ShearedPlats-7b Instruct
This is an ShearedPlats-7b model that has been fine-tuned on 2 epochs of the Open-Platypus dataset.
The modified version of the dataset can be found here
Prompt Template
### Instruction:
{query}
### Response:
<Leave new line for model to respond>
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1")
query = "Provide step-by-step instructions for making a sweet chicken bugger"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=350)
output = text_gen(f"### Instruction:\n{query}\n### Response:\n")
print(output[0]['generated_text'])
"""
### Instruction:
Provide step-by-step instructions for making a sweet chicken bugger
### Response:
Step 1: Prepare the ingredients
You will need a mixture of ground chicken, breadcrumbs, butter, Worcestershire sauce, garlic powder, onion powder, salt, and pepper.
Step 2: Form the bugger
Take a piece of chicken breast meat and use a sharp knife to cut it into small cubes. Place the cubes in a bowl and add the remaining ingredients: breadcrumbs, butter, Worcestershire sauce, garlic powder, onion powder, salt, and pepper. Mix the ingredients together until they are well combined.
Step 3: Shape the bugger
Take a piece of the bugger mixture and form it into a ball. Place the ball on a plate or in a bag and refrigerate it for 30 minutes.
Step 4: Cook the bugger
Heat a grill pan or grill to medium-high heat. Take the bugger out of the refrigerator and place it on the grill. Cook the bugger for 5-7 minutes on each side, or until it is cooked through.
Step 5: Serve and enjoy!
Once the bugger is cooked, serve it hot and enjoy!
Note: You can also use a sweet chicken bugger mix to make sweet chicken buggers. Simply follow the instructions above, but use the sweet chicken bugger mix instead of the ground chicken.
Enjoy your sweet chicken buggers!
"""
Evals
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|---------|-------|------|-----:|--------|-----:|---|-----:|
|hellaswag|Yaml |none | 0|acc |0.5283|± |0.0050|
| | |none | 0|acc_norm|0.7068|± |0.0045|
| Groups |Version|Filter|n-shot| Metric | Value | |Stderr|
|----------|-------|------|-----:|-----------|------:|---|-----:|
|truthfulqa|N/A |none | 0|acc | 0.3411|± |0.0016|
| | |none | 0|bleu_max |19.4174|± |0.6888|
| | |none | 0|bleu_acc | 0.3378|± |0.0166|
| | |none | 0|bleu_diff |-4.4165|± |0.6611|
| | |none | 0|rouge1_max |43.6923|± |0.8239|
| | |none | 0|rouge1_acc | 0.3305|± |0.0165|
| | |none | 0|rouge1_diff|-6.4023|± |0.7680|
| | |none | 0|rouge2_max |28.4074|± |0.8883|
| | |none | 0|rouge2_acc | 0.2827|± |0.0158|
| | |none | 0|rouge2_diff|-6.7716|± |0.8844|
| | |none | 0|rougeL_max |40.2657|± |0.8218|
| | |none | 0|rougeL_acc | 0.3023|± |0.0161|
| | |none | 0|rougeL_diff|-6.5447|± |0.7706|
|----------|-------|------|-----:|------|-----:|---|-----:|
|winogrande|Yaml |none | 0|acc |0.6464|± |0.0134|
|-------------|-------|------|-----:|--------|-----:|---|-----:|
|arc_challenge|Yaml |none | 0|acc |0.3652|± |0.0141|
| | |none | 0|acc_norm|0.3908|± |0.0143|
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 41.13 |
AI2 Reasoning Challenge (25-Shot) | 40.19 |
HellaSwag (10-Shot) | 70.08 |
MMLU (5-Shot) | 28.12 |
TruthfulQA (0-shot) | 41.23 |
Winogrande (5-shot) | 65.04 |
GSM8k (5-shot) | 2.12 |
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Model tree for mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1
Base model
vihangd/shearedplats-2.7b-v2Dataset used to train mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1
Evaluation results
- hellaswag(0-Shot) on hellaswagself-reported0.528
- winogrande(0-Shot) on winograndeself-reported0.646
- arc_challenge(0-Shot) on arc_challengeshearedplats-2.7b-v2-instruct-v0.1 model card0.365
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard40.190
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard70.080
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard28.120
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard41.230
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard65.040
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard2.120