base_model, tags, license, language, datasets

base_model	tags	license	language	datasets
unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit	text-generation-inference transformers unsloth llama	apache-2.0	en	Sharathhebbar24/Evol-Instruct-Code-80k-v1

Saif-1.0-Coder

A code-focused assistant fine-tuned from Llama 3.2 3B Instruct.

Model Details

• Base model: unsloth/Llama-3.2-3B-Instruct
• Fine-tuned by: Saif658
• Training: QLoRA 4-bit, 500 steps
• Dataset: Sharathhebbar24/Evol-Instruct-Code-80k-v1
• License: Apache 2.0

What it's good at

• Writing code in Python, JavaScript, Java, C, C++, C#, TypeScript, PHP, Go, Rust
• Explaining code and algorithms
• Debugging and fixing code

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Saif658/Saif-1.0-Coder")
model = AutoModelForCausalLM.from_pretrained(
    "Saif658/Saif-1.0-Coder",
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [{"role": "user", "content": "Write a binary search in Python"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Small 3B model — may struggle with very complex or long codebases.