SmolLM2-360M-Grpo-r999

SmolLM2-360M-Grpo-r999

SmolLM2-360M-Grpo-r999 is fine-tuned based on SmolLM2-360M-Instruct. SmolLM2 demonstrates significant advances over its predecessor, SmolLM1, particularly in instruction following, knowledge, and reasoning. The 360M model was trained on 2 trillion tokens using a diverse combination of datasets: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets.

How to Use

Transformers

pip install transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "prithivMLmods/SmolLM2-360M-Grpo-r999"

device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

messages = [{"role": "user", "content": "What is gravity?"}]
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
print(input_text)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))

Limitations of SmolLM2-360M-Grpo-r999

1. Model Size: While 360M parameters provide enhanced capabilities, the model still has limitations in handling highly complex reasoning tasks or long-context dependencies compared to larger models.

2. Bias and Inaccuracy: Despite fine-tuning on diverse datasets, the model may generate biased, inaccurate, or factually incorrect responses, particularly for niche topics or specialized knowledge areas.

3. Context Length: The model might struggle with very long conversations or extended prompts, potentially leading to truncation or loss of contextual coherence.

4. Fine-Tuning Specificity: Performance on specialized domains may require additional fine-tuning with domain-specific datasets.

5. Generalization: The model may not generalize as effectively to rare queries or unseen tasks compared to larger models, sometimes providing generic or incomplete answers.

6. Limited Multi-Turn Conversations: While it supports multi-turn interactions, its ability to retain and use context over extended conversations is not as strong as larger models.

Intended Use of SmolLM2-360M-Grpo-r999

1. General-purpose Conversational AI – Ideal for chatbots, virtual assistants, and interactive applications requiring basic reasoning and knowledge retrieval.

2. Education & Tutoring – Supports answering educational queries, explaining concepts, and aiding learning across multiple domains.

3. Content Generation – Can generate short-form text, summaries, and brainstorming ideas for writing assistants or creativity tools.

4. Code Assistance – Fine-tuned on programming datasets, making it useful for debugging, explaining code, and assisting developers.

5. Instruction Following – Optimized for following structured commands, making it suitable for task-based applications.

6. Prototyping & Experimentation – Lightweight model for fast deployment in new AI applications, balancing performance with efficiency.

7. Low-Resource Environments – Runs on edge devices, mobile apps, and local servers where larger models are infeasible.

8. Research & Development – Can be used as a base model for further fine-tuning or model optimizations.