Not So Good
Collection
Models merged/fine-tuned But not so viable β’ 2 items β’ Updated
How to use fhai50032/CatLake-7B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="fhai50032/CatLake-7B") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("fhai50032/CatLake-7B")
model = AutoModelForCausalLM.from_pretrained("fhai50032/CatLake-7B")How to use fhai50032/CatLake-7B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fhai50032/CatLake-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "fhai50032/CatLake-7B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/fhai50032/CatLake-7B
How to use fhai50032/CatLake-7B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "fhai50032/CatLake-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": "fhai50032/CatLake-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 "fhai50032/CatLake-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": "fhai50032/CatLake-7B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use fhai50032/CatLake-7B with Docker Model Runner:
docker model run hf.co/fhai50032/CatLake-7B
CatLake-7B is a merge of the following models using LazyMergekit:
I Would say in my current testing RolePlayLake is Better than CatLake , anyways catPPT is contaminated(i think so) slerping with catppt doesn't enchance its RP ability
I would try to only merge Uncensored Models with Baising towards Chat rather than Instruct mainly for my medicinal use case and exploring Generative AI
slices:
- sources:
- model: fhai50032/RolePlayLake-7B
layer_range: [0, 32]
- model: rishiraj/CatPPT-base
layer_range: [0, 32]
merge_method: slerp
base_model: fhai50032/RolePlayLake-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.69 #increased fallback for rest tensors
dtype: float16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "fhai50032/CatLake-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])