---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- transformers
- TensorBlock
- GGUF
datasets:
- mwitiderrick/AlpacaCode
base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
inference: true
model_type: llama
prompt_template: '### Instruction:\n
{prompt}
### Response:
'
created_by: mwitiderrick
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:
- type: hellaswag (0-Shot)
value: 0.6581
name: hellaswag(0-Shot)
- task:
type: text-generation
dataset:
name: winogrande
type: winogrande
metrics:
- type: winogrande (0-Shot)
value: 0.6267
name: winogrande(0-Shot)
- task:
type: text-generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- type: arc_challenge (0-Shot)
value: 0.3712
name: arc_challenge(0-Shot)
source:
url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
name: open_llama_3b_instruct_v_0.2 model card
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 41.21
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 66.96
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 27.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 35.01
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.43
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 1.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
name: Open LLM Leaderboard
---
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## mwitiderrick/open_llama_3b_code_instruct_0.1 - GGUF
This repo contains GGUF format model files for [mwitiderrick/open_llama_3b_code_instruct_0.1](https://huggingface.co/mwitiderrick/open_llama_3b_code_instruct_0.1).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).
## Our projects
## Prompt template
```
Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [open_llama_3b_code_instruct_0.1-Q2_K.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q2_K.gguf) | Q2_K | 1.980 GB | smallest, significant quality loss - not recommended for most purposes |
| [open_llama_3b_code_instruct_0.1-Q3_K_S.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q3_K_S.gguf) | Q3_K_S | 1.980 GB | very small, high quality loss |
| [open_llama_3b_code_instruct_0.1-Q3_K_M.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q3_K_M.gguf) | Q3_K_M | 2.139 GB | very small, high quality loss |
| [open_llama_3b_code_instruct_0.1-Q3_K_L.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q3_K_L.gguf) | Q3_K_L | 2.215 GB | small, substantial quality loss |
| [open_llama_3b_code_instruct_0.1-Q4_0.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q4_0.gguf) | Q4_0 | 1.980 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [open_llama_3b_code_instruct_0.1-Q4_K_S.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q4_K_S.gguf) | Q4_K_S | 2.403 GB | small, greater quality loss |
| [open_llama_3b_code_instruct_0.1-Q4_K_M.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q4_K_M.gguf) | Q4_K_M | 2.580 GB | medium, balanced quality - recommended |
| [open_llama_3b_code_instruct_0.1-Q5_0.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q5_0.gguf) | Q5_0 | 2.395 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [open_llama_3b_code_instruct_0.1-Q5_K_S.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q5_K_S.gguf) | Q5_K_S | 2.603 GB | large, low quality loss - recommended |
| [open_llama_3b_code_instruct_0.1-Q5_K_M.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q5_K_M.gguf) | Q5_K_M | 2.757 GB | large, very low quality loss - recommended |
| [open_llama_3b_code_instruct_0.1-Q6_K.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q6_K.gguf) | Q6_K | 3.642 GB | very large, extremely low quality loss |
| [open_llama_3b_code_instruct_0.1-Q8_0.gguf](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/open_llama_3b_code_instruct_0.1-Q8_0.gguf) | Q8_0 | 3.642 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF --include "open_llama_3b_code_instruct_0.1-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```