FuseChat: Knowledge Fusion of Chat Models
Paper • 2408.07990 • Published • 15
How to use bruhzair/prototype-0.4x328 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="bruhzair/prototype-0.4x328")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("bruhzair/prototype-0.4x328")
model = AutoModelForCausalLM.from_pretrained("bruhzair/prototype-0.4x328", device_map="auto")
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-0.4x328 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bruhzair/prototype-0.4x328"
# 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-0.4x328",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/bruhzair/prototype-0.4x328
How to use bruhzair/prototype-0.4x328 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "bruhzair/prototype-0.4x328" \
--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-0.4x328",
"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-0.4x328" \
--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-0.4x328",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use bruhzair/prototype-0.4x328 with Docker Model Runner:
docker model run hf.co/bruhzair/prototype-0.4x328
This is a merge of pre-trained language models created using mergekit.
This model was merged using the SCE merge method using /workspace/cache/models--deepcogito--cogito-v2-preview-llama-70B/snapshots/1e1d12e8eaebd6084a8dcf45ecdeaa2f4b8879ce as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: /workspace/prototype-0.4x310
- model: /workspace/prototype-0.4x312
- model: /workspace/prototype-0.4x323
- model: /workspace/cache/models--bruhzair--prototype-0.4x301/snapshots/ba244ff497fd1a57c7954269c360a0f96e89f7a4
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-70B-v1/snapshots/d46ef2629f1c3cd46789a55793c5ff0af60de3e8
base_model: /workspace/cache/models--deepcogito--cogito-v2-preview-llama-70B/snapshots/1e1d12e8eaebd6084a8dcf45ecdeaa2f4b8879ce
select_topk: 0.25
merge_method: sce
tokenizer:
source: base
chat_template: llama3
pad_to_multiple_of: 8
int8_mask: true
dtype: float32
out_dtype: bfloat16