output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)Model_Stock_Mixv0.1-7B
Model_Stock_Mixv0.1-7B is a merge of the following models using LazyMergekit:
- Static GGUF quants made with AutoGGUF
- Imatrix quant done manually, imatrix.dat provided.
🧩 Configuration
models:
- model: MTSAIR/multi_verse_model
- model: jsfs11/MoEv4Config-TestWeightedTIES-7b
- model: automerger/Experiment27Pastiche-7B
- model: Gille/StrangeMerges_32-7B-slerp
- model: automerger/YamshadowExperiment28-7B
- model: liminerity/M7-7b
merge_method: model_stock
base_model: jsfs11/MoEv4Config-TestWeightedTIES-7b
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/Model_Stock_Mixv0.1-7B"
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"])
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jsfs11/Model_Stock_Mixv0.1-7B-GGUF", filename="", )