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Model: prithivMLmods/SmolLM2-360M-Grpo-r999
Source: Original Platform

.gitattributes

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README.md

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+---
+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.

config.json

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+{
+  "_name_or_path": "HuggingFaceTB/SmolLM2-360M-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 960,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 15,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 5,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_scaling": null,
+  "rope_theta": 100000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "4.48.3",
+  "use_cache": false,
+  "vocab_size": 49152
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.48.3"
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
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+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}