Update README.md

README.md

@@ -1,33 +1,65 @@
 ---
 license: apache-2.0
 base_model: openchat/openchat_3.5
+datasets:
+- HuggingFaceH4/no_robots
+language:
+- en
 tags:
 - generated_from_trainer
+pipeline_tag: text-generation
 model-index:
 - name: smol-7b
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Smol 7B
 
-# smol-7b
+This model is a fine-tuned version of [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) on the open source dataset [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) using the recipes published in [The Alignment Handbook](https://github.com/huggingface/alignment-handbook).
 
-This model is a fine-tuned version of [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 2.0409
+## Model date
 
-## Model description
+rishiraj/smol-7b was trained between 1st and 3rd December, 2023.
 
-More information needed
+## Evaluation
 
-## Intended uses & limitations
+It achieves the following results on the [Open_LLM_Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). At the time of release, smol-7b is the highest ranked 7B chat model on the [MMLU Benchmark](https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu).
 
-More information needed
+| Model                        | Average | ARC   | HellaSwag | MMLU  | TruthfulQA | Winogrande | GSM8K |
+| ---------------------------- | ------- | ----- | --------- | ----- | ---------- | ---------- | ----- |
+| rishiraj/smol-7b             | 67.11   | 63.74 | 84.77     | 65    | 46.17      | 80.66      | 62.32 |
+| argilla/notus-7b-v1          | 63.49   | 64.59 | 84.83     | 63.04 | 54.35      | 79.56      | 34.57 |
+| Intel/neural-chat-7b-v3-1    | 61.59   | 66.21 | 83.64     | 62.37 | 59.65      | 78.14      | 19.56 |
+| HuggingFaceH4/zephyr-7b-beta | 61.59   | 62.46 | 84.35     | 60.7  | 57.83      | 77.11      | 27.07 |
+| Qwen/Qwen-7B                 | 59.19   | 51.37 | 78.47     | 59.84 | 47.79      | 72.69      | 44.96 |
+| microsoft/Orca-2-7b          | 54.55   | 54.1  | 76.19     | 56.37 | 52.45      | 73.48      | 14.71 |
+| 01-ai/Yi-6B                  | 54.08   | 55.55 | 76.57     | 64.11 | 41.96      | 74.19      | 12.13 |
 
-## Training and evaluation data
+## Inference procedure
 
-More information needed
+Here's how you can run the model using the pipeline() function from 🤗 Transformers:
+
+```
+import torch
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="rishiraj/smol-7b", torch_dtype=torch.bfloat16, device_map="auto")
+
+# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
+messages = [
+    {
+        "role": "system",
+        "content": "You are a friendly chatbot who always responds in the style of a pirate"
+    },
+    {
+        "role": "user",
+        "content": "How many helicopters can a human eat in one sitting?"
+    }
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
 
 ## Training procedure
 
@@ -58,3 +90,16 @@ The following hyperparameters were used during training:
 - Pytorch 2.1.1+cu121
 - Datasets 2.14.6
 - Tokenizers 0.14.1
+
+## Citation Information
+
+```
+@misc{rishiraj2023smol,
+  author = {Rishiraj Acharya},
+  title = {Smol 7B},
+  year = {2023},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\url{https://huggingface.co/rishiraj/smol-7b}}
+}
+```