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language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: other
license_name: fair
license_link: LICENSE
base_model: facebook/layerskip-llama3.2-1B
model-index:
- name: LayerSkip Llama3.2 1B
results:
- task:
type: question-answering
dataset:
name: BoolQ
type: google/boolq
metrics:
- type: acc
value: 0.646
name: acc
verified: false
- task:
type: question-answering
dataset:
name: PIQA
type: ybisk/piqa
metrics:
- type: acc
value: 0.724
name: acc
verified: false
- task:
type: question-answering
dataset:
name: SIQA
type: allenai/social_i_qa
metrics:
- type: acc
value: 0.419
name: acc
verified: false
- task:
type: text-generation
dataset:
name: HellaSwag
type: Rowan/hellaswag
metrics:
- type: acc
value: 0.455
name: acc
verified: false
- task:
type: question-answering
dataset:
name: WinoGrande
type: allenai/winogrande
metrics:
- type: acc
value: 0.6
name: acc
verified: false
- task:
type: question-answering
dataset:
name: ARC (Easy)
type: allenai/ai2_arc
metrics:
- type: acc
value: 0.651
name: acc
verified: false
- type: acc
value: 0.295
name: acc
verified: false
- task:
type: question-answering
dataset:
name: OpenBookQA
type: allenai/openbookqa
metrics:
- type: acc
value: 0.258
name: acc
verified: false
- task:
type: question-answering
dataset:
name: RACE
type: ehovy/race
metrics:
- type: acc
value: 0.354
name: acc
verified: false
- task:
type: question-answering
dataset:
name: MMLU
type: cais/mmlu
metrics:
- type: acc
value: 0.247
name: acc
verified: false
- task:
type: text-generation
dataset:
name: Natural Questions
type: google-research-datasets/nq_open
metrics:
- type: exact_match
value: 0.038
name: exact_match
verified: false
- task:
type: question-answering
dataset:
name: TriviaQA
type: sentence-transformers/trivia-qa
metrics:
- type: acc
value: 0.121
name: acc
verified: false
- task:
type: text-generation
dataset:
name: GSM8K
type: openai/gsm8k
metrics:
- type: exact_match
value: 0.024
name: exact_match
verified: false
- task:
type: question-answering
dataset:
name: MathQA
type: allenai/math_qa
metrics:
- type: acc
value: 0.259
name: acc
verified: false
- task:
type: question-answering
dataset:
name: SQuAD2.0
type: rajpurkar/squad_v2
metrics:
- type: exact
value: 0.355
name: exact
verified: false
- task:
type: text-classification
dataset:
name: ToxiGen
type: toxigen/toxigen-data
metrics:
- type: acc
value: 0.431
name: acc
verified: false
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 0.024
name: pass@1
verified: false
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 0.062
name: pass@1
verified: false
---
# impactframes/layerskip-llama3.2-1B-Q4_K_M-GGUF
![image/png](https://cdn-uploads.huggingface.co/production/uploads/63c2c05b8cc87cf0c05b89ba/PWrUdqxKbBBM18x8uZt40.png)
This model was converted to GGUF format from [`facebook/layerskip-llama3.2-1B`](https://huggingface.co/facebook/layerskip-llama3.2-1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/facebook/layerskip-llama3.2-1B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo impactframes/layerskip-llama3.2-1B-Q4_K_M-GGUF --hf-file layerskip-llama3.2-1b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo impactframes/layerskip-llama3.2-1B-Q4_K_M-GGUF --hf-file layerskip-llama3.2-1b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo impactframes/layerskip-llama3.2-1B-Q4_K_M-GGUF --hf-file layerskip-llama3.2-1b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo impactframes/layerskip-llama3.2-1B-Q4_K_M-GGUF --hf-file layerskip-llama3.2-1b-q4_k_m.gguf -c 2048
```
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