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ujjwaljha/
Safar-1.0

Name: Safar-1.0
Availability: InStock

safetensors

gpt2

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🤖 Safar-1.0

Safar-1.0 is a fine-tuned GPT-2 Medium (345M parameters) model trained on general English conversations and Computer Science educational content.

🧠 Model Details

Base Model: GPT-2 Medium (345M parameters)
Architecture: GPT2LMHeadModel
Framework: PyTorch + Transformers
Training Platform: Google Colab (T4 GPU)
License: MIT (inherits GPT-2 license)

📊 Evaluation Results

Evaluated using EleutherAI lm-eval-harness (0-shot):

ARC-Easy: 50.6%
HellaSwag: 33.1%
Winogrande: 53.2%

Performance is comparable to GPT-2 Medium baseline with minor improvements in reasoning consistency.

📚 Training Data

The model was fine-tuned on a mixture of open datasets including:

General conversational text
Computer Science educational material
Programming-related discussions
Academic-style explanations
Public domain open datasets

⚙️ Training Details

GPU: NVIDIA T4
Training Time: ~25–45 minutes
Optimizer: AdamW
Fine-tuned using HuggingFace Trainer
Final Training Loss: (add your value here)

💬 Intended Use

Safar-1.0 is designed for:

General chat
CS topic explanations
Educational Q&A
English text generation

⚠️ Limitations

May hallucinate factual information
Not aligned with RLHF
Not suitable for critical production usage
Performance lower than modern large LLMs

🚀 Example Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ujjwaljha/Safar-1.0"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Explain binary search in simple terms."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)

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

📊 Model Type

💬 Chat Model (Instruction-style fine-tuned GPT-2)

👤 Author

Built and trained by Ujjwal Jha.