Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 15
How to use bruhzair/prototype-0.4x193 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="bruhzair/prototype-0.4x193")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bruhzair/prototype-0.4x193")
model = AutoModelForCausalLM.from_pretrained("bruhzair/prototype-0.4x193")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use bruhzair/prototype-0.4x193 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bruhzair/prototype-0.4x193"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bruhzair/prototype-0.4x193",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/bruhzair/prototype-0.4x193
How to use bruhzair/prototype-0.4x193 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "bruhzair/prototype-0.4x193" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bruhzair/prototype-0.4x193",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "bruhzair/prototype-0.4x193" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bruhzair/prototype-0.4x193",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use bruhzair/prototype-0.4x193 with Docker Model Runner:
docker model run hf.co/bruhzair/prototype-0.4x193
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-70B-v1/snapshots/d46ef2629f1c3cd46789a55793c5ff0af60de3e8
- model: /workspace/cache/models--Sao10K--L3-70B-Euryale-v2.1/snapshots/36ad832b771cd783ea7ad00ed39e61f679b1a7c6
- model: /workspace/cache/models--Sao10K--L3.3-70B-Euryale-v2.3/snapshots/e5737724a37ae00926e95acf663ca73d430dc8ad
base_model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
merge_method: model_stock
tokenizer:
source: base
int8_mask: true
dtype: float32
out_dtype: bfloat16