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Model: uukuguy/speechless-thoughts-mistral-7b
Source: Original Platform

README.md

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+---
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- jondurbin/airoboros-2.2
+- Open-Orca/OpenOrca
+- garage-bAInd/Open-Platypus
+- WizardLM/WizardLM_evol_instruct_V2_196k
+- TokenBender/python_eval_instruct_51k
+- codefuse-ai/Evol-Instruction-66k
+tags:
+- llama-2
+- code
+license: llama2
+model-index:
+- name: SpeechlessCoder
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 
+      verified: false
+---
+
+<p><h1> speechless-thoughts-mistral-7b  </h1></p>
+
+[code](https://github.com/uukuguy/multi_loras)
+
+speechless-thoughts-mistral-7b is fine-tuned as a baseline of the [speechless-sparsetral-16x7b-MoE](https://huggingface.co/uukuguy/speechless-sparsetral-16x7b-MoE). 
+
+The specific datasets (speechless-thoughts-252k) are as follows:
+
+- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
+- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
+- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
+- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
+- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
+- Spider: 8,659 samples
+- codefuse-ai/Evol-Instruction-66k: 100%, 66,862 samples
+
+## Alpaca Prompt Format
+
+```
+### Instruction:
+<instruction>
+### Response:
+```
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name_or_path="uukuguy/speechless-thoughts-mistral-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
+  model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=True).eval()
+
+  system = ""Below is an instruction that describes a task.\nWrite a response that appropriately completes the request.\n\n""
+  prompt = f"{system}\n\n### Instruction:\n{instruction}\n\n### Response:"
+
+  inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+  pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1)
+  print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
+```
+
+## HumanEval
+
+| Metric | Value |
+| --- | --- |
+| humaneval-python | |
+
+## lm-evaluation-harness
+
+```json
+{'ARC (acc_norm)': ,
+'HellaSwag (acc_norm)': ,
+'MMLU (acc)': ,
+'TruthfulQA (mc2)': ,
+'Winoground (acc)': ,
+'GSM8K (acc)': ,
+'DROP (f1)': ,
+'Open LLM Score': }
+```
+
+# [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_uukuguy__speechless-thoughts-mistral-7b)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 59.72   |
+| ARC (25-shot)         |    58.96       |
+| HellaSwag (10-shot)   |  80.71   |
+| MMLU (5-shot)         |  60.11        |
+| TruthfulQA (0-shot)   |  49.91  |
+| Winogrande (5-shot)   |  77.82  |
+| GSM8K (5-shot)        |  30.78       |
+