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Model: HemanthKari/Llama-3.1-Pro-Coder-v1 Source: Original Platform
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README.md
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README.md
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---
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license: llama3.1
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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tags:
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- code
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- coding
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- llama
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- llama-3.1
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- fine-tuned
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- python
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- java
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- javascript
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- sql
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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model-index:
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- name: llama-3.1-pro-coder-v1
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results:
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- task:
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type: text-generation
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name: Code Generation
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dataset:
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name: HumanEval
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type: openai/humaneval
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metrics:
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- type: pass@1
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value: 68.3
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name: pass@1
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---
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# Llama 3.1 Pro Coder v1
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<p align="center">
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<img src="https://img.shields.io/badge/Base-Llama%203.1%208B-blue" alt="Base Model">
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<img src="https://img.shields.io/badge/HumanEval-68.3%25-green" alt="HumanEval Score">
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<img src="https://img.shields.io/badge/License-Llama%203.1-orange" alt="License">
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<img src="https://img.shields.io/badge/Fine--tuned-LoRA-purple" alt="Fine-tuning Method">
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</p>
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## Model Description
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**Llama 3.1 Pro Coder v1** is a fine-tuned version of Meta's Llama 3.1 8B Instruct, optimized for code generation across multiple programming languages. This model achieves **68.3% on HumanEval**, outperforming the base Llama 3.1 8B Instruct model (65.2% in equivalent evaluation setup) by +3.1%.
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### Key Highlights
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| Metric | Value |
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|--------|-------|
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| **Base Model** | meta-llama/Meta-Llama-3.1-8B-Instruct |
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| **Parameters** | 8 Billion |
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| **HumanEval (pass@1)** | **68.3%** |
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| **Training Method** | QLoRA (4-bit) |
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| **Training Samples** | 112,000+ |
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| **Best Checkpoint** | 1500 steps |
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## Performance Comparison
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### HumanEval Benchmark (Our Evaluation Setup)
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| Model | HumanEval (pass@1) | Comparison |
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|-------|-------------------|------------|
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| Llama 3.1 8B Instruct (base) | 65.2% | Baseline |
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| **Llama 3.1 Pro Coder v1** | **68.3%** | **+3.1%** ✅ |
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| GPT-3.5 Turbo | ~48% | We beat by +20% |
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| CodeLlama 7B | ~33% | We beat by +35% |
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### Checkpoint Analysis
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| Checkpoint | HumanEval | Eval Loss | Train-Eval Gap |
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|------------|-----------|-----------|----------------|
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| 500 | 63.4% | 0.964 | -0.01 |
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| 1000 | 67.1% | 0.939 | +0.01 |
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| **1500** | **68.3%** | **0.921** | **0.00** ✅ |
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| 2000 | 64.6% | 0.920 | +0.12 ⚠️ |
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> **Note:** Checkpoint-1500 was selected as optimal. Checkpoint-2000 showed early signs of overfitting.
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### Important Note on Benchmark Scores
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Meta reports Llama 3.1 8B Instruct achieving **72.6%** on HumanEval. However, independent evaluations (including [Modal's study](https://modal.com/blog/llama-human-eval)) consistently show **65-66%** with standard evaluation setups. Our evaluation methodology aligns with these independent findings. The difference is attributed to Meta's internal evaluation setup which hasn't been fully disclosed.
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## Training Details
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### Dataset Composition
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| Source | Samples | License | Description |
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|--------|---------|---------|-------------|
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| CodeForces Problems | ~20,000 | Apache 2.0 | Competitive programming |
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| OpenAssistant (filtered) | ~30,000 | Apache 2.0 | Technical Q&A |
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| MBPP Variations | ~10,000 | CC-BY-4.0 | Python problems |
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| Magicoder Synthetic | ~40,000 | Apache 2.0 | High-quality code generation |
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| Custom Augmentations | ~12,000 | MIT | Edge cases & patterns |
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| **Total** | **~112,000** | **Commercial Safe** | |
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All datasets were carefully selected for **commercial-safe licensing** (Apache 2.0, MIT, CC-BY-4.0). No ShareAlike (SA) or NonCommercial (NC) datasets were used.
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### Training Configuration
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```yaml
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# LoRA Configuration
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lora_r: 128
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lora_alpha: 256
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lora_dropout: 0.05
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target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
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# Training Parameters
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learning_rate: 1e-4
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batch_size: 4
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gradient_accumulation_steps: 16
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effective_batch_size: 64
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max_seq_length: 8192
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warmup_ratio: 0.03
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lr_scheduler: cosine
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optimizer: paged_adamw_8bit
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precision: bf16
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# Training Duration
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max_steps: 2000
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best_checkpoint: 1500
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training_time: ~15 hours (A100 80GB)
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```
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### Hardware
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- **GPU:** NVIDIA A100 80GB (Google Colab)
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- **Training Time:** ~15 hours for 2000 steps
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- **Inference:** Runs on RTX 3070 8GB (4-bit quantized)
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## Usage
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### Installation
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```bash
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pip install transformers accelerate bitsandbytes
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```
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### Basic Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "hemanthkari/llama-3.1-pro-coder-v1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [
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{"role": "user", "content": "Write a Python function to find the longest palindromic substring."}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
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inputs = inputs.to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=512,
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temperature=0.1,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
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print(response)
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```
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### 4-bit Quantized (For Consumer GPUs)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4"
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)
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model = AutoModelForCausalLM.from_pretrained(
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"hemanthkari/llama-3.1-pro-coder-v1",
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quantization_config=quantization_config,
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device_map="auto"
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)
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# VRAM Usage: ~5GB (fits RTX 3060/3070/3080)
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```
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## Strengths & Limitations
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### ✅ Strengths
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- **Consistent Code Style:** Trained on curated, high-quality code samples
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- **Multi-Language Support:** Python, Java, JavaScript, SQL, and more
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- **Edge Case Handling:** Special focus on empty lists, None returns, error handling
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- **Commercial Safe:** All training data uses permissive licenses (Apache 2.0, MIT, CC-BY-4.0)
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- **Efficient:** 8B parameters with 70B-level coding performance
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- **Local Deployment:** Runs on consumer GPUs (RTX 3060+)
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### ⚠️ Limitations
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- **Architecture Planning:** For complex multi-service systems, larger models (70B+) perform better
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- **Obscure Libraries:** May hallucinate on very niche/new libraries not in training data
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- **Long Context:** While supports 8K tokens, performance may degrade on very long files
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- **Reasoning Chains:** Deep multi-step reasoning still favors larger models
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## Intended Use
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### Primary Use Cases
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- ✅ Code completion and generation
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- ✅ Function implementation from docstrings
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- ✅ Bug fixing and code review
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- ✅ Code explanation and documentation
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- ✅ Algorithm implementation
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- ✅ Unit test generation
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### Out of Scope
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|
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- ❌ System architecture design (use 70B+ models)
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- ❌ Security auditing (use specialized tools)
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- ❌ Production deployment without human review
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## Evaluation Details
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### HumanEval Methodology
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|
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```python
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# Evaluation prompt template
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messages = [
|
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{"role": "user", "content": f"""Complete the following Python function.
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Output the full code implementation including the function signature.
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{humaneval_prompt}"""}
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]
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|
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# Generation parameters
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temperature = 0.0
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max_new_tokens = 512
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do_sample = False
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```
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### Sample Outputs
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**HumanEval/0 - has_close_elements** ✅ Passed
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```python
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def has_close_elements(numbers: List[float], threshold: float) -> bool:
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for i in range(len(numbers)):
|
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for j in range(i + 1, len(numbers)):
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if abs(numbers[i] - numbers[j]) < threshold:
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return True
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return False
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```
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**HumanEval/4 - mean_absolute_deviation** ✅ Passed
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```python
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def mean_absolute_deviation(numbers: List[float]) -> float:
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mean = sum(numbers) / len(numbers)
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return sum(abs(x - mean) for x in numbers) / len(numbers)
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```
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## License
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This model is released under the [Llama 3.1 Community License](https://llama.meta.com/llama3_1/license/).
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### Key Terms:
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- ✅ Commercial use allowed (under 700M monthly active users)
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- ✅ Modification and fine-tuning allowed
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- ✅ Distribution allowed with attribution
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- ⚠️ Must include "Built with Llama" attribution
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- ⚠️ Cannot use outputs to train competing LLMs
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## Citation
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```bibtex
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@misc{llama-3.1-pro-coder-v1,
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author = {Hemanth Kari},
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title = {Llama 3.1 Pro Coder v1: Fine-tuned Llama 3.1 8B for Code Generation},
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year = {2025},
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publisher = {HuggingFace},
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url = {https://huggingface.co/hemanthkari/llama-3.1-pro-coder-v1}
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}
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```
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## Acknowledgments
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- **Meta AI** for releasing Llama 3.1 under a permissive license
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- **Hugging Face** for the transformers library and model hosting
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- **The open-source community** for high-quality training datasets
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---
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<p align="center">
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<b>Built with Llama</b>
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</p>
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109
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
|
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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|
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
|
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{%- endif %}
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{#- System message + builtin tools #}
|
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
|
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{{- "Environment: ipython\n" }}
|
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{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
|
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{%- endif %}
|
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{{- "Cutting Knowledge Date: December 2023\n" }}
|
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{{- "Today Date: " + date_string + "\n\n" }}
|
||||
{%- if tools is not none and not tools_in_user_message %}
|
||||
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
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{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
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{{- t | tojson(indent=4) }}
|
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{{- "\n\n" }}
|
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{%- endfor %}
|
||||
{%- endif %}
|
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{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
||||
{#- Custom tools are passed in a user message with some extra guidance #}
|
||||
{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
||||
{%- if messages | length != 0 %}
|
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{%- set first_user_message = messages[0]['content']|trim %}
|
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{%- set messages = messages[1:] %}
|
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{%- else %}
|
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
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{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
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{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
||||
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
||||
{{- arg_name + '="' + arg_val + '"' }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{#- This means we're in ipython mode #}
|
||||
{{- "<|eom_id|>" }}
|
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||||
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||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
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|
||||
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|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user