Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
jdqwoi/TooManyMixRolePlay-7B-Story
jdqwoi/02
text-generation-inference
Instructions to use jdqwoi/TooManyMixRolePlay-7B-Story_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdqwoi/TooManyMixRolePlay-7B-Story_V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jdqwoi/TooManyMixRolePlay-7B-Story_V1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jdqwoi/TooManyMixRolePlay-7B-Story_V1") model = AutoModelForCausalLM.from_pretrained("jdqwoi/TooManyMixRolePlay-7B-Story_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jdqwoi/TooManyMixRolePlay-7B-Story_V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jdqwoi/TooManyMixRolePlay-7B-Story_V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jdqwoi/TooManyMixRolePlay-7B-Story_V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jdqwoi/TooManyMixRolePlay-7B-Story_V1
- SGLang
How to use jdqwoi/TooManyMixRolePlay-7B-Story_V1 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 "jdqwoi/TooManyMixRolePlay-7B-Story_V1" \ --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": "jdqwoi/TooManyMixRolePlay-7B-Story_V1", "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 "jdqwoi/TooManyMixRolePlay-7B-Story_V1" \ --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": "jdqwoi/TooManyMixRolePlay-7B-Story_V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jdqwoi/TooManyMixRolePlay-7B-Story_V1 with Docker Model Runner:
docker model run hf.co/jdqwoi/TooManyMixRolePlay-7B-Story_V1
TooManyMixRolePlay-7B-Story_V1
TooManyMixRolePlay-7B-Story_V1 is a merge of the following models using LazyMergekit:
EXL2 quants of jdqwoi/TooManyMixRolePlay-7B-Story_V1 by kim512
- 4.00 bits per weight
- 5.00 bits per weight
- 6.00 bits per weight
- 7.00 bits per weight
- 8.00 bits per weight
๐งฉ Configuration
slices:
- sources:
- model: jdqwoi/TooManyMixRolePlay-7B-Story
layer_range: [0, 32]
- model: jdqwoi/02
layer_range: [0, 32]
merge_method: slerp
base_model: jdqwoi/TooManyMixRolePlay-7B-Story
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: 0.5
dtype: bfloat16
๐ป Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jdqwoi/TooManyMixRolePlay-7B-Story_V1"
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"])
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