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---
language:
- en
license: apache-2.0
tags:
- LLMs
- mistral
- math
- Intel
- llama-cpp
- gguf-my-repo
base_model: Intel/neural-chat-7b-v3-2
datasets:
- meta-math/MetaMathQA
model-index:
- name: neural-chat-7b-v3-2
results:
- task:
type: Large Language Model
name: Large Language Model
dataset:
name: meta-math/MetaMathQA
type: meta-math/MetaMathQA
metrics:
- type: ARC (25-shot)
value: 67.49
name: ARC (25-shot)
verified: true
- type: HellaSwag (10-shot)
value: 83.92
name: HellaSwag (10-shot)
verified: true
- type: MMLU (5-shot)
value: 63.55
name: MMLU (5-shot)
verified: true
- type: TruthfulQA (0-shot)
value: 59.68
name: TruthfulQA (0-shot)
verified: true
- type: Winogrande (5-shot)
value: 79.95
name: Winogrande (5-shot)
verified: true
- type: GSM8K (5-shot)
value: 55.12
name: GSM8K (5-shot)
verified: true
---
# DarkJanissary/neural-chat-7b-v3-2-Q6_K-GGUF
This model was converted to GGUF format from [`Intel/neural-chat-7b-v3-2`](https://huggingface.co/Intel/neural-chat-7b-v3-2) 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/Intel/neural-chat-7b-v3-2) 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 --hf-repo DarkJanissary/neural-chat-7b-v3-2-Q6_K-GGUF --hf-file neural-chat-7b-v3-2-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo DarkJanissary/neural-chat-7b-v3-2-Q6_K-GGUF --hf-file neural-chat-7b-v3-2-q6_k.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.
```
./main --hf-repo DarkJanissary/neural-chat-7b-v3-2-Q6_K-GGUF --hf-file neural-chat-7b-v3-2-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./server --hf-repo DarkJanissary/neural-chat-7b-v3-2-Q6_K-GGUF --hf-file neural-chat-7b-v3-2-q6_k.gguf -c 2048
```
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