File size: 2,311 Bytes
a19e303 ef741c2 a19e303 ba60a5f a19e303 ba60a5f a19e303 ba60a5f a19e303 ba60a5f a19e303 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: XeAI/LLaMa_3.2_3B_Instruct_Text2SQL
---
# ZhafranR/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF
This model was converted to GGUF format from [`XeAI/LLaMa_3.2_3B_Instruct_Text2SQL`](https://huggingface.co/XeAI/LLaMa_3.2_3B_Instruct_Text2SQL) 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/XeAI/LLaMa_3.2_3B_Instruct_Text2SQL) for more details on the model.
## Use with llama-cpp-python
```python
from llama_cpp import Llama
# Load the model
model = Llama(
model_path="path_to_your_model.gguf",
n_ctx=2048,
n_batch=512,
n_threads=6
)
# Generate text
output = model.create_completion(
"Your prompt here",
max_tokens=512,
temperature=0.7,
top_p=0.95,
top_k=40,
repeat_penalty=1.1
)
print(output['choices'][0]['text'])
```
## 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 XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-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 XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo XeAI/LLaMa_3.2_3B_Instruct_Text2SQL-Q4_K_M-GGUF --hf-file llama_3.2_3b_instruct_text2sql-q4_k_m.gguf -c 2048
```
|