🚀 Apollo V1 7B: Advanced Reasoning Language Model - Complete model release with LoRA fine-tuned Mistral-7B-Instruct-v0.2, 161M parameter adapter, Apache 2.0 license, and professional documentation for logical, mathematical, and legal reasoning

Apollo V1 7B represents the first public release in the Apollo model series from VANTA Research. This specialized language model is optimized for advanced reasoning tasks including logical reasoning, mathematical problem-solving, and legal analysis.

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Training artifacts that shouldn't be in final release
+*.log
+wandb/
+runs/
+outputs/
+checkpoints/
+logs/
+
+# Temporary files
+*.tmp
+*.temp
+.cache/
+
+# Model training specific (keep the final weights)
+training_args.bin
+
+# Local development files
+test_*.py
+debug_*.py
+scratch_*.py

LICENSE

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MERGE_GUIDE.md

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+# Merging LoRA Weights with Base Model
+
+This repository contains LoRA (Low-Rank Adaptation) weights that need to be merged with the base Mistral-7B-Instruct-v0.2 model for use.
+
+## Quick Start
+
+### Option 1: Use with Ollama (Recommended)
+```bash
+# Download this repository
+git clone https://huggingface.co/vanta-research/apollo-v1-7b
+cd apollo-v1-7b
+
+# Create Ollama model
+echo 'FROM mistral:7b' > Modelfile
+ollama create apollo-v1-7b -f Modelfile
+ollama run apollo-v1-7b
+```
+
+### Option 2: Merge with Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "mistralai/Mistral-7B-Instruct-v0.2",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "./apollo-v1-7b")
+
+# Merge and save
+merged_model = model.merge_and_unload()
+merged_model.save_pretrained("./apollo-v1-7b-merged")
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
+tokenizer.save_pretrained("./apollo-v1-7b-merged")
+```
+
+### Option 3: Use with PEFT directly
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model_name = "mistralai/Mistral-7B-Instruct-v0.2"
+model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16)
+model = PeftModel.from_pretrained(model, "./apollo-v1-7b")
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Use for inference
+inputs = tokenizer("Hello, how can I help you today?", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Requirements
+
+- Base model: `mistralai/Mistral-7B-Instruct-v0.2`
+- Python packages: `transformers`, `peft`, `torch`
+- CUDA-compatible GPU (recommended)
+
+## Model Architecture
+
+- **Base Model**: Mistral 7B Instruct v0.2
+- **Training Method**: LoRA (Low-Rank Adaptation)
+- **Rank**: 64
+- **Alpha**: 16
+- **Target Modules**: q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, down_proj

MODEL_CARD.md

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+# Model Card: Apollo V1 7B
+
+## Model Details
+
+**Model Name**: Apollo V1 7B  
+**Developer**: VANTA Research  
+**Model Version**: 1.0.0  
+**Release Date**: September 2025  
+**License**: Apache 2.0  
+**Base Model**: mistralai/Mistral-7B-Instruct-v0.2  
+**Model Type**: Causal Language Model with LoRA Adapters  
+
+## Intended Use
+
+### Primary Use Cases
+- Educational reasoning assistance and tutoring
+- Mathematical problem solving with step-by-step explanations
+- Logical reasoning and argument analysis
+- Legal education and case study analysis (not professional advice)
+- Academic research support and hypothesis evaluation
+
+### Intended Users
+- Students and educators in STEM and legal fields
+- Researchers studying AI reasoning capabilities
+- Developers building reasoning-focused applications
+- Academic institutions and educational platforms
+
+## Model Architecture
+
+- **Base Architecture**: Mistral 7B Instruct v0.3
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **Total Parameters**: ~7 billion
+- **LoRA Configuration**:
+  - Rank (r): 16
+  - Alpha: 32
+  - Dropout: 0.1
+  - Target modules: All linear layers
+- **Precision**: FP16 (GPU) / FP32 (CPU)
+- **Context Length**: 32,768 tokens
+
+## Training Data
+
+### Dataset Composition
+- **Total Instances**: 264 specialized reasoning examples
+- **Data Sources**: Curated legal reasoning scenarios, mathematical word problems, logical puzzles
+- **Data Quality**: Hand-crafted and reviewed by domain experts
+- **Language**: English
+- **Content Areas**:
+  - Legal reasoning and case analysis (40%)
+  - Mathematical problem solving (30%)
+  - Logical reasoning and puzzles (20%)
+  - Chain-of-thought examples (10%)
+
+### Data Processing
+- All instances manually reviewed for quality and accuracy
+- Balanced representation across reasoning domains
+- Consistent formatting and structure
+- Ethical content filtering applied
+
+## Training Procedure
+
+### Training Configuration
+- **Method**: Supervised Fine-tuning with LoRA
+- **Base Model**: mistralai/Mistral-7B-Instruct-v0.2
+- **Training Framework**: Transformers + PEFT
+- **Hardware**: NVIDIA RTX 3060 (12GB)
+- **Training Duration**: Multiple epochs until convergence
+- **Optimization**: AdamW optimizer with learning rate scheduling
+
+### Training Process
+1. Data preprocessing and tokenization
+2. LoRA adapter initialization
+3. Supervised fine-tuning on reasoning dataset
+4. Validation and checkpoint selection
+5. Model merging and evaluation
+
+## Evaluation
+
+### Comprehensive Reasoning Tests
+- **Test Suite**: 14 comprehensive reasoning tasks
+- **Success Rate**: 100% (14/14 tests passed)
+- **Categories Tested**:
+  - Apollo Identity: 3/3 tests passed
+  - Logical Reasoning: 3/3 tests passed
+  - Legal Reasoning: 3/3 tests passed
+  - Mathematical Reasoning: 3/3 tests passed
+  - Chain-of-Thought: 2/2 tests passed
+
+### Performance Benchmarks
+- **Mathematical Accuracy**: 100% on standard math problems
+- **Response Speed**: 2-7x faster than comparable models
+- **Token Generation**: 52-53 tokens/second
+- **Average Response Time**: 3.9 seconds
+
+### Comparative Analysis
+Head-to-head comparison with Apollo Qwen2 Champion:
+- Legal Reasoning: Apollo V1 won (3.77s vs 26.98s)
+- Logic Problems: Apollo V1 won (3.78s vs 10.69s)  
+- Scientific Reasoning: Apollo V1 won (3.83s vs 14.72s)
+- **Overall**: 3/3 wins with superior speed
+
+## Limitations
+
+### Known Limitations
+1. **Domain Specialization**: Optimized for reasoning tasks, may have limitations in creative writing, general conversation, or domain-specific knowledge outside training scope
+2. **Legal Advice Disclaimer**: Provides educational legal analysis only, not professional legal advice
+3. **Verification Required**: While highly accurate, outputs should be verified for critical applications
+4. **Context Constraints**: Limited to 32K token context window
+5. **Language**: Primarily trained and tested in English
+
+### Technical Limitations  
+- Memory requirements: ~14GB for full precision inference
+- Inference speed depends on hardware capabilities
+- May require specific software dependencies (transformers, peft)
+
+## Bias and Fairness
+
+### Bias Mitigation Efforts
+- Diverse reasoning problem selection
+- Manual review of training examples
+- Testing across different problem types and complexity levels
+- Continuous monitoring of model outputs
+
+### Known Biases
+- May reflect biases present in base Mistral model
+- Training data primarily from Western legal and educational contexts
+- Potential bias toward formal logical reasoning approaches
+
+### Fairness Considerations
+- Model designed for educational use across diverse populations
+- Open source licensing enables community oversight
+- Transparent documentation of capabilities and limitations
+
+## Environmental Impact
+
+### Carbon Footprint
+- Training conducted on single RTX 3060 GPU
+- Relatively efficient LoRA training vs full model fine-tuning
+- Estimated training time: <24 hours total
+- Carbon impact significantly lower than training large models from scratch
+
+### Efficiency Measures
+- LoRA fine-tuning reduces computational requirements
+- Optimized inference for various hardware configurations
+- Support for CPU-only inference to reduce GPU dependence
+
+## Ethical Considerations
+
+### Responsible Use
+- Clear documentation of intended use cases
+- Explicit warnings about limitations and verification needs
+- Educational focus with appropriate disclaimers
+- Open source to enable community review
+
+### Potential Misuse
+- Should not be used for professional legal, medical, or financial advice
+- Not suitable for critical decision-making without human oversight
+- May be misused if presented as infallible reasoning system
+
+### Mitigation Strategies
+- Clear usage guidelines and disclaimers
+- Educational focus in documentation
+- Open source licensing for transparency
+- Community feedback mechanisms
+
+## Technical Specifications
+
+### System Requirements
+- **Minimum**: 16GB RAM, modern CPU
+- **Recommended**: 16GB+ GPU, 32GB+ system RAM
+- **Software**: Python 3.8+, PyTorch 2.0+, Transformers 4.44+
+
+### Deployment Options
+- Local inference (GPU/CPU)
+- Cloud deployment (AWS, GCP, Azure)
+- Edge deployment (with quantization)
+- API integration via FastAPI/Flask
+
+## Version History
+
+### Version 1.0.0 (September 2025)
+- Initial public release
+- Base model: Mistral 7B Instruct v0.3
+- 264 training instances across reasoning domains
+- Comprehensive evaluation and benchmarking
+- Full documentation and usage examples
+
+## Citation
+
+```bibtex
+@misc{apollo-v1-7b-2025,
+  title={Apollo V1 7B: Advanced Reasoning AI Model},
+  author={VANTA Research Team},
+  year={2025},
+  url={https://huggingface.co/vanta-research/apollo-v1-7b},
+  note={First public release of specialized reasoning language model}
+}
+```
+
+## Contact and Support
+
+- **Primary Contact**: research@vanta.ai
+- **GitHub Issues**: [vanta-research/apollo-v1-7b](https://github.com/vanta-research/apollo-v1-7b/issues)
+- **Documentation**: [vanta.ai/models/apollo-v1-7b](https://vanta.ai/models/apollo-v1-7b)
+- **Community**: [Discord Server](https://discord.gg/vanta-research)
+
+## Acknowledgments
+
+- Mistral AI for the excellent base model
+- Hugging Face for the transformers and PEFT libraries
+- Microsoft for LoRA research and implementation
+- Open source community for tools and inspiration
+- Beta testers and early adopters for valuable feedback
+
+---
+
+*Last Updated: September 2025*  
+*Model Card Version: 1.0*

README.md

@@ -1,3 +1,95 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+library_name: peft
+tags:
+- reasoning
+- legal-analysis
+- mathematical-reasoning
+- logical-reasoning
+- mistral
+- lora
+- vanta-research
+- apollo
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Apollo V1 7B
+
+**Advanced Reasoning Language Model**
+
+Apollo V1 7B is a specialized language model designed for advanced reasoning tasks, including logical reasoning, mathematical problem-solving, and legal analysis. Built on Mistral 7B-Instruct-v0.2 using LoRA fine-tuning, this model represents the first public release in the Apollo model series from VANTA Research.
+
+## Model Overview
+
+Apollo V1 7B is a specialized language model optimized for reasoning-intensive tasks. The model demonstrates exceptional performance in logical reasoning, mathematical problem-solving, and legal analysis through targeted fine-tuning on curated reasoning datasets.
+
+### Key Capabilities
+
+- **Logical Reasoning**: Advanced syllogistic reasoning, conditional logic, and contradiction detection
+- **Mathematical Problem Solving**: Step-by-step mathematical reasoning with high accuracy
+- **Legal Analysis**: Educational legal reasoning and case analysis capabilities
+- **High Performance**: Optimized for fast inference while maintaining quality
+- **Consistent Identity**: Maintains clear model identity and capability awareness
+
+## Model Details
+
+- **Base Model**: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- **Training Method**: LoRA (Low-Rank Adaptation) fine-tuning
+- **Parameters**: ~7.24B total parameters
+- **LoRA Rank**: 64
+- **Target Modules**: All linear layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
+- **Training Precision**: 16-bit (bfloat16)
+- **License**: Apache 2.0
+
+## Quick Start
+
+### Using the LoRA Adapter
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Load base model and tokenizer
+model_name = "mistralai/Mistral-7B-Instruct-v0.2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Load and apply LoRA adapter
+model = PeftModel.from_pretrained(model, "vanta-research/apollo-v1-7b")
+
+# Example usage
+prompt = "Solve this logical reasoning problem: If all cats are mammals, and Fluffy is a cat, what can we conclude about Fluffy?"
+
+inputs = tokenizer(prompt, return_tensors="pt")
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## License
+
+This model is released under the Apache 2.0 License. See [LICENSE](./LICENSE) for details.
+
+## Contact
+
+For questions, issues, or collaboration opportunities, please visit the [model repository](https://huggingface.co/vanta-research/apollo-v1-7b).
+
+---
+
+**Apollo V1 7B - Advancing the frontier of reasoning in language models**

USAGE_GUIDE.md

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+# Apollo V1 7B Usage Guide
+
+## Installation & Setup
+
+### Requirements
+```bash
+pip install transformers>=4.44.0 peft>=0.12.0 torch>=2.0.0
+```
+
+### Basic Setup
+```python
+from transformers import AutoTokenizer
+from peft import AutoPeftModelForCausalLM
+import torch
+
+# Load model (adjust device_map based on your hardware)
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "vanta-research/apollo-v1-7b",
+    torch_dtype=torch.float16,
+    device_map="auto"  # or "cpu" for CPU-only
+)
+
+tokenizer = AutoTokenizer.from_pretrained("vanta-research/apollo-v1-7b")
+```
+
+## Usage Patterns
+
+### 1. Mathematical Problem Solving
+
+```python
+def solve_math_problem(problem):
+    prompt = f"Solve this step by step: {problem}"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    outputs = model.generate(
+        **inputs,
+        max_length=400,
+        temperature=0.1,  # Low temperature for accuracy
+        do_sample=True,
+        top_p=0.9
+    )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Examples
+problems = [
+    "What is 15% of 240?",
+    "If x + 5 = 12, what is x?", 
+    "A rectangle has length 8 and width 5. What is its area?"
+]
+
+for problem in problems:
+    solution = solve_math_problem(problem)
+    print(f"Problem: {problem}")
+    print(f"Solution: {solution}")
+    print("-" * 50)
+```
+
+### 2. Legal Reasoning
+
+```python
+def analyze_legal_scenario(scenario):
+    prompt = f"Analyze this legal scenario: {scenario}"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    outputs = model.generate(
+        **inputs,
+        max_length=600,
+        temperature=0.2,  # Slightly higher for nuanced analysis
+        repetition_penalty=1.1
+    )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example legal scenarios
+scenarios = [
+    "A contract requires payment within 30 days, but the buyer received defective goods.",
+    "Police conducted a search without a warrant, claiming exigent circumstances.",
+    "An employee was fired for social media posts made outside work hours."
+]
+
+for scenario in scenarios:
+    analysis = analyze_legal_scenario(scenario)
+    print(f"Scenario: {scenario}")
+    print(f"Analysis: {analysis}")
+    print("-" * 50)
+```
+
+### 3. Logical Reasoning
+
+```python
+def solve_logic_puzzle(puzzle):
+    prompt = f"Solve this logic puzzle step by step: {puzzle}"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    outputs = model.generate(
+        **inputs,
+        max_length=500,
+        temperature=0.1,
+        top_k=50
+    )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example logic puzzles
+puzzles = [
+    "If all A are B, and all B are C, what can we conclude about A and C?",
+    "All cats are animals. Some animals are pets. Can we conclude all cats are pets?",
+    "If it rains, the ground gets wet. The ground is wet. Did it rain?"
+]
+
+for puzzle in puzzles:
+    solution = solve_logic_puzzle(puzzle)
+    print(f"Puzzle: {puzzle}")
+    print(f"Solution: {solution}")
+    print("-" * 50)
+```
+
+## Advanced Usage
+
+### Batch Processing
+```python
+def batch_process_questions(questions, batch_size=4):
+    results = []
+    
+    for i in range(0, len(questions), batch_size):
+        batch = questions[i:i+batch_size]
+        
+        # Process batch
+        batch_results = []
+        for question in batch:
+            inputs = tokenizer(question, return_tensors="pt")
+            outputs = model.generate(**inputs, max_length=300)
+            response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            batch_results.append(response)
+        
+        results.extend(batch_results)
+    
+    return results
+```
+
+### Memory Optimization
+```python
+# For limited GPU memory
+import torch
+
+def memory_efficient_generation(prompt, max_length=400):
+    with torch.no_grad():
+        inputs = tokenizer(prompt, return_tensors="pt")
+        
+        outputs = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=0.1,
+            use_cache=True,  # Enable KV caching
+            pad_token_id=tokenizer.eos_token_id
+        )
+        
+        # Clear cache after generation
+        if hasattr(model, 'past_key_values'):
+            model.past_key_values = None
+        
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
+### Custom Prompting
+```python
+def create_apollo_prompt(question, context="", task_type="general"):
+    """Create optimized prompts for different task types."""
+    
+    task_prompts = {
+        "math": "Solve this mathematical problem step by step:",
+        "legal": "Analyze this legal scenario considering relevant laws and precedents:",
+        "logic": "Solve this logical reasoning problem step by step:",
+        "general": "Please provide a clear and detailed response to:"
+    }
+    
+    task_prompt = task_prompts.get(task_type, task_prompts["general"])
+    
+    if context:
+        full_prompt = f"Context: {context}
+
+{task_prompt} {question}"
+    else:
+        full_prompt = f"{task_prompt} {question}"
+    
+    return full_prompt
+
+# Usage
+question = "What is 25% of 160?"
+prompt = create_apollo_prompt(question, task_type="math")
+```
+
+## Performance Optimization
+
+### GPU Settings
+```python
+# For RTX 3060 (12GB) or similar
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "vanta-research/apollo-v1-7b",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    max_memory={0: "10GB"}  # Reserve some GPU memory
+)
+```
+
+### CPU Inference
+```python
+# For CPU-only inference
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "vanta-research/apollo-v1-7b",
+    torch_dtype=torch.float32,  # Use float32 for CPU
+    device_map="cpu"
+)
+```
+
+### Quantization (Coming Soon)
+```python
+# 8-bit quantization for reduced memory usage
+from transformers import BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_8bit=True,
+    llm_int8_enable_fp32_cpu_offload=True
+)
+
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "vanta-research/apollo-v1-7b",
+    quantization_config=quantization_config
+)
+```
+
+## Integration Examples
+
+### FastAPI Server
+```python
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+app = FastAPI()
+
+class QuestionRequest(BaseModel):
+    question: str
+    task_type: str = "general"
+    max_length: int = 400
+
+@app.post("/ask")
+async def ask_apollo(request: QuestionRequest):
+    prompt = create_apollo_prompt(request.question, task_type=request.task_type)
+    response = memory_efficient_generation(prompt, request.max_length)
+    
+    return {
+        "question": request.question,
+        "response": response,
+        "task_type": request.task_type
+    }
+
+# Run with: uvicorn app:app --host 0.0.0.0 --port 8000
+```
+
+### Gradio Interface
+```python
+import gradio as gr
+
+def apollo_interface(message, task_type):
+    prompt = create_apollo_prompt(message, task_type=task_type)
+    return memory_efficient_generation(prompt)
+
+interface = gr.Interface(
+    fn=apollo_interface,
+    inputs=[
+        gr.Textbox(label="Your Question"),
+        gr.Dropdown(["general", "math", "legal", "logic"], label="Task Type")
+    ],
+    outputs=gr.Textbox(label="Apollo's Response"),
+    title="Apollo V1 7B Chat",
+    description="Chat with Apollo V1 7B - Advanced Reasoning AI"
+)
+
+interface.launch(share=True)
+```
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Out of Memory**: Reduce batch size, use CPU inference, or enable memory optimization
+2. **Slow Generation**: Check device placement, enable caching, optimize prompt length
+3. **Poor Quality**: Adjust temperature (lower for factual, higher for creative)
+
+### Performance Tips
+
+- Use `torch.compile()` for faster inference (PyTorch 2.0+)
+- Enable gradient checkpointing for memory efficiency
+- Use appropriate data types (float16 for GPU, float32 for CPU)
+- Optimize prompt length and structure
+- Consider quantization for resource-constrained environments
+
+## Best Practices
+
+1. **Prompt Engineering**: Be specific and clear in your questions
+2. **Temperature Settings**: Use 0.1-0.2 for factual/mathematical tasks, 0.3-0.7 for creative tasks
+3. **Context Management**: Provide relevant context for complex scenarios
+4. **Verification**: Always verify critical information, especially for legal/financial advice
+5. **Ethical Usage**: Use responsibly and within intended capabilities
+
+For more examples and advanced usage patterns, see the GitHub repository and documentation.

adapter_config.json

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+  "base_model_name_or_path": "mistralai/Mistral-7B-Instruct-v0.3",
+  "bias": "none",
+  "corda_config": null,
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+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+    "q_proj",
+    "v_proj",
+    "o_proj",
+    "k_proj",
+    "up_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "model_name": "Apollo V1 7B",
+  "created_by": "VANTA Research",
+  "version": "1.0.0"
+}

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config.json

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+{
+  "_name_or_path": "vanta-research/apollo-v1-7b",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 32000,
+  "model_name": "Apollo V1 7B",
+  "version": "1.0.0",
+  "created_by": "VANTA Research",
+  "base_model": "mistralai/Mistral-7B-Instruct-v0.3",
+  "license": "MIT",
+  "model_description": "Advanced reasoning AI model specialized in logical reasoning, mathematical problem-solving, and legal analysis.",
+  "release_date": "2025-09-21T11:35:57.508765"
+}

generation_config.json

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+  "bos_token_id": 1,
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+  "max_length": 32768,
+  "pad_token_id": 2,
+  "transformers_version": "4.44.2"
+}

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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
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+  }
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