Sachin21112004/Sancara_text_generation

Sancara – Instruction-Tuned Text Generation Model

This repository contains the full Sancara text generation model, exported as a standard Hugging Face Transformers checkpoint (model.safetensors + tokenizer). The model is optimized for instruction following, chat-style dialogue, question answering, and general-purpose text generation.

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Model overview

• Repository: Sachin21112004/Sancara_text_generation
• Model type: Causal language model (decoder-only) for text generation
• Language: English
• License: SRL(others)
• Status: Merged, standalone model (not only a LoRA adapter)

The repo includes both:

• A merged full model in model.safetensors, and
• An adapter file adapter_model.safetensors from a previous LoRA-based phase.

For most users, loading model.safetensors via AutoModelForCausalLM is the recommended way to use Sancara.

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Files in this repository

Key files:

• model.safetensors – full model weights (~2.84 GB)
• config.json – model architecture and configuration
• generation_config.json – default generation parameters
• tokenizer.json, tokenizer_config.json, vocab.json, merges.txt – tokenizer and BPE merges
• special_tokens_map.json, added_tokens.json – definition of special and extra tokens
• adapter_model.safetensors – LoRA adapter weights (optional use)
• training_args.bin – serialized Hugging Face Trainer arguments
• checkpoint-12000/, checkpoint-12992/ – intermediate training checkpoints

If you just want to run the model, you only need the main repo id: Sachin21112004/Sancara_text_generation.

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Intended use

Direct use

The model is intended for:

• Instruction following (task-style prompts with clear instructions)
• Chatbots and conversational agents
• Question answering and explanation-style responses
• General light-weight reasoning and text generation

Example applications:

• Personal AI assistants
• Educational or coding helpers
• Internal tools that need a natural language interface

Out-of-scope use

This model is not suitable for:

• Medical, legal, financial, or other professional advice
• High-risk decision-making without human supervision
• Generating harmful, abusive, or disallowed content

Always keep a human in the loop for any sensitive or production-critical usage.

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Quick start (inference)

Basic text generation

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Sachin21112004/Sancara_text_generation"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,  # or float16/float32 depending on hardware
    device_map="auto",
)

prompt = "Explain how transformers-based large language models work in simple terms."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

output_ids = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

You can override generation parameters in the code above or rely on generation_config.json which stores defaults shipped with the model.

Using an intermediate checkpoint

If you want to inspect or continue training from a specific checkpoint:

from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "Sachin21112004/Sancara_text_generation"
ckpt_id = "Sachin21112004/Sancara_text_generation/checkpoint-12992"

tokenizer = AutoTokenizer.from_pretrained(base_id)
model = AutoModelForCausalLM.from_pretrained(ckpt_id)

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(Optional) Using the LoRA adapter

The repository still contains adapter_model.safetensors from a LoRA fine-tuning stage. If you want to reproduce an adapter-based setup instead of the merged full model, you can:

1. Load the original base model (e.g. microsoft/phi-2 or your chosen base).
2. Load the LoRA adapter with peft and apply it on top.

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "microsoft/phi-2"  # or the base you originally used
adapter_repo = "Sachin21112004/Sancara_text_generation"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype="auto",
    device_map="auto",
)

model = PeftModel.from_pretrained(base_model, adapter_repo)

Most users can ignore this and just use the merged model.safetensors.

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Training and data

The final Sancara model was trained with Hugging Face's Trainer, with arguments stored in training_args.bin. Training was performed as supervised fine-tuning for instruction following and chat, on high-quality conversational and instruction-style datasets such as:

• HuggingFaceH4/ultrachat_200k
• databricks/databricks-dolly-15k

High-level training setup:

• Objective: Causal language modeling (next token prediction)
• Format: Instruction–response pairs and multi-turn chats
• Infrastructure: Standard Transformers + Trainer pipeline
• Checkpoints: Saved periodically (e.g. checkpoint-12000, checkpoint-12992), then merged into model.safetensors

If you want to continue training, you can load one of the checkpoints as initialization and reuse training_args.bin or your own training script.

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Limitations and risks

• The model can hallucinate facts, dates, and citations.
• Outputs may reflect biases or stereotypes from training data.
• It may produce toxic, offensive, or otherwise undesirable content if prompted directly.

Recommended mitigations:

• Use prompt filtering and output moderation in downstream applications.
• Keep humans in the loop for any important or high-impact use.
• Evaluate on your own tasks and domains before deploying in production.

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How to cite / attribution

If you use this model in your work, please credit:

Sancara – Instruction-Tuned Text Generation Model, by Sachin (Sachin21112004 on Hugging Face).

And link to the model card:

https://huggingface.co/Sachin21112004/Sancara_text_generation