初始化项目，由ModelHub XC社区提供模型

Model: Sharathhebbar24/SSH_355M
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- databricks/databricks-dolly-15k
+- gamino/wiki_medical_terms
+- Sharathhebbar24/openhermes
+- Sharathhebbar24/Open-Platypus
+- Sharathhebbar24/sql-create-context
+- Sharathhebbar24/Evol-Instruct-Code-80k-v1
+- Sharathhebbar24/BeaverTails_filtered
+- Sharathhebbar24/arxiv-math-instruct-50k
+- Sharathhebbar24/MetaMathQA
+- Intel/orca_dpo_pairs
+model-index:
+- name: SSH_300M
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 28.24
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/SSH_300M
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 38.74
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/SSH_300M
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 27.03
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/SSH_300M
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 42.51
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/SSH_300M
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 53.67
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/SSH_300M
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 0.3
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/SSH_300M
+      name: Open LLM Leaderboard
+---
+
+
+This model is a finetuned version of ```gpt2-medium```
+
+## Model description
+
+GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This
+means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
+of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
+it was trained to guess the next word in sentences.
+
+More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
+shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
+predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
+prompt.
+
+### To use this model
+
+```python
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM
+>>> model_name = "Sharathhebbar24/SSH_355M"
+>>> model = AutoModelForCausalLM.from_pretrained(model_name)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+>>> def generate_text(prompt):
+>>>  inputs = tokenizer.encode(prompt, return_tensors='pt')
+>>>  outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
+>>>  generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+>>>  return generated[:generated.rfind(".")+1]
+
+>>> generate_text("Should I Invest in stocks")
+
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__SSH_300M)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |31.75|
+|AI2 Reasoning Challenge (25-Shot)|28.24|
+|HellaSwag (10-Shot)              |38.74|
+|MMLU (5-Shot)                    |27.03|
+|TruthfulQA (0-shot)              |42.51|
+|Winogrande (5-shot)              |53.67|
+|GSM8k (5-shot)                   | 0.30|
+