Instructions to use brucethemoose/Capybara-Tess-Yi-34B-200K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brucethemoose/Capybara-Tess-Yi-34B-200K with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="brucethemoose/Capybara-Tess-Yi-34B-200K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brucethemoose/Capybara-Tess-Yi-34B-200K")
model = AutoModelForCausalLM.from_pretrained("brucethemoose/Capybara-Tess-Yi-34B-200K")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use brucethemoose/Capybara-Tess-Yi-34B-200K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brucethemoose/Capybara-Tess-Yi-34B-200K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brucethemoose/Capybara-Tess-Yi-34B-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/brucethemoose/Capybara-Tess-Yi-34B-200K

SGLang

How to use brucethemoose/Capybara-Tess-Yi-34B-200K 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/Capybara-Tess-Yi-34B-200K" \
    --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/Capybara-Tess-Yi-34B-200K",
		"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/Capybara-Tess-Yi-34B-200K" \
        --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/Capybara-Tess-Yi-34B-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use brucethemoose/Capybara-Tess-Yi-34B-200K with Docker Model Runner:
```
docker model run hf.co/brucethemoose/Capybara-Tess-Yi-34B-200K
```

Capybara-Tess-Yi-34B-200K / README.md

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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
	pipeline_tag: text-generation
	tags:
	- merge
	---

	# Obsolete, succeeded by a new merge: https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity

	***

	NousResearch/Nous-Capybara-34B and migtissera/Tess-M-Creative-v1.0 ties merged with mergekit.

	I would suggest an exllama version for local inference with 40K+ context in 24GB:
	https://huggingface.co/brucethemoose/Capybara-Tess-Yi-34B-200K-exl2-4bpw-fiction
	https://huggingface.co/brucethemoose/Capybara-Tess-Yi-34B-200K-exl2-31bpw-fiction

	Merged with the following config:

	```
	models:
	- model: /home/alpha/Storage/Models/Raw/larryvrh_Yi-34B-200K-Llamafied
	# no parameters necessary for base model
	- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-M-v1.0
	parameters:
	density: 0.6
	weight: 1.0
	- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
	parameters:
	density: 0.6
	weight: 1.0
	merge_method: ties
	base_model: //home/alpha/Storage/Models/Raw/larryvrh_Yi-34B-200K-Llamafied
	parameters:
	normalize: true
	int8_mask: true
	dtype: float16
	```

	Both are 200K context models with Vicuna syntax, so:

	# Prompt Format:

	```
	SYSTEM: ...
	USER: ...
	ASSISTANT: ...
	```
	Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` this as an additional stopping condition.

	***

	Credits:

	https://github.com/cg123/mergekit

	https://huggingface.co/NousResearch/Nous-Capybara-34B/discussions

	https://huggingface.co/migtissera/Tess-M-Creative-v1.0

	https://huggingface.co/larryvrh/Yi-34B-200K-Llamafied

	https://huggingface.co/01-ai/Yi-34B-200K