README.md

---
license: bsd
datasets:
- ManthanKulakarni/Text2JQL_v2
language:
- en
pipeline_tag: text-generation
tags:
- LLaMa
- JQL
- Jira
- GGML
- GGML-q8_0
- GPU
- CPU
- 7B
- llama.cpp
- text-generation-webui
---

GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp)

## How to run in `llama.cpp`


```
./main -t 10 -ngl 32 -m ggml-model-q8_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write JQL(Jira query Language) for give input ### Input: stories assigned to manthan which are created in last 10 days with highest priority and label is set to release ### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.

Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Tto have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

## How to run in `text-generation-webui`

Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).

## How to run using `LangChain`

##### Instalation on CPU
```
pip install llama-cpp-python
```
##### Instalation on GPU
```
CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python
```

```python
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
n_ctx=2048

callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

# Make sure the model path is correct for your system!
llm = LlamaCpp(
    model_path="./ggml-model-q8_0.bin",
    n_gpu_layers=n_gpu_layers, n_batch=n_batch,
    callback_manager=callback_manager,
    verbose=True,
    n_ctx=n_ctx
)

llm("""### Instruction:
Write JQL(Jira query Language) for give input

### Input:
stories assigned to manthan which are created in last 10 days with highest priority and label is set to release

### Response:""")
```
For more information refer [LangChain](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp)