Update README.md

README.md

@@ -1,8 +1,8 @@
 ---
 tags:
-- generated_from_trainer
 - code
 - coding
+- python
 - llama-2
 - gptq
 model-index:
@@ -34,83 +34,20 @@ Model Architecture Llama 2 is an auto-regressive language model that uses an opt
 
 The dataset contains problem descriptions and code in python language. This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style.
 
-### Training hyperparameters
-
-The following `bitsandbytes` quantization config was used during training:
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: float16
-
-**SFTTrainer arguments**
-```py
-    # Number of training epochs
-    num_train_epochs = 1
-    # Enable fp16/bf16 training (set bf16 to True with an A100)
-    fp16 = False
-    bf16 = True
-    # Batch size per GPU for training
-    per_device_train_batch_size = 4
-    # Number of update steps to accumulate the gradients for
-    gradient_accumulation_steps = 1
-    # Enable gradient checkpointing
-    gradient_checkpointing = True
-    # Maximum gradient normal (gradient clipping)
-    max_grad_norm = 0.3
-    # Initial learning rate (AdamW optimizer)
-    learning_rate = 2e-4
-    # Weight decay to apply to all layers except bias/LayerNorm weights
-    weight_decay = 0.001
-    # Optimizer to use
-    optim = "paged_adamw_32bit"
-    # Learning rate schedule
-    lr_scheduler_type = "cosine" #"constant"
-    # Ratio of steps for a linear warmup (from 0 to learning rate)
-    warmup_ratio = 0.03
-```
 ### Framework versions
 - PEFT 0.4.0
 
-### Training metrics
-```
-{'loss': 1.044, 'learning_rate': 3.571428571428572e-05, 'epoch': 0.01}
-{'loss': 0.8413, 'learning_rate': 7.142857142857143e-05, 'epoch': 0.01}
-{'loss': 0.7299, 'learning_rate': 0.00010714285714285715, 'epoch': 0.02}
-{'loss': 0.6593, 'learning_rate': 0.00014285714285714287, 'epoch': 0.02}
-{'loss': 0.6309, 'learning_rate': 0.0001785714285714286, 'epoch': 0.03}
-{'loss': 0.5916, 'learning_rate': 0.00019999757708974043, 'epoch': 0.03}
-{'loss': 0.5861, 'learning_rate': 0.00019997032069768138, 'epoch': 0.04}
-{'loss': 0.6118, 'learning_rate': 0.0001999127875580558, 'epoch': 0.04}
-{'loss': 0.5928, 'learning_rate': 0.00019982499509519857, 'epoch': 0.05}
-{'loss': 0.5978, 'learning_rate': 0.00019970696989770335, 'epoch': 0.05}
-{'loss': 0.5791, 'learning_rate': 0.0001995587477103701, 'epoch': 0.06}
-{'loss': 0.6054, 'learning_rate': 0.00019938037342337933, 'epoch': 0.06}
-{'loss': 0.5864, 'learning_rate': 0.00019917190105869708, 'epoch': 0.07}
-{'loss': 0.6159, 'learning_rate': 0.0001989333937537136, 'epoch': 0.08}
-{'loss': 0.583, 'learning_rate': 0.00019866492374212205, 'epoch': 0.08}
-{'loss': 0.6066, 'learning_rate': 0.00019836657233204182, 'epoch': 0.09}
-{'loss': 0.5934, 'learning_rate': 0.00019803842988139374, 'epoch': 0.09}
-{'loss': 0.5836, 'learning_rate': 0.00019768059577053473, 'epoch': 0.1}
-{'loss': 0.6021, 'learning_rate': 0.00019729317837215943, 'epoch': 0.1}
-{'loss': 0.5659, 'learning_rate': 0.00019687629501847898, 'epoch': 0.11}
-{'loss': 0.5754, 'learning_rate': 0.00019643007196568606, 'epoch': 0.11}
-{'loss': 0.5936, 'learning_rate': 0.000195954644355717, 'epoch': 0.12}
-```
 
 ### Example of usage
 
 ```py
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = "edumunozsala/llama-2-7b-int4-python-code-20k"
+model_id = "NurtureAI/llama-2-7b-int4-gptq-python"
 tokenizer = AutoTokenizer.from_pretrained(hf_model_repo)
-model = AutoModelForCausalLM.from_pretrained(hf_model_repo, load_in_4bit=True, torch_dtype=torch.float16, 
-                                             device_map=device_map)
+model = AutoModelForCausalLM.from_pretrained(
+  hf_model_repo, load_in_4bit=True,
+  torch_dtype=torch.float16, device_map=device_map)
 instruction="Write a Python function to display the first and last elements of a list."
 input=""
 prompt = f"""### Instruction:
@@ -131,11 +68,11 @@ print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().n
 ### Citation
 
 ```
-@misc {edumunozsala_2023,
-	author       = { {Eduardo Muñoz} },
-	title        = { llama-2-7b-int4-python-coder },
+@misc {NurtureAI,
+	author       = { {Raymond Hernandez} },
+	title        = { NurtureAI/llama-2-7b-int4-gptq-python },
 	year         = 2023,
-	url          = { https://huggingface.co/edumunozsala/llama-2-7b-int4-python-18k-alpaca },
+	url          = { https://huggingface.co/NurtureAI/llama-2-7b-int4-gptq-python },
 	publisher    = { Hugging Face }
 }
 ```