library_name, tags, base_model, datasets, language, pipeline_tag

library_name	tags	base_model	datasets	language	pipeline_tag
transformers	text-generation casual-lm sft trl	Qwen/Qwen2.5-3B	HuggingFaceTB/smoltalk	en	text-generation

Qwen-2.5-3B Smoltalk SFT

This is a fine-tuned version of the 3-billion parameter Qwen/Qwen2.5-3B base model. It has been instruction fine-tuned via Low-Rank Adaptation (LoRA) and fully merged.

Model Details

• Base Model: Qwen/Qwen2.5-3B
• Fine-tuning Dataset: HuggingFaceTB/smoltalk (everyday-conversations subset)
• Methodology: Supervised Fine-Tuning (SFT) using TRL
• Hardware Used: 1 x NVIDIA L4 GPU (24GB VRAM)

How to Get Started

You can load and use this model directly with the Hugging Face pipeline API.

import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "Kerassy/qwen-2.5-3b-smoltalk-sft"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16,
    device_map="auto"
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

messages = [
    {"role": "user", "content": "Why is the sky blue?"}
]

formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

outputs = pipe(
    formatted_prompt, 
    max_new_tokens=128, 
    do_sample=True, 
    temperature=0.7,
    top_k=40,
    clean_up_tokenization_spaces=False,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.encode("<|end|>")[0] if "<|end|>" in tokenizer.get_vocab() else tokenizer.eos_token_id
)

print(outputs[0]['generated_text'])