Update README.md

README.md

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----
-license: apache-2.0
----
+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- safetensors
+- onnx
+- transformers.js
+base_model:
+- HuggingFaceTB/SmolLM2-135M
+---
+This model has been trained on human generated, AI Reasoned questions and answers https://huggingface.co/datasets/KingNish/reasoning-base-20k .
+# Uploaded  model
+
+- **Developed by:** ewre324
+- **License:** apache-2.0
+- **Finetuned from model :** HuggingFaceTB/SmolLM2-135M-Instruct
+
+# SmolLM2-Reasoning
+
+##  Table of Contents
+
+1. [Model Summary](##model-summary)
+2. [Limitations](##limitations)
+3. [Training](##training)
+4. [License](##license)
+5. [Citation](##citation)
+
+## Model Summary
+
+SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.
+
+SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets 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. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
+
+The instruct model additionally supports tasks such as text rewriting, summarization and function calling (for the 1.7B) thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1).
+You can find the SFT dataset here: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk and finetuning code at https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2
+
+### How to use
+
+### Transformers
+```bash
+pip install transformers
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct"
+
+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]))
+```
+
+### Chat in TRL
+You can also use the TRL CLI to chat with the model from the terminal:
+```bash
+pip install trl
+trl chat --model_name_or_path HuggingFaceTB/SmolLM2-135M-Instruct --device cpu
+```
+
+## Evaluation
+TODO
+
+
+
+## Limitations
+
+SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
+
+## Training
+
+### Model
+
+- **Architecture:** Transformer decoder
+- **Pretraining tokens:** 2T
+- **Precision:** bfloat16
+
+### Hardware
+
+- **GPUs:** 2 A100
+
+### Software
+
+- **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main)
+
+## License
+
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)