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Model: nassimjp/Ghanam-7B-Base-Pashto-v0.1 Source: Original Platform
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---
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library_name: transformers
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tags:
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- pashto
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- mistral
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- 4bit
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- nf4
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- vocabulary-expansion
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- afghanistan
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- pashto-language
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language:
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- ps
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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base_model: mistralai/Mistral-7B-v0.1
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---
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# Ghanam-7B-Base-Pashto-v0.1
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## Model Description
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**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.
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### Key Features
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- **Vocabulary Size**: 32,010 tokens (original 32,000 + 47 Pashto characters)
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- **Quantization**: 4-bit NF4 with double quantization for efficient inference
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- **Memory Efficient**: ~4GB VRAM usage for inference
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- **Pashto Script Support**: Now recognizes all Pashto-specific characters including ښ، ږ، څ، ډ، ړ، ځ، ګ، ۍ، ې
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### Pashto Characters Added
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```
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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
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```
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### ⚠️ Important Note
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This model **does not yet understand Pashto language semantics**. It can:
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- ✅ Tokenize Pashto text correctly
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- ✅ Generate Pashto characters (if prompted in Pashto)
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- ❌ Understand Pashto meaning or context
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- ❌ Respond meaningfully in Pashto
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The model still generates English text as it hasn't been fine-tuned on Pashto data.
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## Model Details
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### Model Description
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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.
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- **Developed by:** Nassim JP (Community Project)
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- **Model type:** Causal Language Model (Decoder-only Transformer)
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- **Language(s):** English (original) + Pashto script support (tokenization only)
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- **License:** Apache 2.0
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- **Base Model:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
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- **Quantization:** 4-bit NF4 (BitsAndBytes)
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- **Vocabulary Expansion Method:** Stanford mean resizing
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### Model Sources
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- **Repository:** [Coming Soon]
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- **Base Model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
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## Uses
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### Direct Use
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You can use this model for:
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- **Pashto tokenization** and text preprocessing
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- **Inference experiments** with Pashto prompts (though responses will be in English)
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- **As a starting point** for fine-tuning on Pashto datasets
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- **Research** on vocabulary expansion for low-resource languages
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### Downstream Use
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This model is intended to be **fine-tuned** on Pashto text data for:
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- Pashto language modeling
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- Pashto text generation
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- Pashto translation tasks
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- Pashto question answering
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### Out-of-Scope Use
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- **Do not use** this model for production Pashto applications without fine-tuning
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- **Do not expect** Pashto understanding or generation in Pashto
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- **Not suitable** for tasks requiring Pashto semantic comprehension
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- **Not tested** for bias, toxicity, or safety in Pashto context
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## Bias, Risks, and Limitations
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### Technical Limitations
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1. **No Pashto Understanding**: The model has only expanded vocabulary but has not learned Pashto semantics
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2. **English-Only Knowledge**: All pre-training knowledge remains in English
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3. **Potential Performance Degradation**: Vocabulary expansion may slightly impact English performance
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4. **Untested on Pashto Tasks**: No evaluation has been conducted on Pashto benchmarks
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### Recommendations
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- Use this model **only as a foundation** for Pashto fine-tuning
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- **Validate carefully** before using in any production environment
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- **Expect English responses** when prompting in Pashto (until fine-tuned)
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- Consider this a **research checkpoint** rather than a production model
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## How to Get Started with the Model
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### Loading the Model (4-bit)
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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# Model path (local or from Hub)
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model_path = "nassimjp/Ghanam-7B-Base-Pashto-v0.1"
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# 4-bit quantization config
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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quantization_config=bnb_config,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# Set padding token (optional)
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tokenizer.pad_token = tokenizer.eos_token
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# Test tokenization
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pashto_text = "پښتونخوا کې ډېر ښکلي ښارونه او ځنګلونه شته"
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tokens = tokenizer.encode(pashto_text)
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print(f"Tokens: {tokens}")
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print(f"Decoded: {tokenizer.decode(tokens)}")
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```
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### Generating Text
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```python
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# Prompt in Pashto (model will respond in English)
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prompt = "سلام، څنګه یاست؟"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=50,
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temperature=0.7,
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do_sample=True,
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repetition_penalty=1.2
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Inspecting New Tokens
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```python
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# Check if Pashto characters are recognized
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pashto_chars = ["ښ", "ږ", "څ", "ډ", "ړ", "ځ", "ګ"]
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for char in pashto_chars:
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token_id = tokenizer.encode(char)[0]
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token_str = tokenizer.decode([token_id])
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print(f"Character: {char} -> Token ID: {token_id} -> Decoded: {token_str}")
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```
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## Training Details
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### Training Data
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**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.
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### Training Procedure
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#### Vocabulary Expansion Method
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The model underwent a **two-stage process**:
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**Stage 1: Tokenizer Modification**
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- Added 47 Pashto/Arabic characters to the original tokenizer
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- Preserved all original tokens (no replacements)
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- New vocabulary size: 32,010
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**Stage 2: Embedding Resizing**
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- Used Stanford vocabulary expansion method (mean resizing)
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- New embeddings initialized with the mean of existing embeddings
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- Both `lm_head` and embedding layers were resized
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- No gradient updates or training performed
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#### Training Hyperparameters
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- **Quantization**: 4-bit NF4 with double quantization
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- **Compute dtype**: bfloat16
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- **Method**: Vocabulary expansion only (no fine-tuning)
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- **Embedding Initialization**: Mean resizing
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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Initial testing was performed using:
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- **Pashto sample texts** to verify tokenization
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- **English prompts** to ensure base functionality
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- **Character-level inspection** to confirm tokenizer additions
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#### Metrics
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- **Vocabulary Size**: Successfully expanded from 32,000 to 32,010
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- **Tokenization Accuracy**: All 47 characters are properly tokenized
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- **Generation Quality**: English generation preserved
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- **Memory Usage**: ~4GB VRAM for inference
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### Results
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| Test | Result | Status |
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|------|--------|--------|
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| Pashto Tokenization | ✅ All 47 characters recognized | PASS |
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| English Generation | ✅ Original performance preserved | PASS |
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| Pashto Understanding | ❌ No semantic understanding | EXPECTED |
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| Pashto Response | ❌ Generates English | EXPECTED |
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| Model Loading (4-bit) | ✅ Successfully loads | PASS |
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| Memory Efficiency | ✅ ~4GB VRAM | PASS |
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## Environmental Impact
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Carbon emissions were minimal as this model:
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- Did not require any training
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- Only performed vocabulary expansion and quantization
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- Used a single GPU for a few minutes
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- **Hardware Type:** NVIDIA A100 (or similar)
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- **Hours used:** < 1 hour
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- **Cloud Provider:** N/A (local)
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- **Compute Region:** N/A
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- **Carbon Emitted:** Negligible
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## Technical Specifications
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### Model Architecture and Objective
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- **Architecture:** Transformer decoder (Mistral-7B)
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- **Number of Layers:** 32
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- **Hidden Size:** 4096
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- **Attention Heads:** 32
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- **Intermediate Size:** 14336
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- **Vocabulary Size:** 32,010 (expanded from 32,000)
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- **Positional Encoding:** Rotary Position Embeddings (RoPE)
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### Compute Infrastructure
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#### Hardware
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- CPU for embedding expansion
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- GPU for quantization and inference testing
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#### Software
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- **Transformers**: v4.31.0+
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- **BitsAndBytes**: v0.41.0+
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- **PyTorch**: v2.0.0+
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- **Accelerate**: v0.20.0+
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## Model Card Authors
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- **Nassim JP** - Vocabulary expansion and quantization implementation
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- Original model: Mistral AI team
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## Model Card Contact
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For questions or collaboration:
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- Hugging Face: [nassimjp](https://huggingface.co/nassimjp)
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## Acknowledgments
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- Mistral AI for the base model
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- Stanford NLP for the vocabulary expansion method
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- Hugging Face for the transformers library
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- BitsAndBytes team for 4-bit quantization
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## Glossary
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- **Vocabulary Expansion**: Adding new tokens to a model's tokenizer
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- **Mean Resizing**: Initializing new embeddings with the mean of existing embeddings
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- **4-bit NF4**: 4-bit Normal Float quantization method
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- **Fine-tuning**: Training a pre-trained model on domain-specific data
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## Next Steps
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To make this model truly Pashto-capable, the next steps include:
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1. **Collect Pashto corpus** (books, articles, web text)
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2. **Pre-train on Pashto** data (continued pre-training)
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3. **Fine-tune** for specific tasks (translation, QA, generation)
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4. **Evaluate** on Pashto benchmarks
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5. **Deploy** in production environments
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## Citation
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If you use this model or the vocabulary expansion method, please cite:
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```bibtex
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@misc{ghanam2024pashto,
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author = {Nassim JP},
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title = {Ghanam-7B-Base-Pashto: Vocabulary Expansion for Pashto Language},
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year = {2024},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/nassimjp/Ghanam-7B-Base-Pashto-v0.1}}
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}
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```
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### Original Mistral-7B Citation
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```bibtex
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@article{jiang2023mistral,
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title={Mistral 7B},
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author={Jiang, Albert Q and others},
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journal={arXiv preprint arXiv:2310.06825},
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year={2023}
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}
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```
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---
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## Additional Information
|
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### Usage Tips
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1. **Tokenization**: The tokenizer now correctly handles all Pashto characters
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2. **Memory**: 4-bit quantization allows running on consumer GPUs with 6-8GB VRAM
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3. **Generation**: Use `do_sample=True` and `temperature=0.7` for diverse outputs
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4. **Pad Token**: If using batch generation, set `pad_token=tokenizer.eos_token`
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### Common Issues
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| Issue | Solution |
|
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|-------|----------|
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| Tokenizer missing characters | Ensure you're using the correct tokenizer from this repo |
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| Memory errors | Reduce `max_new_tokens` or use CPU offloading |
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| Poor generation quality | This is expected - the model needs fine-tuning |
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| Pashto text displayed as `[UNK]` | Tokenizer hasn't loaded properly - reload from the repo |
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---
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**Model Status:** 🔬 Research Phase - Pre-fine-tuning checkpoint
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**Last Updated:** June 2026
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```
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config.json
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
|
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"max_position_embeddings": 32768,
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"model_type": "mistral",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
|
||||
"rope_theta": 10000.0,
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"rope_type": "default"
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},
|
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"sliding_window": 4096,
|
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"tie_word_embeddings": false,
|
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"transformers_version": "5.5.0",
|
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"use_cache": true,
|
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"vocab_size": 32010
|
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}
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generation_config.json
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generation_config.json
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{
|
||||
"_from_model_config": true,
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"bos_token_id": 1,
|
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"eos_token_id": 2,
|
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"transformers_version": "5.5.0"
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}
|
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e87f36f81724f01054c7281eac32acff1163ecd3cb7dedcf84bc47032618032
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size 14483662064
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268476
tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
|
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"add_prefix_space": null,
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"backend": "tokenizers",
|
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"bos_token": "<s>",
|
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"clean_up_tokenization_spaces": false,
|
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"eos_token": "</s>",
|
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"extra_special_tokens": [],
|
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"is_local": true,
|
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"legacy": false,
|
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"model_max_length": 1000000000000000019884624838656,
|
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"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "TokenizersBackend",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user