Update README.md

README.md

@@ -7,4 +7,149 @@ datasets:
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 ## Model description
 
-This is a base Yi-34B-200K XLCTX model treated with DPO with adamo1139/rawrr_v2-2_stage1 dataset to make outputs be completions instead of answers for a question. DPO was done using chatml format but no previous SFT step was done. If it would do it now, I would have used ORPO instead of DPO for this step to make it stronger, but too late for that. It can be used to maybe slightly decensor a model, but I don't think this idea works too well with DPO before SFT step, as was widely known but I did it anyway.
+This is a base Yi-34B-200K XLCTX model treated with DPO with adamo1139/rawrr_v2-2_stage1 dataset to make outputs be completions instead of answers for a question. DPO was done using chatml format but no previous SFT step was done. If it would do it now, I would have used ORPO instead of DPO for this step to make it stronger, but too late for that. It can be used to maybe slightly decensor a model, but I don't think this idea works too well with DPO before SFT step, as was widely known but I did it anyway.
+
+
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" alt="made with Unsloth" width="400" height="64"/>](https://github.com/unslothai/unsloth)
+
+## Training script for Unsloth
+
+```
+from unsloth import FastLanguageModel
+from datasets import Dataset, load_dataset
+from dataclasses import dataclass, field
+from typing import Dict, Optional
+import torch
+max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "adamo1139/Yi-34B-200K-XLCTX", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2
+    max_seq_length = max_seq_length,
+    attn_implementation="flash_attention_2",
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+
+
+#@title Alignment Handbook utils
+import os
+import re
+from typing import List, Literal, Optional
+
+from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk
+from datasets.builder import DatasetGenerationError
+
+
+#DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
+tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+
+
+EOS_TOKEN = tokenizer.eos_token
+
+def chatml_format(example):
+    # Format system
+    if len(example['system']) > 0:
+        message = {"role": "system", "content": example['system']}
+        system = tokenizer.apply_chat_template([message], tokenize=False)
+    else:
+        system = ""
+
+    # Format instruction
+    message = {"role": "user", "content": example['prompt']}
+    prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True)
+
+    # Format chosen answer
+    chosen = example['chosen'] + "<|im_end|>\n" + EOS_TOKEN
+
+    # Format rejected answer
+    rejected = example['rejected'] + "<|im_end|>\n" + EOS_TOKEN
+
+    return {
+        "prompt": system + prompt,
+        "chosen": chosen,
+        "rejected": rejected,
+    }
+
+# Load dataset
+dataset = load_dataset("adamo1139/rawrr_v2-2_stage1", split="train")
+
+import pprint
+pprint.pprint("""NOT a formatted dataset
+""")
+pprint
+pprint.pprint(dataset[250])
+pprint.pprint(dataset[260])
+pprint.pprint(dataset[270])
+pprint.pprint(dataset[280])
+pprint.pprint(dataset[290])
+# Save columns
+original_columns = dataset.column_names
+
+# Format dataset
+dataset = dataset.map(
+    chatml_format,
+    remove_columns=original_columns
+)
+
+# Print sample
+pprint.pprint("""formatted dataset""")
+pprint.pprint(dataset[250])
+pprint.pprint(dataset[260])
+pprint.pprint(dataset[270])
+pprint.pprint(dataset[280])
+pprint.pprint(dataset[290])
+
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 32,
+    lora_dropout = 0, # Currently only supports dropout = 0
+    bias = "none",    # Currently only supports bias = "none"
+    use_gradient_checkpointing = "unsloth",
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments
+from trl import DPOTrainer
+
+dpo_trainer = DPOTrainer(
+    model = model,
+    ref_model = None,
+    args = TrainingArguments(
+        per_device_train_batch_size = 1,
+        gradient_accumulation_steps = 16,
+        warmup_ratio = 0.03,
+        num_train_epochs = 1,
+        learning_rate = 0.0001,
+        fp16 = not torch.cuda.is_bf16_supported(),
+        bf16 = torch.cuda.is_bf16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.0,
+        lr_scheduler_type = "cosine",
+        seed = 42,
+        save_strategy = "steps",
+        save_steps = 100,
+        save_total_limit = 20,
+        output_dir = "1904-yi-200k-xlctx-raw-intermediate",
+    ),
+    beta = 0.1,
+    train_dataset = dataset,
+    # eval_dataset = raw_datasets["test"],
+    tokenizer = tokenizer,
+    max_length = 650,
+    max_prompt_length = 650,
+)
+dpo_trainer.train()
+model.save_pretrained("1904-yi-200k-xlctx-raw-final") # Local saving
+```