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Model: puravky/chatOP
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+tags:
+- llm
+- fine-tuned
+- lora
+- sft
+- text-generation
+- student-project
+datasets:
+- HuggingFaceTB/smoltalk
+pipeline_tag: text-generation
+---
+
+# chatOP — SmolLM2-135M Fine-tuned
+
+A fine-tuned version of [SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) trained to act as a friendly study assistant for CS and ML concepts.
+
+## Model Details
+
+| | |
+|---|---|
+| **Base model** | HuggingFaceTB/SmolLM2-135M-Instruct |
+| **Model type** | Causal Language Model |
+| **Fine-tuning method** | SFT + LoRA |
+| **Language** | English |
+| **License** | Apache 2.0 |
+
+## Training Details
+
+### Dataset
+- **Name:** [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
+- **Split used:** `all`
+- **Format:** Conversational chat messages (system, user, assistant)
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Learning rate | 2e-4 |
+| Training steps | 200 |
+| Batch size | 2 |
+| Packing | True |
+| Optimizer | AdamW |
+| LR scheduler | Linear |
+| Precision | fp16 |
+
+### LoRA Config
+
+| Parameter | Value |
+|---|---|
+| Rank (`r`) | 16 |
+| Alpha (`lora_alpha`) | 16 |
+| Dropout | 0.05 |
+| Target modules | all-linear |
+| Bias | none |
+| Trainable parameters | ~1.7M / 135M (1.26%) |
+
+### Hardware
+- **GPU:** NVIDIA Tesla T4 (Google Colab)
+- **Training time:** ~15–20 minutes
+
+## Example Outputs
+
+**Prompt:** What is machine learning?
+
+> Machine learning is a field of AI where systems learn from data to make predictions or decisions without being explicitly programmed for each task.
+
+**Prompt:** Explain transformers in simple terms.
+
+> A Transformer is a neural network architecture that uses attention mechanisms to understand relationships between all words in a sentence simultaneously, rather than reading them one by one like older models did.
+
+**Prompt:** What is the difference between LoRA and full fine-tuning?
+
+> Full fine-tuning updates all parameters of a model which requires a lot of memory. LoRA instead freezes the base model and adds small trainable matrices to each layer, training only ~1% of parameters while achieving similar results.
+
+## Limitations
+
+- Small model (135M parameters) — responses may be less detailed than larger models
+- Trained for only 200 steps — longer training would improve quality
+- Best for short explanations — may struggle with very complex multi-step reasoning
+- Not suitable for factual lookups — may hallucinate specific facts or numbers
+
+## Training Framework
+
+Built as a learning project while studying the [HuggingFace LLM Course](https://huggingface.co/learn/llm-course) — specifically Chapter 11 (Fine-tuning LLMs).
+
+**Libraries used:**
+- 🤗 Transformers
+- 🤗 PEFT
+- TRL (SFTTrainer)
+- 🤗 Datasets
+- Accelerate
+
+## Author
+
+Made by [puravky](https://huggingface.co/puravky) — undergrad student exploring ML and AI.

chat_template.jinja

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+{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
+You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.041666666666666664,
+  "intermediate_size": 1536,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_parameters": {
+    "rope_theta": 100000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "5.9.0",
+  "use_cache": false,
+  "vocab_size": 49152
+}

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": "5.9.0"
+}

model.safetensors

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}