Instructions to use LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project

SGLang

How to use LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project 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 "LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project" \
    --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": "LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project",
		"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 "LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project" \
        --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": "LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/SpydazWeb_AI_Extended_Context_128k_Yarn_Project
```

LeroyDyer commited on Aug 26, 2024

Commit

2da9e62

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1 Parent(s): dc79757

Update README.md

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@@ -10,7 +10,6 @@ tags:
 - code
 - medical
 - text-generation-inference
-- not-for-all-audiences
 license: mit
 language:
 - en
@@ -21,12 +20,18 @@ metrics:
 - code_eval
 - cer
 ---
-### Models Merged
-The following models were included in the merge:
-* [LeroyDyer/Mixtral_AI_128k_bioMedical](https://huggingface.co/LeroyDyer/Mixtral_AI_128k_bioMedical)
-* [filipealmeida/Mistral-7B-Instruct-v0.1-sharded](https://huggingface.co/filipealmeida/Mistral-7B-Instruct-v0.1-sharded)
 GREAT MODEL !
 ```

 - code
 - medical
 - text-generation-inference
 license: mit
 language:
 - en
 - code_eval
 - cer
 ---
+<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
+https://github.com/spydaz
+Enhanced with an expanded context window and advanced routing mechanisms, the Mistral-7B-Instruct-v0.2 exemplifies the power of Mixture of Experts, allowing seamless integration of specialized sub-models. This architecture facilitates unparalleled performance and scalability, enabling the CyberSeries to tackle a myriad of tasks with unparalleled speed and accuracy.
+Among its illustrious sub-models, the OpenOrca - Mistral-7B-8k shines as a testament to fine-tuning excellence, boasting top-ranking performance in its class. Meanwhile, the Hermes 2 Pro introduces cutting-edge capabilities such as Function Calling and JSON Mode, catering to diverse application needs.
+Driven by Reinforcement Learning from AI Feedback, the Starling-LM-7B-beta demonstrates remarkable adaptability and optimization, while the Phi-1.5 Transformer model stands as a beacon of excellence across various domains, from common sense reasoning to medical inference.
+With models like BioMistral tailored specifically for medical applications and Nous-Yarn-Mistral-7b-128k excelling in handling long-context data, the MEGA_MIND 24b CyberSeries emerges as a transformative force in the landscape of language understanding and artificial intelligence.
+Experience the future of language models with the MEGA_MIND 24b CyberSeries, where innovation meets performance, and possibilities are limitless.
 GREAT MODEL !
 ```