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

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")

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
```

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 TIES 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:

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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Collection including brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw

Most Recent Merge

Collection

5 items • Updated Mar 2 • 1

Papers for brucethemoose/Yi-34B-200K-RPMerge-exl2-31bpw

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 33

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19