DeepXR/Helion-V1.5

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<img src="https://imgur.com/aUIJXf7.png" alt="Helion-V1 Logo" width="100%"/>

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Helion-V1.5

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.

Model Details

Architecture: Llama-2-7B with LoRA adapters Parameters: 7 billion (base) + 67M (LoRA) Context Length: 4096 tokens Training: QLoRA (4-bit) fine-tuning on high-quality instruction data License: Apache 2.0

Key Improvements over Helion-V1

Technical Specifications

LoRA Configuration:

• —Rank: 64
• —Alpha: 128
• —Target Modules: All linear layers (q,k,v,o,gate,up,down)
• —Dropout: 0.05

Performance Benchmarks

How to Use

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "DeepXR/Helion-V1.5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Prepare messages
messages = [
    {"role": "user", "content": "Explain machine learning in simple terms"}
]

# Apply chat template
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# Generate response
output = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Using with Text Generation Inference (TGI)

docker run --gpus all --shm-size 1g -p 8080:80 \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id DeepXR/Helion-V1.5 \
  --max-input-length 3584 \
  --max-total-tokens 4096

Using with vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="DeepXR/Helion-V1.5")
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512)

prompts = ["Explain quantum computing"]
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(output.outputs[0].text)

Using with LangChain

from langchain.llms import HuggingFacePipeline
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="DeepXR/Helion-V1.5",
    max_new_tokens=512
)

llm = HuggingFacePipeline(pipeline=pipe)
response = llm("What is artificial intelligence?")

Training Data

Dataset Composition

The model was trained on a curated dataset including:

• —Conversational Data (40%): Multi-turn dialogues focusing on helpfulness
• —Instruction Following (30%): Task completion and instruction adherence
• —Safety Examples (15%): Refusal training for harmful requests
• —Domain-Specific (15%): Programming, writing, analysis tasks

Total Training Examples: ~50,000 Data Quality: High-quality, manually filtered and safety-checked

Data Processing

• —Deduplication using MinHash
• —Safety filtering for harmful content
• —Quality scoring and filtering (score > 0.7)
• —Format standardization to chat template
• —Context length trimming (max 4096 tokens)

Evaluation

Benchmark Results

Capabilities

Advanced Features

• —Function Calling: Supports structured function/tool calling
• —Code Execution: Can generate and explain code across multiple languages
• —Multi-turn Context: Maintains conversation context up to 4096 tokens
• —Streaming Support: Compatible with streaming inference
• —Batch Processing: Efficient batch generation support
• —Custom System Prompts: Flexible system message configuration

Limitations

Known Limitations

1. 1.Knowledge Cutoff: Training data up to April 2023
2. 2.Hallucinations: May generate plausible but incorrect information
3. 3.Context Limitations: 4096 token context window
4. 4.Math Reasoning: Struggles with complex multi-step calculations
5. 5.Multilingual: Primarily English, limited other languages
6. 6.Temporal Reasoning: May not accurately understand time-sensitive queries
7. 7.Factual Accuracy: Not suitable as sole source of truth

Bias and Fairness

The model may exhibit biases present in the training data. We've implemented:

• —Bias evaluation across demographic groups
• —Regular fairness audits
• —User feedback integration
• —Ongoing bias mitigation efforts

Responsible Use

Users should:

• —Verify critical information from authoritative sources
• —Implement appropriate safeguards for production use
• —Monitor outputs for accuracy and appropriateness
• —Comply with applicable laws and regulations
• —Provide proper attribution for AI-generated content

Citation

@misc{helion-v1.5-2024,
  author = {DeepXR},
  title = {Helion-V1.5: Enhanced Conversational AI},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/DeepXR/Helion-V1.5}
}

Model Version: 1.5.0 | Release: December 2025