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Model: gokaygokay/SmolLM2-Prompt-Enhance Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- trl
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- sft
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license: apache-2.0
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datasets:
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- gokaygokay/prompt-enhancement-75k
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language:
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- en
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base_model:
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- HuggingFaceTB/SmolLM2-135M-Instruct
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pipeline_tag: text-generation
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---
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_id = "gokaygokay/SmolLM2-Prompt-Enhance"
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tokenizer_id = "HuggingFaceTB/SmolLM2-135M-Instruct"
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_id )
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model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
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# Model response generation functions
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def generate_response(model, tokenizer, instruction, device="cpu"):
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"""Generate a response from the model based on an instruction."""
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messages = [{"role": "user", "content": instruction}]
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input_text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
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outputs = model.generate(
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inputs, max_new_tokens=256, repetition_penalty=1.2
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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def print_response(response):
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"""Print the model's response."""
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print(f"Model response:")
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print(response.split("assistant\n")[-1])
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print("-" * 100)
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prompt = "cat"
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response = generate_response(model, tokenizer, prompt, device)
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print_response(response)
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# a gray cat with white fur and black eyes is in the center of an open window on a concrete floor.
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# The front wall has two large windows that have light grey frames behind them.
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# here is a small wooden door to the left side of the frame at the bottom right corner.
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# A metal fence runs along both sides of the image from top down towards the middle ground.
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# Behind the cats face away toward the camera's view it appears as if there is another cat sitting next to the one
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# they're facing forward against the glass surface above their head.
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```
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### Training Script
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https://colab.research.google.com/drive/1Gqmp3VIcr860jBnyGYEbHtCHcC49u0mo?usp=sharing
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<!-- gokaygokay-citation -->
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## Citation and attribution
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This model release is maintained by [Gökay Aydoğan](https://orcid.org/0000-0002-2343-9433). If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.
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```bibtex
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@software{aydogan2024smollm2_prompt_enhance,
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author = {Aydoğan, Gökay},
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title = {{SmolLM2-Prompt-Enhance}},
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year = {2024},
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publisher = {Hugging Face},
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url = {https://huggingface.co/gokaygokay/SmolLM2-Prompt-Enhance},
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note = {Model repository; cite the base model and upstream datasets as required.}
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}
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```
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config.json
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{
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"_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 576,
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"initializer_range": 0.041666666666666664,
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"intermediate_size": 1536,
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"is_llama_config": true,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 9,
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"num_hidden_layers": 30,
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"num_key_value_heads": 3,
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"pad_token_id": 2,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_interleaved": false,
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"rope_scaling": null,
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"rope_theta": 100000,
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"tie_word_embeddings": true,
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"torch_dtype": "float32",
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"transformers.js_config": {
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"kv_cache_dtype": {
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"fp16": "float16",
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"q4f16": "float16"
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}
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},
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"transformers_version": "4.46.1",
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"use_cache": true,
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"vocab_size": 49152
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:7d27c493c729a66ecefc837280b05d948b1ed50d130eebdbf911b1b36cf38ed7
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