Text Generation
Transformers
Safetensors
English
llama
mergekit
Merge
Yi
exllama
exllamav2
exl2
text-generation-inference
Instructions to use brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw") model = AutoModelForCausalLM.from_pretrained("brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw
- SGLang
How to use brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw 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 "brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw" \ --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": "brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw", "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 "brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw" \ --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": "brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw with Docker Model Runner:
docker model run hf.co/brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw
File size: 2,192 Bytes
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license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
language:
- en
library_name: transformers
base_model: []
tags:
- mergekit
- merge
- Yi
- exllama
- exllamav2
- exl2
---
# RPmerge
See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-RPMerge
Quantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
### Models Merged
The following models were included in the merge:
* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
* /home/alpha/Models/Raw/Nous-Capybara-34B
* /home/alpha/Models/Raw/admo_limarp
* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
# No parameters necessary for base model
- model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
#Emphasize the beginning of Vicuna format models
parameters:
weight: 0.19
density: 0.59
- model: /home/alpha/Models/Raw/Nous-Capybara-34B
parameters:
weight: 0.19
density: 0.55
# Vicuna format
- model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
parameters:
weight: 0.05
density: 0.55
- model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
parameters:
weight: 0.19
density: 0.55
- model: /home/alpha/Models/Raw/admo_limarp
parameters:
weight: 0.19
density: 0.48
- model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
parameters:
weight: 0.19
density: 0.59
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```
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