Instructions to use TroyDoesAI/Codestral-RAG-19B-Pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TroyDoesAI/Codestral-RAG-19B-Pruned with Transformers:

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

pipe = pipeline("text-generation", model="TroyDoesAI/Codestral-RAG-19B-Pruned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TroyDoesAI/Codestral-RAG-19B-Pruned")
model = AutoModelForCausalLM.from_pretrained("TroyDoesAI/Codestral-RAG-19B-Pruned")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TroyDoesAI/Codestral-RAG-19B-Pruned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TroyDoesAI/Codestral-RAG-19B-Pruned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TroyDoesAI/Codestral-RAG-19B-Pruned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TroyDoesAI/Codestral-RAG-19B-Pruned

SGLang

How to use TroyDoesAI/Codestral-RAG-19B-Pruned 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 "TroyDoesAI/Codestral-RAG-19B-Pruned" \
    --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": "TroyDoesAI/Codestral-RAG-19B-Pruned",
		"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 "TroyDoesAI/Codestral-RAG-19B-Pruned" \
        --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": "TroyDoesAI/Codestral-RAG-19B-Pruned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TroyDoesAI/Codestral-RAG-19B-Pruned with Docker Model Runner:
```
docker model run hf.co/TroyDoesAI/Codestral-RAG-19B-Pruned
```

Quant with imatrix?

by cyberjunk - opened Jun 10, 2024

Discussion

cyberjunk

Jun 10, 2024

I've attempted to add imatrix and quant it to q5_K_M, I'm having a few issues at the moment with my machine so I'm just testing a direct conversion to q8_0 ,
do you think imatrix might make up for the loss at lower vram usages?

please get in touch, love your work

TroyDoesAI

Owner Jun 14, 2024

Hey, Sorry to get back so late, I havent really played with Quantization with imatrix so you are more of an expert in that department.
=)

I try to prune my models to keep the full precision and fit under 24 GB.
Thank you for your kind words, I wish I could help, maybe message someone that releases imatrix.
I am very interested in how a pruned model performs after being quantized as I feel I removed many redundant layers that might have been what makes quantized models perform ok. Please stay in touch. I am very curious and want to learn more! :D

Reach out to me:
https://www.linkedin.com/in/troyandrewschultz/

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