Name: Llama-3.1-8B-Open-SFT
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Llama-3.1-8B-Open-SFT

The Llama-3.1-8B-Open-SFT model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct, designed for advanced text generation tasks, including conversational interactions, question answering, and chain-of-thought reasoning. This model leverages Supervised Fine-Tuning (SFT) using the O1-OPEN/OpenO1-SFT dataset to provide enhanced performance in context-sensitive and instruction-following tasks.

File Name	Size	Description	Upload Status
`.gitattributes`	1.57 kB	Git LFS configuration for tracking large files.	Uploaded
`README.md`	324 Bytes	Updated README with minimal information.	Uploaded
`config.json`	1.03 kB	Model configuration and metadata.	Uploaded
`generation_config.json`	248 Bytes	Configuration for text generation specifics.	Uploaded
`pytorch_model-00001-of-00004.bin`	4.98 GB	First shard of PyTorch model.	Uploaded (LFS)
`pytorch_model-00002-of-00004.bin`	5.00 GB	Second shard of PyTorch model.	Uploaded (LFS)
`pytorch_model-00003-of-00004.bin`	4.92 GB	Third shard of PyTorch model.	Uploaded (LFS)
`pytorch_model-00004-of-00004.bin`	1.17 GB	Final shard of PyTorch model.	Uploaded (LFS)
`pytorch_model.bin.index.json`	24.2 kB	Index file for model shards.	Uploaded
`special_tokens_map.json`	357 Bytes	Map for special tokens used in tokenizer.	Uploaded
`tokenizer.json`	17.2 MB	Full tokenizer JSON file.	Uploaded (LFS)
`tokenizer_config.json`	57.4 kB	Configuration for the tokenizer.	Uploaded

Sample Long CoT:

Key Features

Text Generation with CoT Reasoning:
- Implements Chain-of-Thought (CoT) prompting for logical and step-by-step reasoning tasks.
Conversational AI:
- Excels in generating context-aware and coherent responses in multi-turn conversations.
Supervised Fine-Tuning (SFT):
- Optimized for open-domain tasks using the O1-OPEN/OpenO1-SFT dataset.
Multi-Purpose Functionality:
- Supports a wide range of NLP tasks, including summarization, question answering, and text completion.
Scalable Sharded Architecture:
- Model weights are distributed across four shards, ensuring efficient loading for large-scale applications.

Training Details

Base Model: meta-llama/Llama-3.1-8B
Finetuned Dataset: O1-OPEN/OpenO1-SFT
- Dataset includes 77.7k fine-tuning samples, curated for instruction-based and open-domain tasks.
Model Size:
- 8 Billion parameters distributed over 4 shards for efficient deployment.

Applications

Chain-of-Thought (CoT) Reasoning:
- Solve complex problems step-by-step with logical reasoning capabilities.
Conversational Agents:
- Ideal for chatbots, virtual assistants, and conversational systems.
Question Answering:
- Answer open-domain or context-specific questions accurately.
Text Completion:
- Generate coherent continuations for incomplete inputs.
Creative Writing:
- Support for generating stories, articles, or brainstorming ideas.

Usage

Loading the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Llama-3.1-8B-Open-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Inference Example

python

prompt = """
Explain the concept of gravity in a simple way suitable for a 10-year-old:
"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150, temperature=0.7)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Model Output:", response)

Expected Output

"Gravity is a force that pulls things toward each other. It's the reason why things fall to the ground when you drop them. On Earth, gravity keeps us on the ground and makes sure everything stays in place, like your toys, the water in the ocean, and even the air we breathe."

Performance Requirements

Hardware:
- High-performance GPUs are recommended for efficient inference.
- Minimum memory: ~16GB VRAM for full precision; 8GB for quantized models.
Optimization Options:
- Use Safetensors for secure and efficient weight loading.
- Quantization or model parallelism for resource-constrained environments.