mwitiderrick_-_shearedplats-2.7b-v2-instruct-v0.1-gguf

Quantization made by Richard Erkhov.

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shearedplats-2.7b-v2-instruct-v0.1 - GGUF

• Model creator: https://huggingface.co/mwitiderrick/
• Original model: https://huggingface.co/mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1/

Original model description:

language:

• en license: apache-2.0 library_name: transformers tags:

• transformers datasets:

• mwitiderrick/OpenPlatypus base_model: vihangd/shearedplats-2.7b-v2 inference: true model_type: llama prompt_template: '### Instruction:\n

  {prompt}

  Response:

  ' created_by: mwitiderrick pipeline_tag: text-generation model-index:

• name: mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 results:

  • task: type: text-generation dataset: name: hellaswag type: hellaswag metrics:
    • type: hellaswag (0-Shot) value: 0.5283 name: hellaswag(0-Shot)
  • task: type: text-generation dataset: name: winogrande type: winogrande metrics:
    • type: winogrande (0-Shot) value: 0.6464 name: winogrande(0-Shot)
  • task: type: text-generation dataset: name: arc_challenge type: arc_challenge metrics:
    • type: arc_challenge (0-Shot) value: 0.3652 name: arc_challenge(0-Shot) source: url: https://huggingface.co/mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 name: shearedplats-2.7b-v2-instruct-v0.1 model card
  • 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: 40.19 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 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: 70.08 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 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: 28.12 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 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: 41.23 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 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: 65.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 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: 2.12 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1 name: Open LLM Leaderboard

---

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