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Model: ShahriarFerdoush/llama-3.2-1b-code-instruct
Source: Original Platform

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README.md

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+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- sahil2801/CodeAlpaca-20k
+base_model:
+- meta-llama/Llama-3.2-1B
+---
+
+
+# 🧠 Llama-3.2-1B Code Solver (QLoRA Fine-Tuned)
+
+A lightweight yet powerful **code-focused language model** fine-tuned from **Meta Llama-3.2-1B** using **QLoRA (4-bit)** on the **CodeAlpaca-20K** dataset.  
+Designed for **efficient code generation, reasoning, and problem-solving** on limited GPU resources.
+
+> 🚀 Trained on a single Tesla P100 GPU  
+> ⚡ Optimized for Kaggle, Colab, and low-VRAM environments  
+> 🧩 Ideal for research, education, and rapid prototyping
+
+---
+
+## 🔍 Model Overview
+
+| Attribute | Value |
+|---------|------|
+| **Base Model** | `meta-llama/Llama-3.2-1B` |
+| **Model Type** | Decoder-only causal language model |
+| **Fine-Tuning Method** | QLoRA (4-bit quantization + LoRA) |
+| **LoRA Rank** | 16 |
+| **Task Domain** | Code generation & code reasoning |
+| **Training Samples** | 10,000 |
+| **Training Time** | ~5 hours |
+| **Hardware** | NVIDIA Tesla P100 |
+| **Precision** | 4-bit (NF4) |
+| **Frameworks** | Hugging Face Transformers, PEFT, BitsAndBytes |
+
+---
+
+## 🎯 What This Model Is Good At
+
+- 🧑‍💻 Code generation (Python-focused, but generalizable)
+- 🧠 Step-by-step coding reasoning
+- 🧪 Algorithmic problem solving
+- 📘 Educational coding assistance
+- ⚙️ Running efficiently on **low-VRAM GPUs**
+
+---
+
+## 📚 Training Dataset
+
+### **CodeAlpaca-20K**
+
+A high-quality instruction-tuning dataset derived from the Alpaca format and specialized for coding tasks.
+
+- **Total dataset size**: 20,000 samples  
+- **Used for training**: 10,000 samples (50%)
+- **Data format**:
+  ```json
+  {
+    "instruction": "Describe the coding task",
+    "input": "Optional context or input code",
+    "output": "Expected code solution"
+  }
+  ```
+
+* **Task Types**:
+
+  * Algorithm implementation
+  * Code completion
+  * Debugging
+  * Function writing
+  * Problem solving
+
+---
+
+## 🏗️ Training Methodology
+
+This model was fine-tuned using **QLoRA**, enabling efficient adaptation of large language models on limited hardware.
+
+### Key Techniques Used
+
+* **4-bit Quantization (NF4)** via BitsAndBytes
+* **LoRA adapters** applied to attention layers
+* **Frozen base model weights**
+* **Low-rank updates only**
+
+### Why QLoRA?
+
+* 🔻 Drastically reduces GPU memory usage
+* ⚡ Enables training on consumer-grade GPUs
+* 📈 Maintains strong downstream performance
+
+---
+
+## ⚙️ Training Configuration
+
+| Parameter             | Value                   |
+| --------------------- | ----------------------- |
+| Max Sequence Length   | 1024                    |
+| LoRA Rank (r)         | 16                      |
+| LoRA Alpha            | 32                      |
+| LoRA Dropout          | 0.05                    |
+| Optimizer             | AdamW                   |
+| Learning Rate         | 2e-4                    |
+| Batch Size            | Small (GPU-constrained) |
+| Gradient Accumulation | Enabled                 |
+| Quantization          | 4-bit                   |
+
+---
+
+## 🚀 Usage
+
+### Load the Model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "YOUR_USERNAME/llama-3.2-1b-code-solver"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    load_in_4bit=True
+)
+```
+
+### Example Inference
+
+```python
+prompt = "Write a Python function to check if a number is prime."
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=200)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+
+## 🧪 Evaluation Notes
+
+* This model is **instruction-tuned**, not benchmark-optimized
+* No formal benchmarks (HumanEval / MBPP) were run
+* Best evaluated through **qualitative code generation**
+
+## ⚠️ Limitations
+
+* 1B parameters → limited long-context reasoning
+* Not optimized for natural language chat
+* May hallucinate on complex or ambiguous prompts
+* English-centric training data
+
+
+## 🧭 Intended Use
+
+✅ **Allowed**
+
+* Research and experimentation
+* Coding assistants
+* Educational tools
+* Prototyping LLM systems
+
+
+## 🙏 Acknowledgements
+
+* **Meta AI** for Llama 3.2
+* **CodeAlpaca** dataset creators
+* **Hugging Face** ecosystem
+* **QLoRA & PEFT** authors

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": 128001,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|end_of_text|>"
+}

tokenizer.json

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