speechless-thoughts-mistral-7b/README.md

language, library_name, pipeline_tag, datasets, tags, license, model-index

language	library_name	pipeline_tag	datasets	tags	license	model-index
en	transformers	text-generation	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	llama-2 code	llama2	name results SpeechlessCoder task dataset metrics type text-generation type name openai_humaneval HumanEval name type value verified pass@1 pass@1 false

speechless-thoughts-mistral-7b

code

speechless-thoughts-mistral-7b is fine-tuned as a baseline of the 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

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

{'ARC (acc_norm)': ,
'HellaSwag (acc_norm)': ,
'MMLU (acc)': ,
'TruthfulQA (mc2)': ,
'Winoground (acc)': ,
'GSM8K (acc)': ,
'DROP (f1)': ,
'Open LLM Score': }

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

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