mingyue0101
/

codellama-7b-matplotlib-assistant

@@ -1,204 +1,118 @@
 ---
 library_name: peft
 base_model: codellama/CodeLlama-7b-Instruct-hf
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
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-### Framework versions
-- PEFT 0.10.0

 ---
 library_name: peft
 base_model: codellama/CodeLlama-7b-Instruct-hf
+tags:
+- instruction-tuning
+- qlora
+- code-llama
+- text-generation
+language:
+- en
+datasets:
+- mingyue0101/prompt_code_parquet
+- mingyue0101/prompts_modi
 ---
+# Model Card for super-cool-instruct
+This model is a fine-tuned version of `codellama/CodeLlama-7b-Instruct-hf` designed to enhance instruction-following capabilities. It was developed as part of a Master's thesis project.
 ## Model Details
 ### Model Description
+The `super-cool-instruct` model is a large language model fine-tuned using the QLoRA (4-bit Quantization + LoRA) technique. The goal of this model was to adapt the base CodeLlama model to better follow user instructions while maintaining its coding and reasoning capabilities.
+- **Developed by:** mingyue0101
+- **Model type:** Causal Language Model (Fine-tuned with PEFT/LoRA)
+- **Language(s) (NLP):** English, Chinese
+- **License:** Apache-2.0 (inherited from CodeLlama)
+- **Finetuned from model:** codellama/CodeLlama-7b-Instruct-hf
+### Model Sources
+- **Repository:** https://huggingface.co/mingyue0101/super-cool-instruct
+- **Dataset:** https://huggingface.co/datasets/mingyue0101/parquet02
 ## Uses
 ### Direct Use
+The model can be used for text generation, code assistance, and general-purpose instruction following. It is particularly suited for tasks where a balance of technical coding knowledge and conversational instruction following is required.
 ### Out-of-Scope Use
+The model should not be used for high-stakes decision-making, generating malicious code, or any application that violates the safety guidelines of the base CodeLlama model.
 ## Bias, Risks, and Limitations
+This model may inherit biases present in the training data or the base model. Since it was fine-tuned on a specific dataset (`parquet02`), it might exhibit limitations when handling domains outside of its training distribution. Users should expect potential hallucinations in complex reasoning tasks.
 ### Recommendations
+Users are encouraged to use safety filters when deploying this model in production and to perform domain-specific evaluation before use.
 ## How to Get Started with the Model
+Use the code below to load the model in 4-bit precision:
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+model_id = "codellama/CodeLlama-7b-Instruct-hf"
+peft_model_id = "mingyue0101/super-cool-instruct"
+# Load 4-bit configuration
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+# Load base model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+# Load the fine-tuned adapter
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+# Inference
+prompt = "Write a Python function to sort a list."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 ## Training Details
 ### Training Data
+The model was trained on the `mingyue0101/parquet02` dataset. This dataset contains instruction-response pairs formatted for Supervised Fine-Tuning (SFT).
+### Training Procedure
+**Training Hyperparameters**
+- Training regime: QLoRA 4-bit (NF4) mixed precision (fp16)
+- Learning rate: 2e-4
+- Optimizer: paged_adamw_32bit
+- Batch size: 4
+- Epochs: 1
+- LoRA Rank (r): 64
+- LoRA Alpha: 16
+- LoRA Dropout: 0.1
+- LR Scheduler: constant
+- Warmup Ratio: 0.03
+## Technical Specifications
 ### Model Architecture and Objective
+Based on the Llama 2 architecture, this model utilizes grouped-query attention (GQA) and rotary positional embeddings (RoPE), fine-tuned with a causal language modeling objective.
 ### Compute Infrastructure
+### Software
+- PEFT 0.10.0
+- Transformers
+- Bitsandbytes
+- TRL (SFTTrainer)