell me about the president of Mexico in 2019.",
+        "Tell me about the king of France in 2019.",
+        "List all Canadian provinces in alphabetical order.",
+        "Write a Python program that prints the first 10 Fibonacci numbers.",
+        "Write a program that prints the numbers from 1 to 100. But for multiples of three print 'Fizz' instead of the number and for the multiples of five print 'Buzz'. For numbers which are multiples of both three and five print 'FizzBuzz'.",
+        "Tell me five words that rhyme with 'shock'.",
+        "Translate the sentence 'I have no mouth but I must scream' into Spanish.",
+        "Count up from 1 to 500.",
+    ]:
+        print("Instruction:", instruction)
+        print("Response:", evaluate(instruction))
+        print()
+
+

alpaca-lora-main/lengths.ipynb

@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/eric/miniconda3/envs/dl3/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Found cached dataset json (/home/eric/.cache/huggingface/datasets/json/default-789f51900889f651/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n",
+      "100%|██████████| 1/1 [00:00<00:00, 784.28it/s]\n",
+      "Loading cached processed dataset at /home/eric/.cache/huggingface/datasets/json/default-789f51900889f651/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-f691ee34ec2034cb.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from transformers import LlamaTokenizer\n",
+    "\n",
+    "\n",
+    "tokenizer = LlamaTokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\", add_eos_token=True)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
+    "\n",
+    "data = load_dataset(\"json\", data_files=\"alpaca_data.json\")\n",
+    "\n",
+    "\n",
+    "def generate_prompt(data_point):\n",
+    "    # sorry about the formatting disaster gotta move fast\n",
+    "    if data_point[\"input\"]:\n",
+    "        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.\n",
+    "\n",
+    "### Instruction:\n",
+    "{data_point[\"instruction\"]}\n",
+    "\n",
+    "### Input:\n",
+    "{data_point[\"input\"]}\n",
+    "\n",
+    "### Response:\n",
+    "{data_point[\"output\"]}\"\"\"\n",
+    "    else:\n",
+    "        return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
+    "\n",
+    "### Instruction:\n",
+    "{data_point[\"instruction\"]}\n",
+    "\n",
+    "### Response:\n",
+    "{data_point[\"output\"]}\"\"\"\n",
+    "\n",
+    "\n",
+    "data = data.map(lambda data_point: {\"prompt\": tokenizer(generate_prompt(data_point))})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f6f1af20af0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "lens = [len(x[\"prompt\"][\"input_ids\"]) for x in data[\"train\"]]\n",
+    "plt.hist(lens, bins=100)\n",
+    "plt.title(\"Distribution of prompt lengths\")\n",
+    "plt.axvline(256, color=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f6eef316ce0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([len([l for l in lens if l <= m]) for m in range(max(lens) + 1)])\n",
+    "plt.title(\"Number of fully covered examples as a function of max length\")\n",
+    "plt.axvline(x=256, color=\"red\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Percentage of tokens left out:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f6eef392020>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([sum(min(l, m) for l in lens) for m in range(max(lens) + 1)])\n",
+    "plt.title(\"Token coverage as a function of max length\")\n",
+    "plt.axvline(x=256, color=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dl3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "90bfda469df5ac7fed8d7e225d563f60a7a7aa420ccfadb091c914debf775e49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

alpaca-lora-main/requirements.txt

@@ -0,0 +1,9 @@
+datasets
+loralib
+sentencepiece
+git+https://github.com/huggingface/transformers.git
+accelerate
+bitsandbytes
+git+https://github.com/huggingface/peft.git
+gradio
+appdirs