---
license: apache-2.0
language:
- en
- zh
base_model:
- HuggingFaceTB/SmolLM2-360M-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Grpo
- text-generation-inference
- Llama
- trl
---
![d9-mAgyravvwWXZGi3sK5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jTUNV5nFY_tyhYQM-zeXl.png)

# **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
```bash
pip install transformers
```

```python
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.