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README.md

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+---
+license: apache-2.0
+base_model: openchat/openchat_3.5
+datasets:
+- HuggingFaceH4/no_robots
+language:
+- en
+widget:
+  - text: |
+      <|system|>
+      You are a friendly chatbot who always responds in the style of a pirate</s>
+      <|user|>
+      How many helicopters can a human eat in one sitting?</s>
+      <|assistant|>
+    output:
+      text: >-
+        Ahoy there, me hearty! As a friendly pirate chatbot, I be tellin' ye that a human cannot eat a helicopter, as it be a large machine made of metal and suchlike, not fit for human consumption. A human can eat food, like a fine feast of roasted meat and sweet fruits, but a helicopter? That be nonsense, me hearty! So, the answer be none, none at all. Arr!
+tags:
+- generated_from_trainer
+pipeline_tag: text-generation
+model-index:
+- name: smol-7b
+  results: []
+---
+
+# 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).
+
+## Model date
+
+rishiraj/smol-7b was trained between 1st and 3rd December, 2023.
+
+## Evaluation
+
+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).
+
+| 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 |
+
+## Inference procedure
+
+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
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 128
+- total_train_batch_size: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 2.0569        | 0.16  | 3    | 2.0409          |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- 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}}
+}
+```

added_tokens.json

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+{
+  "<|end_of_turn|>": 32000,
+  "<|pad_0|>": 32001
+}

all_results.json

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+{
+    "epoch": 0.16,
+    "eval_loss": 2.0409016609191895,
+    "eval_runtime": 14.9343,
+    "eval_samples": 500,
+    "eval_samples_per_second": 33.48,
+    "eval_steps_per_second": 4.218,
+    "train_loss": 2.1085566679636636,
+    "train_runtime": 937.9718,
+    "train_samples": 9500,
+    "train_samples_per_second": 10.128,
+    "train_steps_per_second": 0.019
+}

config.json

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+{
+  "_name_or_path": "openchat/openchat_3.5",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.2",
+  "use_cache": true,
+  "vocab_size": 32002
+}

eval_results.json

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+{
+    "epoch": 0.16,
+    "eval_loss": 2.0409016609191895,
+    "eval_runtime": 14.9343,
+    "eval_samples": 500,
+    "eval_samples_per_second": 33.48,
+    "eval_steps_per_second": 4.218
+}

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