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AbacusResearch_-_Jallabi-34B-gguf
RichardErkhov/AbacusResearch_-_Jallabi-34B-gguf
AbacusResearch_-_Jallabi-34B-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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At a glance
- Access
- Public
- Created
- Sep 14, 2024
- Updated
- Sep 15, 2024
- SHA
- 368580e0
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Jallabi-34B - GGUF
- Model creator: https://huggingface.co/AbacusResearch/
- Original model: https://huggingface.co/AbacusResearch/Jallabi-34B/
| Name | Quant method | Size |
|---|---|---|
| Jallabi-34B.Q2_K.gguf | Q2_K | 11.94GB |
| Jallabi-34B.IQ3_XS.gguf | IQ3_XS | 13.26GB |
| Jallabi-34B.IQ3_S.gguf | IQ3_S | 13.99GB |
| Jallabi-34B.Q3_K_S.gguf | Q3_K_S | 13.93GB |
| Jallabi-34B.IQ3_M.gguf | IQ3_M | 14.5GB |
| Jallabi-34B.Q3_K.gguf | Q3_K | 15.51GB |
| Jallabi-34B.Q3_K_M.gguf | Q3_K_M | 15.51GB |
| Jallabi-34B.Q3_K_L.gguf | Q3_K_L | 16.89GB |
| Jallabi-34B.IQ4_XS.gguf | IQ4_XS | 17.36GB |
| Jallabi-34B.Q4_0.gguf | Q4_0 | 18.13GB |
| Jallabi-34B.IQ4_NL.gguf | IQ4_NL | 18.3GB |
| Jallabi-34B.Q4_K_S.gguf | Q4_K_S | 18.25GB |
| Jallabi-34B.Q4_K.gguf | Q4_K | 19.24GB |
| Jallabi-34B.Q4_K_M.gguf | Q4_K_M | 19.24GB |
| Jallabi-34B.Q4_1.gguf | Q4_1 | 20.1GB |
| Jallabi-34B.Q5_0.gguf | Q5_0 | 22.08GB |
| Jallabi-34B.Q5_K_S.gguf | Q5_K_S | 22.08GB |
| Jallabi-34B.Q5_K.gguf | Q5_K | 22.65GB |
| Jallabi-34B.Q5_K_M.gguf | Q5_K_M | 22.65GB |
| Jallabi-34B.Q5_1.gguf | Q5_1 | 24.05GB |
| Jallabi-34B.Q6_K.gguf | Q6_K | 26.28GB |
| Jallabi-34B.Q8_0.gguf | Q8_0 | 34.03GB |
Original model description:
license: apache-2.0 tags:
- llama
- LlamaForCausalLM
- LLM model-index:
- name: Jallabi-34B
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: 66.04 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B 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: 83.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B 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: 76.4 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B 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: 51.46 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B 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: 81.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B 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: 65.2 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc value: 35.29 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm value: 43.62 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match value: 3.93 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm value: 11.86 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm value: 20.24 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc value: 40.91 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B 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:
These are llama only weights of https://huggingface.co/liuhaotian/llava-v1.6-34b . The Clip encoder part is removed and and this model is llama weights only that can be loaded using LlamaForCausalLM. Which indirectly is a https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B licence.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 70.73 |
| AI2 Reasoning Challenge (25-Shot) | 66.04 |
| HellaSwag (10-Shot) | 83.81 |
| MMLU (5-Shot) | 76.40 |
| TruthfulQA (0-shot) | 51.46 |
| Winogrande (5-shot) | 81.45 |
| GSM8k (5-shot) | 65.20 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.97 |
| IFEval (0-Shot) | 35.29 |
| BBH (3-Shot) | 43.62 |
| MATH Lvl 5 (4-Shot) | 3.93 |
| GPQA (0-shot) | 11.86 |
| MuSR (0-shot) | 20.24 |
| MMLU-PRO (5-shot) | 40.91 |
- Created
- Sep 14, 2024
- Updated
- Sep 15, 2024