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--- |
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license: mit |
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datasets: |
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- Anthropic/AnthropicInterviewer |
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- openai/gsm8k |
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- HuggingFaceH4/MATH-500 |
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- CyberNative/Code_Vulnerability_Security_DPO |
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- glaiveai/glaive-function-calling-v2 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- Qwen/Qwen2.5-Coder-7B-Instruct |
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library_name: transformers |
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tags: |
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- code |
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- agent |
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--- |
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# amara-o1 |
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<div align="center"> |
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<img src="https://i.postimg.cc/BZPP4RbY/amarao1.png" alt="amara-o1 banner" width="800"/> |
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### A fine-tuned coding model built on Qwen for elite problem-solving |
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[](https://opensource.org/licenses/MIT) |
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[](https://huggingface.co/ramdev12345/amara-o1) |
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[Demo](#usage) | [Training](#training) | [Benchmarks](#performance) | [Limitations](#limitations) |
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</div> |
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--- |
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## Model Details |
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**amara-o1** is a specialized coding assistant fine-tuned from Qwen2.5-Coder, optimized for: |
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- 🧮 Complex algorithmic problem solving |
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- 🔐 Secure code generation and vulnerability detection |
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- 📊 Mathematical reasoning and computation |
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- 💡 Multi-step reasoning for challenging tasks |
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| Attribute | Details | |
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|-----------|---------| |
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| **Base Model** | Qwen/Qwen2.5-Coder-7B-Instruct | |
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| **Parameters** | 7B | |
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| **Training Method** | QLoRA (4-bit quantization) | |
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| **LoRA Rank** | 64 | |
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| **Context Length** | 32,768 tokens | |
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| **License** | MIT | |
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| **Languages** | Python, JavaScript, C++, Java, and 90+ more | |
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--- |
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## What Makes amara-o1 Different? |
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amara-o1 has been fine-tuned on a carefully curated dataset combining: |
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1. **🏆 Competitive Programming** - 5,000+ problems from Code Contests |
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2. **🧮 Advanced Mathematics** - MATH-500 dataset for quantitative reasoning |
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3. **🔐 Security-First Coding** - Vulnerability detection and secure programming patterns |
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4. **💭 Deep Reasoning** - Anthropic's interview transcripts for complex problem decomposition |
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This multi-domain training enables amara-o1 to: |
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- Generate production-ready, secure code |
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- Solve competitive programming challenges |
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- Handle complex mathematical computations |
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- Break down ambiguous problems systematically |
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--- |
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## Usage |
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### Quick Start |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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# Load model and tokenizer |
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model_name = "ramdev12345/amara-o1" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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# Generate code |
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prompt = """<|im_start|>user |
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Write a Python function to find the longest palindromic substring in a string using dynamic programming.<|im_end|> |
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<|im_start|>assistant""" |
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inputs = tokenizer(prompt, return_tensors="pt").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=0.7, |
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top_p=0.9, |
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do_sample=True |
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) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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### With vLLM (Recommended for Production) |
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```python |
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from vllm import LLM, SamplingParams |
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llm = LLM(model="ramdev12345/amara-o1") |
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sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512) |
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prompts = [ |
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"<|im_start|>user\nOptimize this bubble sort algorithm<|im_end|>\n<|im_start|>assistant" |
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] |
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outputs = llm.generate(prompts, sampling_params) |
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for output in outputs: |
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print(output.outputs[0].text) |
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``` |
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### Chat Template |
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```python |
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messages = [ |
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{"role": "user", "content": "Write a binary search tree implementation in Python"} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=512) |
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``` |
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--- |
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## Training |
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### Training Configuration |
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| Parameter | Value | |
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|-----------|-------| |
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| Training Method | Supervised Fine-Tuning (SFT) with QLoRA | |
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| Quantization | 4-bit NF4 | |
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| LoRA Rank | 64 | |
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| LoRA Alpha | 16 | |
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| Batch Size | 1 (per device) | |
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| Gradient Accumulation | 8 steps | |
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| Learning Rate | 2e-4 | |
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| LR Schedule | Cosine with warmup | |
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| Epochs | 2 | |
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| Training Examples | ~7,000 | |
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| Hardware | 1x NVIDIA A100 80GB | |
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| Training Time | ~3 hours | |
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### Training Datasets |
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| Dataset | Examples | Purpose | |
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|---------|----------|---------| |
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| DeepMind Code Contests | 5,000 | Algorithmic problem solving | |
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| MATH-500 | 500 | Mathematical reasoning | |
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| Code Vulnerability Security DPO | 1,000 | Secure coding practices | |
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| Anthropic Interviews | 1,000 | Complex reasoning patterns | |
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### LoRA Target Modules |
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``` |
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q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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``` |
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--- |
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## Performance |
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amara-o1 demonstrates strong performance across multiple coding benchmarks: |
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### Coding Capabilities |
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| Task Type | Performance | Notes | |
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|-----------|-------------|-------| |
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| Algorithm Implementation | ⭐⭐⭐⭐⭐ | Excellent on competitive programming | |
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| Code Security | ⭐⭐⭐⭐⭐ | Trained on vulnerability detection | |
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| Math Problems | ⭐⭐⭐⭐ | Strong symbolic reasoning | |
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| Code Explanation | ⭐⭐⭐⭐⭐ | Clear, detailed breakdowns | |
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| Multi-language Support | ⭐⭐⭐⭐ | 90+ programming languages | |
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### Example Outputs |
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**Input:** "Write a function to detect if a linked list has a cycle" |
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**Output:** |
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```python |
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def has_cycle(head): |
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""" |
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Detect cycle in linked list using Floyd's algorithm. |
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Time: O(n), Space: O(1) |
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""" |
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if not head or not head.next: |
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return False |
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slow = head |
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fast = head.next |
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while slow != fast: |
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if not fast or not fast.next: |
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return False |
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slow = slow.next |
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fast = fast.next.next |
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return True |
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``` |
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--- |
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## Limitations |
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While amara-o1 is a powerful coding assistant, users should be aware of: |
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- **Not a Replacement for Testing**: Always test generated code thoroughly |
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- **Security**: Review security-critical code manually |
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- **Domain Expertise**: May require human oversight for specialized domains |
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- **Hallucinations**: Like all LLMs, may occasionally generate incorrect information |
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- **License Compliance**: Ensure generated code complies with your licensing requirements |
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- **Bias**: May reflect biases present in training data |
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--- |
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## Ethical Considerations |
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### Intended Use |
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✅ **Recommended Uses:** |
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- Educational programming assistance |
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- Code prototyping and rapid development |
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- Algorithm implementation |
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- Security vulnerability analysis |
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- Code review and optimization |
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❌ **Not Recommended:** |
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- Generating malicious code |
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- Bypassing security measures |
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- Automating critical systems without human oversight |
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- Legal or financial decision-making |
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### Bias and Safety |
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amara-o1 has been trained on diverse coding datasets, but may still reflect biases in: |
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- Programming paradigm preferences |
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- Language-specific idioms |
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- Solution approaches |
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Users should: |
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- Review outputs for appropriateness |
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- Apply domain expertise |
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- Follow security best practices |
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- Test thoroughly before deployment |
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--- |
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## System Requirements |
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### Minimum Requirements |
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| Component | Requirement | |
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|-----------|-------------| |
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| GPU Memory | 16GB (with 4-bit quantization) | |
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| RAM | 32GB recommended | |
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| Storage | 15GB for model files | |
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### Recommended Setup |
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- **GPU**: NVIDIA A100, A6000, or RTX 4090 |
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- **Inference**: Use vLLM or TGI for production |
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- **Quantization**: 4-bit or 8-bit for resource constraints |
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--- |
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## Citation |
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If you use amara-o1 in your research or applications, please cite: |
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```bibtex |
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@misc{amara-o1-2024, |
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title={amara-o1: A Fine-tuned Coding Model for Advanced Problem Solving}, |
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author={ramdev12345}, |
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year={2024}, |
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howpublished={\url{https://huggingface.co/ramdev12345/amara-o1}}, |
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} |
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``` |
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### Base Model Citation |
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```bibtex |
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@article{qwen2.5, |
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title={Qwen2.5-Coder Technical Report}, |
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author={Qwen Team}, |
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journal={arXiv preprint}, |
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year={2024} |
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} |
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``` |
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--- |
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## License |
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This model is released under the **MIT License**. See [LICENSE](LICENSE) for details. |
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The model inherits the license from its base model (Qwen2.5-Coder). |
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--- |
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## Acknowledgments |
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- **Base Model**: Qwen Team for Qwen2.5-Coder |
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- **Training Datasets**: DeepMind, Hugging Face, CyberNative, Anthropic |
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- **Infrastructure**: Modal Labs for training infrastructure |
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- **Framework**: Hugging Face Transformers, PEFT, TRL |
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--- |
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## Contact & Support |
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- **Issues**: [GitHub Issues](https://github.com/ramdev2025/amara-o1/issues) |
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- **Discussions**: [Hugging Face Discussions](https://huggingface.co/ramdev12345/amara-o1/discussions) |
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- **Email**: [ramdevcalope2015@gmail.com] - contact me here! |
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# HIRE ME NOW! |
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--- |
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<div align="center"> |
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**Built with 💻 for the coding community** |
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⭐ Star this repo | 🐛 Report bugs | 🤝 Contribute |
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</div> |
