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dhanushreddy29_-_BrokenKeyboard-gguf
RichardErkhov/dhanushreddy29_-_BrokenKeyboard-gguf
dhanushreddy29_-_BrokenKeyboard-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf141 GB · 100%
At a glance
- Access
- Public
- Created
- Aug 31, 2024
- Updated
- Aug 31, 2024
- SHA
- 22f3784d
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
BrokenKeyboard - GGUF
- Model creator: https://huggingface.co/dhanushreddy29/
- Original model: https://huggingface.co/dhanushreddy29/BrokenKeyboard/
| Name | Quant method | Size |
|---|---|---|
| BrokenKeyboard.Q2_K.gguf | Q2_K | 3.73GB |
| BrokenKeyboard.IQ3_XS.gguf | IQ3_XS | 4.14GB |
| BrokenKeyboard.IQ3_S.gguf | IQ3_S | 4.37GB |
| BrokenKeyboard.Q3_K_S.gguf | Q3_K_S | 4.34GB |
| BrokenKeyboard.IQ3_M.gguf | IQ3_M | 4.51GB |
| BrokenKeyboard.Q3_K.gguf | Q3_K | 4.84GB |
| BrokenKeyboard.Q3_K_M.gguf | Q3_K_M | 4.84GB |
| BrokenKeyboard.Q3_K_L.gguf | Q3_K_L | 5.26GB |
| BrokenKeyboard.IQ4_XS.gguf | IQ4_XS | 5.43GB |
| BrokenKeyboard.Q4_0.gguf | Q4_0 | 5.66GB |
| BrokenKeyboard.IQ4_NL.gguf | IQ4_NL | 5.72GB |
| BrokenKeyboard.Q4_K_S.gguf | Q4_K_S | 5.7GB |
| BrokenKeyboard.Q4_K.gguf | Q4_K | 6.02GB |
| BrokenKeyboard.Q4_K_M.gguf | Q4_K_M | 6.02GB |
| BrokenKeyboard.Q4_1.gguf | Q4_1 | 6.27GB |
| BrokenKeyboard.Q5_0.gguf | Q5_0 | 6.89GB |
| BrokenKeyboard.Q5_K_S.gguf | Q5_K_S | 6.89GB |
| BrokenKeyboard.Q5_K.gguf | Q5_K | 7.08GB |
| BrokenKeyboard.Q5_K_M.gguf | Q5_K_M | 7.08GB |
| BrokenKeyboard.Q5_1.gguf | Q5_1 | 7.51GB |
| BrokenKeyboard.Q6_K.gguf | Q6_K | 8.2GB |
| BrokenKeyboard.Q8_0.gguf | Q8_0 | 10.62GB |
Original model description:
language:
- en license: cc-by-nc-4.0 datasets:
- argilla/distilabel-intel-orca-dpo-pairs base_model:
- upstage/SOLAR-10.7B-Instruct-v1.0 model-index:
- name: BrokenKeyboard
results:
- 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: 71.25 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboard 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: 88.34 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboard 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: 66.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboard 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: 71.36 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboard 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: 83.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboard 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: 64.29 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=dhanushreddy29/BrokenKeyboard name: Open LLM Leaderboard
- 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:
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->Just testing out LLM Finetuning. Finetuned on upstage/SOLAR-10.7B-Instruct-v1.0 using argilla/distilabel-intel-orca-dpo-pairs. Followed the Google Colab mentioned in this article: https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 74.08 |
| AI2 Reasoning Challenge (25-Shot) | 71.25 |
| HellaSwag (10-Shot) | 88.34 |
| MMLU (5-Shot) | 66.04 |
| TruthfulQA (0-shot) | 71.36 |
| Winogrande (5-shot) | 83.19 |
| GSM8k (5-shot) | 64.29 |
- Created
- Aug 31, 2024
- Updated
- Aug 31, 2024