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Deacon-1b - GGUF
- Model creator: https://huggingface.co/KnutJaegersberg/
- Original model: https://huggingface.co/KnutJaegersberg/Deacon-1b/
Name | Quant method | Size |
---|---|---|
Deacon-1b.Q2_K.gguf | Q2_K | 0.4GB |
Deacon-1b.IQ3_XS.gguf | IQ3_XS | 0.44GB |
Deacon-1b.IQ3_S.gguf | IQ3_S | 0.47GB |
Deacon-1b.Q3_K_S.gguf | Q3_K_S | 0.47GB |
Deacon-1b.IQ3_M.gguf | IQ3_M | 0.48GB |
Deacon-1b.Q3_K.gguf | Q3_K | 0.51GB |
Deacon-1b.Q3_K_M.gguf | Q3_K_M | 0.51GB |
Deacon-1b.Q3_K_L.gguf | Q3_K_L | 0.55GB |
Deacon-1b.IQ4_XS.gguf | IQ4_XS | 0.57GB |
Deacon-1b.Q4_0.gguf | Q4_0 | 0.59GB |
Deacon-1b.IQ4_NL.gguf | IQ4_NL | 0.6GB |
Deacon-1b.Q4_K_S.gguf | Q4_K_S | 0.6GB |
Deacon-1b.Q4_K.gguf | Q4_K | 0.62GB |
Deacon-1b.Q4_K_M.gguf | Q4_K_M | 0.62GB |
Deacon-1b.Q4_1.gguf | Q4_1 | 0.65GB |
Deacon-1b.Q5_0.gguf | Q5_0 | 0.71GB |
Deacon-1b.Q5_K_S.gguf | Q5_K_S | 0.71GB |
Deacon-1b.Q5_K.gguf | Q5_K | 0.73GB |
Deacon-1b.Q5_K_M.gguf | Q5_K_M | 0.73GB |
Deacon-1b.Q5_1.gguf | Q5_1 | 0.77GB |
Deacon-1b.Q6_K.gguf | Q6_K | 0.84GB |
Deacon-1b.Q8_0.gguf | Q8_0 | 1.09GB |
Original model description:
license: cc-by-nc-4.0 model-index: - name: Deacon-1b 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: 32.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deacon-1b 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: 58.62 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deacon-1b 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: 24.89 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deacon-1b 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: 35.05 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deacon-1b 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: 59.59 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deacon-1b 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: 0.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deacon-1b name: Open LLM Leaderboard
Base model is appvoid/palmer-001, fine tuned for 3 epochs with Neftune.
Prompt Example:
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### Instruction:
How do you fine tune a large language model?
### Response:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 35.21 |
AI2 Reasoning Challenge (25-Shot) | 32.42 |
HellaSwag (10-Shot) | 58.62 |
MMLU (5-Shot) | 24.89 |
TruthfulQA (0-shot) | 35.05 |
Winogrande (5-shot) | 59.59 |
GSM8k (5-shot) | 0.68 |
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