---
tags:
- mistral
- lora
- peft
- transformers
- scientific-ml
- fine-tuned
- research-assistant
- hypothesis-generation
- scientific-writing
- scientific-reasoning
license: apache-2.0
library_name: peft
datasets:
- Allanatrix/Scientific_Research_Tokenized
pipeline_tag: text-generation
language:
- en
model-index:
- name: Nexa Mistral 7B Sci
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      type: allen/nexa-scientific-tokens
      name: Nexa Scientific Tokens
    metrics:
    - name: BLEU
      type: bleu
      value: 10
    - name: Entropy Novelty
      type: entropy
      value: 6
    - name: Internal Consistency
      type: custom
      value: 9
base_model:
- mistralai/Mistral-7B-v0.1
metrics:
- bleu
---


# Model Card for `nexa-mistral-7b-psi`

## Model Details

**Model Description**:  
`nexa-mistral-7b-psi` is a fine-tuned variant of the open-weight `Mistral-7B-v0.1` model, optimized for scientific research generation tasks such as hypothesis generation, abstract writing, and methodology completion. Fine-tuning was performed using the PEFT (Parameter-Efficient Fine-Tuning) library with LoRA in 4-bit quantized mode using the `bitsandbytes` backend.

This model is part of the **Nexa Scientific Intelligence (Psi)** series, developed for scalable, automated scientific reasoning and domain-specific text generation.

---

**Developed by**: Allan (Independent Scientific Intelligence Architect)  
**Funded by**: Self-funded  
**Shared by**: Allan (https://huggingface.co/allan-wandeer)  
**Model type**: Decoder-only transformer (causal language model)  
**Language(s)**: English (scientific domain-specific vocabulary)  
**License**: Apache 2.0 (inherits from base model)  
**Fine-tuned from**: `mistralai/Mistral-7B-v0.1`  
**Repository**: https://huggingface.co/allan-wandeer/nexa-mistral-7b-psi  
**Demo**: Coming soon via Hugging Face Spaces or Lambda inference endpoint.

---

## Uses

### Direct Use
- Scientific hypothesis generation
- Abstract and method section synthesis
- Domain-specific research writing
- Semantic completion of structured research prompts

### Downstream Use
- Fine-tuning or distillation into smaller expert models
- Foundation for test-time reasoning agents
- Seed model for bootstrapping larger synthetic scientific corpora

### Out-of-Scope Use
- General conversation or chat use cases
- Non-English scientific domains
- Legal, financial, or clinical advice generation

---

## Bias, Risks, and Limitations
While the model performs well on structured scientific input, it inherits biases from its base model (`Mistral-7B`) and fine-tuning dataset. Results should be evaluated by domain experts before use in high-stakes settings. It may hallucinate plausible but incorrect facts, especially in low-data areas.

---

## Recommendations
Users should:
- Validate critical outputs against trusted scientific literature
- Avoid deploying in clinical or regulatory environments without further evaluation
- Consider additional domain fine-tuning for niche fields

---

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "allan-wandia/nexa-mistral-7b-sci"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")

prompt = "Generate a novel hypothesis in quantum materials research:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=250)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---

## Training Details

### Training Data

* **Size**: 100 million tokens sampled from a 500M+ token corpus
* **Source**: Curated scientific literature, abstracts, methodologies, and domain-labeled corpora (Bio, Physics, QST, Astro)
* **Labeling**: Token-level labels auto-generated via `Nexa DataVault` tokenizer infrastructure

### Preprocessing

* Tokenization with sequence truncation to 1024 tokens
* Labeled and batched using CPU; inference dispatched to GPU asynchronously

### Training Hyperparameters

- **Base model**: `mistralai/Mistral-7B-v0.1`
- **Sequence length**: `1024`
- **Batch size**: `1` (with gradient accumulation)
- **Gradient Accumulation Steps**: `64`
- **Effective Batch Size**: `64`
- **Learning rate**: `2e-5`
- **Epochs**: `2`
- **LoRA**: Enabled (PEFT)
- **Quantization**: 4-bit via `bitsandbytes`
- **Optimizer**: 8-bit AdamW
- **Framework**: Transformers + PEFT + Accelerate

---

## Evaluation

### Testing Data

* Synthetic scientific prompts across domains (Physics, Biology, Materials Science)

### Evaluation Factors

* Semantic coherence (BLEU)
* Hypothesis novelty (entropy score)
* Internal scientific consistency (domain-specific rubric)

### Metrics

| Metric                 | Score |
| ---------------------- | ----- |
| BLEU (coherence)       | 10/10 |
| Entropy novelty        | 6/10  |
| Scientific consistency | 9/10  |
| Model similarity coef  | 87%   |

### Results

Model performs robustly in hypothesis generation and scientific prose tasks. While base coherence is high, novelty depends on prompt diversity. Well-suited as a distiller or inference agent for synthetic scientific corpora generation.

---

## Environmental Impact

| Component      | Value                               |
| -------------- | ----------------------------------- |
| Hardware Type  | 2× NVIDIA T4 GPUs                   |
| Hours used     | \~7.5                               |
| Cloud Provider | Kaggle (Google Cloud)               |
| Compute Region | US                                  |
| Carbon Emitted | Estimate pending (likely < 1kg CO2) |

---

## Technical Specifications

### Model Architecture

* Transformer decoder (Mistral-7B architecture)
* LoRA adapters applied to attention and FFN layers
* Quantized with `bitsandbytes` to 4-bit for memory efficiency

### Compute Infrastructure

* CPU: Intel i5 8th Gen vPro (batch preprocessing)
* GPU: 2× NVIDIA T4 (CUDA 12.1)

### Software Stack

* PEFT 0.12.0
* Transformers 4.41.1
* Accelerate
* TRL
* Torch 2.x

---

## Citation

**BibTeX**:

```bibtex
@misc{nexa-mistral-7b-sci,
  title = {Nexa Mistral 7B Sci},
  author = {Allan Wandia},
  year = {2025},
  howpublished = {\url{https://huggingface.co/allan-Wandia/nexa-mistral-7b-sci}},
  note = {Fine-tuned model for scientific generation tasks}
}
```
---

## Model Card Contact

For questions, contact Allan via Hugging Face or at:
📫 Email: \[allanw.mk@gmail.com]

---

## Model Card Authors

* Allan Wandia (Independent ML Engineer and Systems Architect)

---

## Glossary

* **LoRA**: Low-Rank Adaptation
* **PEFT**: Parameter-Efficient Fine-Tuning
* **BLEU**: Bilingual Evaluation Understudy Score
* **Entropy Score**: Metric used to estimate novelty/variation
* **Safe Tensors**: Secure, fast format for model weights

## Links
**Github Repo and notebook**: https://github.com/DarkStarStrix/Nexa_Auto
 
---