Medical Series
Collection
3 items • Updated
How to use LeroyDyer/Mixtral_BioMedical_7b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="LeroyDyer/Mixtral_BioMedical_7b") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_BioMedical_7b")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_BioMedical_7b", device_map="auto")How to use LeroyDyer/Mixtral_BioMedical_7b with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0 # Run inference directly in the terminal: llama cli -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0 # Run inference directly in the terminal: llama cli -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf LeroyDyer/Mixtral_BioMedical_7b:Q8_0
docker model run hf.co/LeroyDyer/Mixtral_BioMedical_7b:Q8_0
How to use LeroyDyer/Mixtral_BioMedical_7b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/Mixtral_BioMedical_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "LeroyDyer/Mixtral_BioMedical_7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/LeroyDyer/Mixtral_BioMedical_7b:Q8_0
How to use LeroyDyer/Mixtral_BioMedical_7b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "LeroyDyer/Mixtral_BioMedical_7b" \
--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/Mixtral_BioMedical_7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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/Mixtral_BioMedical_7b" \
--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/Mixtral_BioMedical_7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use LeroyDyer/Mixtral_BioMedical_7b with Ollama:
ollama run hf.co/LeroyDyer/Mixtral_BioMedical_7b:Q8_0
How to use LeroyDyer/Mixtral_BioMedical_7b with Docker Model Runner:
docker model run hf.co/LeroyDyer/Mixtral_BioMedical_7b:Q8_0
How to use LeroyDyer/Mixtral_BioMedical_7b with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LeroyDyer/Mixtral_BioMedical_7b:Q8_0
lemonade run user.Mixtral_BioMedical_7b-Q8_0
lemonade list
was able to finally get a peice of the biomistral merged due to Nitral AI!
This is a merge of pre-trained language models created using mergekit.
This model was merged using the linear merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: LeroyDyer/Mixtral_AI
parameters:
weight: 0.789
- model: Nitral-AI/ProdigyXBioMistral_7B
parameters:
weight: 0.2312
merge_method: linear
dtype: float16
8-bit