Instructions to use bartowski/code-millenials-34b-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartowski/code-millenials-34b-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bartowski/code-millenials-34b-exl2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bartowski/code-millenials-34b-exl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bartowski/code-millenials-34b-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/code-millenials-34b-exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/code-millenials-34b-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/code-millenials-34b-exl2
- SGLang
How to use bartowski/code-millenials-34b-exl2 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "bartowski/code-millenials-34b-exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/code-millenials-34b-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "bartowski/code-millenials-34b-exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/code-millenials-34b-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bartowski/code-millenials-34b-exl2 with Docker Model Runner:
docker model run hf.co/bartowski/code-millenials-34b-exl2
File size: 2,695 Bytes
c4abd66 6ed57ff c4abd66 6ed57ff c4abd66 6ed57ff c4abd66 6ed57ff c4abd66 | 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 78 79 80 81 82 83 | ---
license: llama2
library_name: transformers
tags:
- code
model-index:
- name: Code Millenials
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.8048
verified: false
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of code-millenials-34b
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization.
## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/budecosystem/code-millenials-34b
<a href="https://huggingface.co/bartowski/code-millenials-34b-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/code-millenials-34b-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/code-millenials-34b-exl2/tree/4_25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/code-millenials-34b-exl2/tree/3_75">3.75 bits per weight</a>
<a href="https://huggingface.co/bartowski/code-millenials-34b-exl2/tree/3_0">3.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/code-millenials-34b-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `code-millenials-34b-exl2`:
```shell
mkdir code-millenials-34b-exl2
huggingface-cli download bartowski/code-millenials-34b-exl2 --local-dir code-millenials-34b-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir code-millenials-34b-exl2-6_5
huggingface-cli download bartowski/code-millenials-34b-exl2 --revision 6_5 --local-dir code-millenials-34b-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir code-millenials-34b-exl2-6.5
huggingface-cli download bartowski/code-millenials-34b-exl2 --revision 6_5 --local-dir code-millenials-34b-exl2-6.5 --local-dir-use-symlinks False
``` |