README.md

---
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       |