Instructions to use Elfrino/BinaryBlush-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elfrino/BinaryBlush-33B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Elfrino/BinaryBlush-33B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Elfrino/BinaryBlush-33B") model = AutoModelForCausalLM.from_pretrained("Elfrino/BinaryBlush-33B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Elfrino/BinaryBlush-33B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Elfrino/BinaryBlush-33B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Elfrino/BinaryBlush-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Elfrino/BinaryBlush-33B
- SGLang
How to use Elfrino/BinaryBlush-33B 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 "Elfrino/BinaryBlush-33B" \ --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": "Elfrino/BinaryBlush-33B", "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 "Elfrino/BinaryBlush-33B" \ --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": "Elfrino/BinaryBlush-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Elfrino/BinaryBlush-33B with Docker Model Runner:
docker model run hf.co/Elfrino/BinaryBlush-33B
NOTES: Looks promising...In testing...*
SETTINGS TO USE (BASED ON KOBOLDCPP)
Chat Template: Alpaca
Quick Preset: Mayday
Max Ctx Tokens: 4096
Temp: 1.3
Top P Sampling: 0.99
Repetition penality: 1
###################################################################################
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
#--DUAL MODEL MERGE SETUP---
# TRY AGAIN, BUT COPY OVER THE CORRECT .JSON FILES FROM
# HARMONIC HARLEQUIN SO IT CAN GGUF.. maybe..
# The models we are going to use.
const_tag: &BASE_MODEL Undi95/PsyMedRP-v1-20B
const_tag: &MODEL1 Elfrino/XwinXtended-20B # Will this guy cause me headaches?
const_tag: &MODEL2 Undi95/PsyMedRP-v1-20B
# The amount to scale the contribution to the residual stream (to hopefully reduce overshoot).
const_tag: &RESIDUAL_SCALE_FACTOR 0.71 # back to 0.7
model1-filter-env: &MODEL1_FILTER_ENV
parameters:
scale:
- filter: down_proj
value: *RESIDUAL_SCALE_FACTOR
- value: 1.0
model2-filter-env: &MODEL2_FILTER_ENV
parameters:
scale:
- filter: down_proj
value: *RESIDUAL_SCALE_FACTOR
- value: 1.0
slices:
# The first 10 layers are not duplicated.
- sources:
- model: *BASE_MODEL
layer_range: [0, 10]
- sources:
- model: *MODEL1
layer_range: [10, 11]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [10, 11]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [11, 12]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [11, 12]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [12, 13]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [12, 13]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [13, 14]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [13, 14]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [14, 15]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [14, 15]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [15, 16]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [15, 16]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [16, 17]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [16, 17]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [17, 18]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [17, 18]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [18, 19]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [18, 19]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [19, 20]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [19, 20]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [20, 21]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [20, 21]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [21, 22]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [21, 22]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [22, 23]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [22, 23]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [23, 24]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [23, 24]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [24, 25]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [24, 25]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [25, 26]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [25, 26]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [26, 27]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [26, 27]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [27, 28]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [27, 28]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [28, 29]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [28, 29]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [29, 30]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [29, 30]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [30, 31]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [30, 31]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [31, 32]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [31, 32]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [32, 33]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [32, 33]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [33, 34]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [33, 34]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [34, 35]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [34, 35]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [35, 36]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [35, 36]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [36, 37]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [36, 37]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [37, 38]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [37, 38]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [38, 39]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [38, 39]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [39, 40]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [39, 40]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [40, 41]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [40, 41]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [41, 42]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [41, 42]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [42, 43]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [42, 43]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [43, 44]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [43, 44]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [44, 45]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [44, 45]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [45, 46]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [45, 46]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [46, 47]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [46, 47]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [47, 48]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [47, 48]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [48, 49]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [48, 49]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [49, 50]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [49, 50]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [50, 51]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [50, 51]
<<: *MODEL2_FILTER_ENV
- sources:
- model: *MODEL1
layer_range: [51, 52]
<<: *MODEL1_FILTER_ENV
- sources:
- model: *MODEL2
layer_range: [51, 52]
<<: *MODEL2_FILTER_ENV
# The last 10 layers are not duplicated.
- sources:
- model: *BASE_MODEL
layer_range: [52, 62]
merge_method: passthrough
dtype: bfloat16
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