---
license: apache-2.0
base_model: Qwen/Qwen2-1.5B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
datasets:
- Replete-AI/code_bagel_hermes-2.5
- Replete-AI/code_bagel
- Replete-AI/OpenHermes-2.5-Uncensored
- teknium/OpenHermes-2.5
- layoric/tiny-codes-alpaca
- glaiveai/glaive-code-assistant-v3
- ajibawa-2023/Code-290k-ShareGPT
- TIGER-Lab/MathInstruct
- chargoddard/commitpack-ft-instruct-rated
- iamturun/code_instructions_120k_alpaca
- ise-uiuc/Magicoder-Evol-Instruct-110K
- cognitivecomputations/dolphin-coder
- nickrosh/Evol-Instruct-Code-80k-v1
- coseal/CodeUltraFeedback_binarized
- glaiveai/glaive-function-calling-v2
- CyberNative/Code_Vulnerability_Security_DPO
- jondurbin/airoboros-2.2
- camel-ai
- lmsys/lmsys-chat-1m
- CollectiveCognition/chats-data-2023-09-22
- CoT-Alpaca-GPT4
- WizardLM/WizardLM_evol_instruct_70k
- WizardLM/WizardLM_evol_instruct_V2_196k
- teknium/GPT4-LLM-Cleaned
- GPTeacher
- OpenGPT
- meta-math/MetaMathQA
- Open-Orca/SlimOrca
- garage-bAInd/Open-Platypus
- anon8231489123/ShareGPT_Vicuna_unfiltered
- Unnatural-Instructions-GPT4
model-index:
- name: Replete-Coder-llama3-8b
results:
- task:
name: HumanEval
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value:
verified: false
- task:
name: AI2 Reasoning Challenge
type: text-generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: accuracy
value:
name: normalized accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: accuracy
value:
name: normalized accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: accuracy
value:
name: accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: multiple_choice_accuracy
value:
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: accuracy
value:
name: accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
- task:
name: Text Generation
type: text-generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: accuracy
value:
name: accuracy
source:
url: https://www.placeholderurl.com
name: Open LLM Leaderboard
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Replete-Coder-Qwen-1.5b
Using llama.cpp release b3197 for quantization.
Original model: https://huggingface.co/Replete-AI/Replete-Coder-Qwen-1.5b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Replete-Coder-Qwen-1.5b-Q8_0_L.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q8_0_L.gguf) | Q8_0_L | 1870.00MB | Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. |
| [Replete-Coder-Qwen-1.5b-Q8_0.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q8_0.gguf) | Q8_0 | 1646.57MB | Extremely high quality, generally unneeded but max available quant. |
| [Replete-Coder-Qwen-1.5b-Q6_K_L.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q6_K_L.gguf) | Q6_K_L | 1550MB | Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. |
| [Replete-Coder-Qwen-1.5b-Q6_K.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q6_K.gguf) | Q6_K | 1272.73MB | Very high quality, near perfect, *recommended*. |
| [Replete-Coder-Qwen-1.5b-Q5_K_L.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q5_K_L.gguf) | Q5_K_L | 1400MB | Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. |
| [Replete-Coder-Qwen-1.5b-Q5_K_M.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q5_K_M.gguf) | Q5_K_M | 1125.04MB | High quality, *recommended*. |
| [Replete-Coder-Qwen-1.5b-Q4_K_L.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q4_K_L.gguf) | Q4_K_L | 1260MB | Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Replete-Coder-Qwen-1.5b-Q4_K_M.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q4_K_M.gguf) | Q4_K_M | 986.04MB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Replete-Coder-Qwen-1.5b-IQ4_XS.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-IQ4_XS.gguf) | IQ4_XS | 895.72MB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Replete-Coder-Qwen-1.5b-Q3_K_XL.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q3_K_XL.gguf) | Q3_K_XL | 1160MB | Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. |
| [Replete-Coder-Qwen-1.5b-Q3_K_L.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q3_K_L.gguf) | Q3_K_L | 880.16MB | Lower quality but usable, good for low RAM availability. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Replete-Coder-Qwen-1.5b-GGUF --include "Replete-Coder-Qwen-1.5b-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Replete-Coder-Qwen-1.5b-GGUF --include "Replete-Coder-Qwen-1.5b-Q8_0.gguf/*" --local-dir Replete-Coder-Qwen-1.5b-Q8_0
```
You can either specify a new local-dir (Replete-Coder-Qwen-1.5b-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski