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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- HuggingFaceTB/SmolLM2-360M-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- Grpo |
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- text-generation-inference |
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- Llama |
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- trl |
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--- |
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# **SmolLM2-360M-Grpo-r999** |
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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. |
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### **How to Use** |
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### Transformers |
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```bash |
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pip install transformers |
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``` |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "prithivMLmods/SmolLM2-360M-Grpo-r999" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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messages = [{"role": "user", "content": "What is gravity?"}] |
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input_text = tokenizer.apply_chat_template(messages, tokenize=False) |
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print(input_text) |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### **Limitations of SmolLM2-360M-Grpo-r999** |
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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. |
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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. |
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3. **Context Length**: The model might struggle with very long conversations or extended prompts, potentially leading to truncation or loss of contextual coherence. |
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4. **Fine-Tuning Specificity**: Performance on specialized domains may require additional fine-tuning with domain-specific datasets. |
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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. |
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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**. |
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### **Intended Use of SmolLM2-360M-Grpo-r999** |
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1. **General-purpose Conversational AI** – Ideal for chatbots, virtual assistants, and interactive applications requiring basic reasoning and knowledge retrieval. |
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2. **Education & Tutoring** – Supports answering educational queries, explaining concepts, and aiding learning across multiple domains. |
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3. **Content Generation** – Can generate short-form text, summaries, and brainstorming ideas for writing assistants or creativity tools. |
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4. **Code Assistance** – Fine-tuned on programming datasets, making it useful for debugging, explaining code, and assisting developers. |
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5. **Instruction Following** – Optimized for following structured commands, making it suitable for task-based applications. |
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6. **Prototyping & Experimentation** – Lightweight model for **fast deployment** in new AI applications, balancing performance with efficiency. |
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7. **Low-Resource Environments** – Runs on **edge devices, mobile apps, and local servers** where larger models are infeasible. |
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8. **Research & Development** – Can be used as a base model for **further fine-tuning** or model optimizations. |
