Instructions to use msy127/ft-240209-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use msy127/ft-240209-sft with Transformers:

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

pipe = pipeline("text-generation", model="msy127/ft-240209-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("msy127/ft-240209-sft")
model = AutoModelForCausalLM.from_pretrained("msy127/ft-240209-sft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use msy127/ft-240209-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "msy127/ft-240209-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "msy127/ft-240209-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/msy127/ft-240209-sft

SGLang

How to use msy127/ft-240209-sft 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 "msy127/ft-240209-sft" \
    --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": "msy127/ft-240209-sft",
		"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 "msy127/ft-240209-sft" \
        --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": "msy127/ft-240209-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use msy127/ft-240209-sft with Docker Model Runner:
```
docker model run hf.co/msy127/ft-240209-sft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

base_model: LDCC/LDCC-SOLAR-10.7B pipeline_tag: text-generation

msy127/ft-240209-sft

Our Team

Research & Engineering	Product Management
David Sohn	David Sohn

Model Details

Base Model

LDCC/LDCC-SOLAR-10.7B

Trained On

OS: Ubuntu 22.04
GPU: A100 40GB 1ea
transformers: v4.37

Implementation Code

This model contains the chat_template instruction format.
You can use the code below.

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

pipe = pipeline("text-generation", model="msy127/ft-240209-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("msy127/ft-240209-sft")
model = AutoModelForCausalLM.from_pretrained("msy127/ft-240209-sft")

Introduction to our service platform

AI Companion service platform that talks while looking at your face.
You can preview the future of the world's best, character.ai.
https://livetalkingai.com

Downloads last month: 5

Safetensors

Model size

11B params

Tensor type

BF16

Model tree for msy127/ft-240209-sft

Quantizations

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