---
base_model: amd/Instella-3B-Instruct

---

# MISHANM/amd-Instella-3B-Instruct-fp8

This model represents an fp8 quantized adaptation of the Instella-3B-Instruct, specifically engineered for deployment on compatible hardware platforms. It offers enhanced computational efficiency, ensuring faster processing and reduced resource usage, while consistently maintaining the high-quality performance characteristics of the original model.


## Model Details
1. Tasks: Causal Language Modeling, Text Generation
2. Base Model: amd/Instella-3B-Instruct
3. Quantization Format: fp8



# Device Used

1. GPUs: 1*AMD Instinct™ MI210 Accelerators 
  
   


 ## Inference with HuggingFace
 ```python3
 
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_path = "MISHANM/amd-Instella-3B-Instruct-fp8"

model = AutoModelForCausalLM.from_pretrained(model_path,device_map="auto")

tokenizer = AutoTokenizer.from_pretrained(model_path)

# Function to generate text
def generate_text(prompt, max_length=1000, temperature=0.9):
    # Format the prompt according to the chat template
    messages = [
        {
            "role": "system",
            "content": "Give response to the user query.",
        },
        {"role": "user", "content": prompt}
    ]

    # Apply the chat template
    formatted_prompt = f"<|system|>{messages[0]['content']}<|user|>{messages[1]['content']}<|assistant|>"

    # Tokenize and generate output
    inputs = tokenizer(formatted_prompt, return_tensors="pt")
    output = model.generate(  # Use model.module for DataParallel
        **inputs, max_new_tokens=max_length, temperature=temperature, do_sample=True
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
prompt = """Give a poem on LLM ."""
text = generate_text(prompt)
print(text)



```

## Citation Information
```
@misc{MISHANM/amd-Instella-3B-Instruct-fp8,
  author = {Mishan Maurya},
  title = {Introducing fp8 quantized version of amd/Instella-3B-Instruct},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  
}
```