README.md

---
base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
datasets:
- mwitiderrick/AlpacaCode
inference: true
model_type: llama
prompt_template: |
  <s>[INST] 
  {prompt}
  [/INST]
created_by: mwitiderrick
tags:
- transformers
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation

model-index:
  - name: mwitiderrick/open_llama_3b_instruct_v_0.2
    results:
      - task:
          type: text-generation
        dataset:
          name: hellaswag
          type: hellaswag
        metrics:
          - name: hellaswag(0-Shot)
            type: hellaswag (0-Shot)
            value: 0.
      - task:
          type: text-generation
        dataset:
          name: winogrande
          type: winogrande
        metrics:
          - name: winogrande(0-Shot)
            type: winogrande (0-Shot)
            value: 0.

      - task:
          type: text-generation
        dataset:
          name: arc_challenge
          type: arc_challenge
        metrics:
          - name: arc_challenge(0-Shot)
            type: arc_challenge (0-Shot)
            value: 0.
        source:
          name: open_llama_3b_instruct_v_0.2 model card
          url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2

          
---
# OpenLLaMA Code Instruct: An Open Reproduction of LLaMA

This is an [OpenLlama model Code Instruct](https://huggingface.co/mwitiderrick/open_llama_3b_code_instruct_0.1) that has been fine-tuned on 1 epoch of the
[Glaive Assistsnt](https://huggingface.co/datasets/mwitiderrick/glaive-code-assistant) dataset.

## Prompt Template
```
<s>[INST] {{ user_msg }} [/INST]

```
## Usage 
```python
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline

tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
query = "Write a quick sort algorithm in Python"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
output = text_gen(f"<s>[INST]{query}[/INST]")
print(output[0]['generated_text'])
"""

"""
```
## Metrics

```

```