Helion-V1.5 / README.md

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metadata

license: apache-2.0
base_model: meta-llama/Llama-2-7b-hf
tags:
  - text-generation
  - conversational
  - llama-2
  - autotrain_compatible
  - function-calling
language:
  - en
pipeline_tag: text-generation
library_name: transformers
model-index:
  - name: Helion-V1.5
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MT-Bench
          type: mt-bench
        metrics:
          - type: score
            value: 7.2
            name: MT-Bench Score
      - task:
          type: text-generation
          name: Conversational
        dataset:
          name: AlpacaEval
          type: alpaca-eval
        metrics:
          - type: win_rate
            value: 78.5
            name: Win Rate %
      - task:
          type: text-generation
          name: Code Generation
        dataset:
          name: HumanEval
          type: humaneval
        metrics:
          - type: pass@1
            value: 42.3
            name: Pass@1
widget:
  - text: Explain the difference between machine learning and deep learning
    example_title: Technical Explanation
  - text: Write a Python function to calculate fibonacci numbers
    example_title: Code Generation

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

Feature	Helion-V1	Helion-V1.5	Improvement
MT-Bench Score	6.8	7.2	+5.9%
AlpacaEval Win Rate	72.3%	78.5%	+8.6%
HumanEval Pass@1	38.1%	42.3%	+11.0%
Avg Response Time	2.3s	1.8s	-21.7%
Function Calling	❌	✅	New
Streaming Support	Basic	Full	Enhanced

Technical Specifications

Component	Value
Hidden Size	4096
Layers	32
Attention Heads	32
Intermediate Size	11008
Vocabulary	32000 tokens
Position Encoding	RoPE
Precision	bfloat16

LoRA Configuration:

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

Performance Benchmarks

Benchmark	Score	Category
MT-Bench	7.2/10	Multi-turn conversation
AlpacaEval	78.5%	Instruction following
HumanEval	42.3%	Code generation
GSM8K	35.7%	Mathematical reasoning
TruthfulQA	51.2%	Factual accuracy
MMLU	48.9%	Knowledge

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

Benchmark	Score	Description
MT-Bench	7.2/10	Multi-turn conversation quality
AlpacaEval	78.5%	Win rate vs. text-davinci-003
HumanEval	42.3%	Python code generation (pass@1)
GSM8K	35.7%	Math word problems
TruthfulQA	51.2%	Truthfulness in answers
MMLU	48.9%	Multi-task language understanding

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

Knowledge Cutoff: Training data up to April 2023
Hallucinations: May generate plausible but incorrect information
Context Limitations: 4096 token context window
Math Reasoning: Struggles with complex multi-step calculations
Multilingual: Primarily English, limited other languages
Temporal Reasoning: May not accurately understand time-sensitive queries
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