---
library_name: transformers
tags:
- pashto
- mistral
- 4bit
- nf4
- vocabulary-expansion
- afghanistan
- pashto-language
language:
- ps
- en
license: apache-2.0
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-v0.1
---

# Ghanam-7B-Base-Pashto-v0.1

## Model Description

**Ghanam-7B-Base-Pashto-v0.1** is an experimental base model that extends the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) vocabulary to include 47 Pashto/Arabic characters. This is the **first stage** of creating a Pashto-capable language model, where the tokenizer has been modified and the embedding layer has been expanded to support Pashto script.

### Key Features
- **Vocabulary Size**: 32,010 tokens (original 32,000 + 47 Pashto characters)
- **Quantization**: 4-bit NF4 with double quantization for efficient inference
- **Memory Efficient**: ~4GB VRAM usage for inference
- **Pashto Script Support**: Now recognizes all Pashto-specific characters including ښ، ږ، څ، ډ، ړ، ځ، ګ، ۍ، ې

### Pashto Characters Added
```
ا ب پ ت ث ج چ ح خ د ذ ر ز ژ س ش ص ض ط ظ ع غ ف ق ک ګ گ ل م ن ڼ و ه ی ډ ړ ځ څ ښ ږ ۍ ې ء آ أ ؤ ئ
```

### ⚠️ Important Note
This model **does not yet understand Pashto language semantics**. It can:
- ✅ Tokenize Pashto text correctly
- ✅ Generate Pashto characters (if prompted in Pashto)
- ❌ Understand Pashto meaning or context
- ❌ Respond meaningfully in Pashto

The model still generates English text as it hasn't been fine-tuned on Pashto data.

## Model Details

### Model Description

This model is the first step in creating a Pashto language model based on Mistral-7B. The vocabulary was expanded using the **Stanford vocabulary expansion method** (mean resizing), where new embeddings were initialized using the mean of existing embeddings to minimize disruption to the model's performance.

- **Developed by:** Nassim JP (Community Project)
- **Model type:** Causal Language Model (Decoder-only Transformer)
- **Language(s):** English (original) + Pashto script support (tokenization only)
- **License:** Apache 2.0
- **Base Model:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Quantization:** 4-bit NF4 (BitsAndBytes)
- **Vocabulary Expansion Method:** Stanford mean resizing

### Model Sources

- **Repository:** [Coming Soon]
- **Base Model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)

## Uses

### Direct Use

You can use this model for:
- **Pashto tokenization** and text preprocessing
- **Inference experiments** with Pashto prompts (though responses will be in English)
- **As a starting point** for fine-tuning on Pashto datasets
- **Research** on vocabulary expansion for low-resource languages

### Downstream Use

This model is intended to be **fine-tuned** on Pashto text data for:
- Pashto language modeling
- Pashto text generation
- Pashto translation tasks
- Pashto question answering

### Out-of-Scope Use

- **Do not use** this model for production Pashto applications without fine-tuning
- **Do not expect** Pashto understanding or generation in Pashto
- **Not suitable** for tasks requiring Pashto semantic comprehension
- **Not tested** for bias, toxicity, or safety in Pashto context

## Bias, Risks, and Limitations

### Technical Limitations
1. **No Pashto Understanding**: The model has only expanded vocabulary but has not learned Pashto semantics
2. **English-Only Knowledge**: All pre-training knowledge remains in English
3. **Potential Performance Degradation**: Vocabulary expansion may slightly impact English performance
4. **Untested on Pashto Tasks**: No evaluation has been conducted on Pashto benchmarks

### Recommendations
- Use this model **only as a foundation** for Pashto fine-tuning
- **Validate carefully** before using in any production environment
- **Expect English responses** when prompting in Pashto (until fine-tuned)
- Consider this a **research checkpoint** rather than a production model

## How to Get Started with the Model

### Loading the Model (4-bit)

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

# Model path (local or from Hub)
model_path = "nassimjp/Ghanam-7B-Base-Pashto-v0.1"

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    quantization_config=bnb_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Set padding token (optional)
tokenizer.pad_token = tokenizer.eos_token

# Test tokenization
pashto_text = "پښتونخوا کې ډېر ښکلي ښارونه او ځنګلونه شته"
tokens = tokenizer.encode(pashto_text)
print(f"Tokens: {tokens}")
print(f"Decoded: {tokenizer.decode(tokens)}")
```

### Generating Text

```python
# Prompt in Pashto (model will respond in English)
prompt = "سلام، څنګه یاست؟"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=50,
        temperature=0.7,
        do_sample=True,
        repetition_penalty=1.2
    )

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

### Inspecting New Tokens

```python
# Check if Pashto characters are recognized
pashto_chars = ["ښ", "ږ", "څ", "ډ", "ړ", "ځ", "ګ"]
for char in pashto_chars:
    token_id = tokenizer.encode(char)[0]
    token_str = tokenizer.decode([token_id])
    print(f"Character: {char} -> Token ID: {token_id} -> Decoded: {token_str}")
```

## Training Details

### Training Data

**No training data was used** for this model. The vocabulary was expanded without any fine-tuning on Pashto text. The model retains only the original Mistral-7B pre-training knowledge.

### Training Procedure

#### Vocabulary Expansion Method

The model underwent a **two-stage process**:

**Stage 1: Tokenizer Modification**
- Added 47 Pashto/Arabic characters to the original tokenizer
- Preserved all original tokens (no replacements)
- New vocabulary size: 32,010

**Stage 2: Embedding Resizing**
- Used Stanford vocabulary expansion method (mean resizing)
- New embeddings initialized with the mean of existing embeddings
- Both `lm_head` and embedding layers were resized
- No gradient updates or training performed

#### Training Hyperparameters

- **Quantization**: 4-bit NF4 with double quantization
- **Compute dtype**: bfloat16
- **Method**: Vocabulary expansion only (no fine-tuning)
- **Embedding Initialization**: Mean resizing

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data
Initial testing was performed using:
- **Pashto sample texts** to verify tokenization
- **English prompts** to ensure base functionality
- **Character-level inspection** to confirm tokenizer additions

#### Metrics
- **Vocabulary Size**: Successfully expanded from 32,000 to 32,010
- **Tokenization Accuracy**: All 47 characters are properly tokenized
- **Generation Quality**: English generation preserved
- **Memory Usage**: ~4GB VRAM for inference

### Results

| Test | Result | Status |
|------|--------|--------|
| Pashto Tokenization | ✅ All 47 characters recognized | PASS |
| English Generation | ✅ Original performance preserved | PASS |
| Pashto Understanding | ❌ No semantic understanding | EXPECTED |
| Pashto Response | ❌ Generates English | EXPECTED |
| Model Loading (4-bit) | ✅ Successfully loads | PASS |
| Memory Efficiency | ✅ ~4GB VRAM | PASS |

## Environmental Impact

Carbon emissions were minimal as this model:
- Did not require any training
- Only performed vocabulary expansion and quantization
- Used a single GPU for a few minutes

- **Hardware Type:** NVIDIA A100 (or similar)
- **Hours used:** < 1 hour
- **Cloud Provider:** N/A (local)
- **Compute Region:** N/A
- **Carbon Emitted:** Negligible

## Technical Specifications

### Model Architecture and Objective

- **Architecture:** Transformer decoder (Mistral-7B)
- **Number of Layers:** 32
- **Hidden Size:** 4096
- **Attention Heads:** 32
- **Intermediate Size:** 14336
- **Vocabulary Size:** 32,010 (expanded from 32,000)
- **Positional Encoding:** Rotary Position Embeddings (RoPE)

### Compute Infrastructure

#### Hardware
- CPU for embedding expansion
- GPU for quantization and inference testing

#### Software
- **Transformers**: v4.31.0+
- **BitsAndBytes**: v0.41.0+
- **PyTorch**: v2.0.0+
- **Accelerate**: v0.20.0+

## Model Card Authors

- **Nassim JP** - Vocabulary expansion and quantization implementation
- Original model: Mistral AI team

## Model Card Contact

For questions or collaboration:
- Hugging Face: [nassimjp](https://huggingface.co/nassimjp)

## Acknowledgments

- Mistral AI for the base model
- Stanford NLP for the vocabulary expansion method
- Hugging Face for the transformers library
- BitsAndBytes team for 4-bit quantization

## Glossary

- **Vocabulary Expansion**: Adding new tokens to a model's tokenizer
- **Mean Resizing**: Initializing new embeddings with the mean of existing embeddings
- **4-bit NF4**: 4-bit Normal Float quantization method
- **Fine-tuning**: Training a pre-trained model on domain-specific data

## Next Steps

To make this model truly Pashto-capable, the next steps include:

1. **Collect Pashto corpus** (books, articles, web text)
2. **Pre-train on Pashto** data (continued pre-training)
3. **Fine-tune** for specific tasks (translation, QA, generation)
4. **Evaluate** on Pashto benchmarks
5. **Deploy** in production environments

## Citation

If you use this model or the vocabulary expansion method, please cite:

```bibtex
@misc{ghanam2024pashto,
  author = {Nassim JP},
  title = {Ghanam-7B-Base-Pashto: Vocabulary Expansion for Pashto Language},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/nassimjp/Ghanam-7B-Base-Pashto-v0.1}}
}
```

### Original Mistral-7B Citation

```bibtex
@article{jiang2023mistral,
  title={Mistral 7B},
  author={Jiang, Albert Q and others},
  journal={arXiv preprint arXiv:2310.06825},
  year={2023}
}
```

---

## Additional Information

### Usage Tips

1. **Tokenization**: The tokenizer now correctly handles all Pashto characters
2. **Memory**: 4-bit quantization allows running on consumer GPUs with 6-8GB VRAM
3. **Generation**: Use `do_sample=True` and `temperature=0.7` for diverse outputs
4. **Pad Token**: If using batch generation, set `pad_token=tokenizer.eos_token`

### Common Issues

| Issue | Solution |
|-------|----------|
| Tokenizer missing characters | Ensure you're using the correct tokenizer from this repo |
| Memory errors | Reduce `max_new_tokens` or use CPU offloading |
| Poor generation quality | This is expected - the model needs fine-tuning |
| Pashto text displayed as `[UNK]` | Tokenizer hasn't loaded properly - reload from the repo |

---

**Model Status:** 🔬 Research Phase - Pre-fine-tuning checkpoint

**Last Updated:** June 2026
```