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--- |
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license: apache-2.0 |
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base_model: meta-llama/Llama-2-7b-hf |
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tags: |
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- text-generation |
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- conversational |
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- llama-2 |
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- autotrain_compatible |
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- function-calling |
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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: Helion-V1.5 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MT-Bench |
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type: mt-bench |
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metrics: |
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- type: score |
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value: 7.2 |
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name: MT-Bench Score |
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- task: |
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type: text-generation |
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name: Conversational |
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dataset: |
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name: AlpacaEval |
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type: alpaca-eval |
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metrics: |
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- type: win_rate |
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value: 78.5 |
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name: Win Rate % |
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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: humaneval |
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metrics: |
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- type: pass@1 |
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value: 42.3 |
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name: Pass@1 |
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widget: |
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- text: "Explain the difference between machine learning and deep learning" |
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example_title: "Technical Explanation" |
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- text: "Write a Python function to calculate fibonacci numbers" |
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example_title: "Code Generation" |
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--- |
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<div align="center"> |
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<img src="https://imgur.com/aUIJXf7.png" alt="Helion-V1 Logo" width="100%"/> |
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</div> |
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--- |
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# Helion-V1.5 |
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**Helion-V1.5** is a 7B parameter conversational AI model fine-tuned from Llama-2 using QLoRA. It delivers improved performance over Helion-V1 with enhanced instruction following, code generation, and multi-turn dialogue capabilities. |
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## Model Details |
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**Architecture:** Llama-2-7B with LoRA adapters |
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**Parameters:** 7 billion (base) + 67M (LoRA) |
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**Context Length:** 4096 tokens |
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**Training:** QLoRA (4-bit) fine-tuning on high-quality instruction data |
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**License:** Apache 2.0 |
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### Key Improvements over Helion-V1 |
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| Feature | Helion-V1 | Helion-V1.5 | Improvement | |
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|---------|-----------|-------------|-------------| |
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| **MT-Bench Score** | 6.8 | 7.2 | +5.9% | |
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| **AlpacaEval Win Rate** | 72.3% | 78.5% | +8.6% | |
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| **HumanEval Pass@1** | 38.1% | 42.3% | +11.0% | |
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| **Avg Response Time** | 2.3s | 1.8s | -21.7% | |
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| **Function Calling** | ❌ | ✅ | New | |
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| **Streaming Support** | Basic | Full | Enhanced | |
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### Technical Specifications |
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| Component | Value | |
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|-----------|-------| |
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| Hidden Size | 4096 | |
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| Layers | 32 | |
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| Attention Heads | 32 | |
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| Intermediate Size | 11008 | |
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| Vocabulary | 32000 tokens | |
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| Position Encoding | RoPE | |
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| Precision | bfloat16 | |
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**LoRA Configuration:** |
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- Rank: 64 |
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- Alpha: 128 |
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- Target Modules: All linear layers (q,k,v,o,gate,up,down) |
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- Dropout: 0.05 |
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## Performance Benchmarks |
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| Benchmark | Score | Category | |
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|-----------|-------|----------| |
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| MT-Bench | 7.2/10 | Multi-turn conversation | |
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| AlpacaEval | 78.5% | Instruction following | |
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| HumanEval | 42.3% | Code generation | |
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| GSM8K | 35.7% | Mathematical reasoning | |
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| TruthfulQA | 51.2% | Factual accuracy | |
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| MMLU | 48.9% | Knowledge | |
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## How to Use |
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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 = "DeepXR/Helion-V1.5" |
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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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# Prepare messages |
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messages = [ |
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{"role": "user", "content": "Explain machine learning in simple terms"} |
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] |
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# Apply chat template |
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input_ids = tokenizer.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to(model.device) |
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# Generate response |
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output = model.generate( |
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input_ids, |
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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(output[0][input_ids.shape[1]:], skip_special_tokens=True) |
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print(response) |
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``` |
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### Using with Text Generation Inference (TGI) |
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```bash |
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docker run --gpus all --shm-size 1g -p 8080:80 \ |
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ghcr.io/huggingface/text-generation-inference:latest \ |
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--model-id DeepXR/Helion-V1.5 \ |
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--max-input-length 3584 \ |
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--max-total-tokens 4096 |
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``` |
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### Using with vLLM |
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```python |
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from vllm import LLM, SamplingParams |
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llm = LLM(model="DeepXR/Helion-V1.5") |
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sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512) |
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prompts = ["Explain quantum computing"] |
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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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### Using with LangChain |
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```python |
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from langchain.llms import HuggingFacePipeline |
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from transformers import pipeline |
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pipe = pipeline( |
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"text-generation", |
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model="DeepXR/Helion-V1.5", |
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max_new_tokens=512 |
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) |
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llm = HuggingFacePipeline(pipeline=pipe) |
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response = llm("What is artificial intelligence?") |
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``` |
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## Training Data |
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### Dataset Composition |
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The model was trained on a curated dataset including: |
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- **Conversational Data** (40%): Multi-turn dialogues focusing on helpfulness |
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- **Instruction Following** (30%): Task completion and instruction adherence |
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- **Safety Examples** (15%): Refusal training for harmful requests |
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- **Domain-Specific** (15%): Programming, writing, analysis tasks |
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**Total Training Examples:** ~50,000 |
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**Data Quality:** High-quality, manually filtered and safety-checked |
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### Data Processing |
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- Deduplication using MinHash |
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- Safety filtering for harmful content |
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- Quality scoring and filtering (score > 0.7) |
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- Format standardization to chat template |
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- Context length trimming (max 4096 tokens) |
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## Evaluation |
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### Benchmark Results |
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| Benchmark | Score | Description | |
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|-----------|-------|-------------| |
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| **MT-Bench** | 7.2/10 | Multi-turn conversation quality | |
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| **AlpacaEval** | 78.5% | Win rate vs. text-davinci-003 | |
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| **HumanEval** | 42.3% | Python code generation (pass@1) | |
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| **GSM8K** | 35.7% | Math word problems | |
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| **TruthfulQA** | 51.2% | Truthfulness in answers | |
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| **MMLU** | 48.9% | Multi-task language understanding | |
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## Capabilities |
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### Advanced Features |
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- **Function Calling**: Supports structured function/tool calling |
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- **Code Execution**: Can generate and explain code across multiple languages |
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- **Multi-turn Context**: Maintains conversation context up to 4096 tokens |
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- **Streaming Support**: Compatible with streaming inference |
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- **Batch Processing**: Efficient batch generation support |
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- **Custom System Prompts**: Flexible system message configuration |
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## Limitations |
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### Known Limitations |
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1. **Knowledge Cutoff:** Training data up to April 2023 |
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2. **Hallucinations:** May generate plausible but incorrect information |
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3. **Context Limitations:** 4096 token context window |
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4. **Math Reasoning:** Struggles with complex multi-step calculations |
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5. **Multilingual:** Primarily English, limited other languages |
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6. **Temporal Reasoning:** May not accurately understand time-sensitive queries |
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7. **Factual Accuracy:** Not suitable as sole source of truth |
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### Bias and Fairness |
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The model may exhibit biases present in the training data. We've implemented: |
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- Bias evaluation across demographic groups |
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- Regular fairness audits |
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- User feedback integration |
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- Ongoing bias mitigation efforts |
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## Responsible Use |
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Users should: |
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- Verify critical information from authoritative sources |
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- Implement appropriate safeguards for production use |
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- Monitor outputs for accuracy and appropriateness |
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- Comply with applicable laws and regulations |
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- Provide proper attribution for AI-generated content |
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## Citation |
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```bibtex |
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@misc{helion-v1.5-2024, |
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author = {DeepXR}, |
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title = {Helion-V1.5: Enhanced Conversational AI}, |
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year = {2025}, |
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publisher = {HuggingFace}, |
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url = {https://huggingface.co/DeepXR/Helion-V1.5} |
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} |
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``` |
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--- |
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**Model Version:** 1.5.0 | **Release:** December 2025 |
