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Jebadiah/Qwen-2.5-base-tron-7b
Qwen-2.5-base-tron-7b is a machine learning model from Jebadiah. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Qwen-2.5-base-tron-7b is a merge of the following models using LazyMergekit:
Downloads · 30 days
39
12% of all-time downloads
All-time downloads
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.gguf15.2 GB · 50%
From the Hugging Face model README
Qwen-2.5-base-tron-7b is a merge of the following models using LazyMergekit:
name: Qwen-2.5-base-tron-7b
merge_method: sce
parameters:
select_topk: 0.666
normalize: true
dtype: float32
out_dtype: bfloat16
base_model: Jebadiah/Qwen-2.5-base-7b
tokenizer:
source: union
special_tokens: keep_all
priority: none
add_padding_token: true
force_fast_tokenizer: true # Can help with compatibility
resolve_conflicts: append_ids # Append IDs to conflicting tokens to make them unique
models:
- model: bunnycore/Blabbertron-1.2
- model: Xiaojian9992024/Qwen2.5-7B-MS-Destroyer
- model: trollek/Qwen2.5-7B-CySecButler-v0.1
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Jebadiah/Qwen-2.5-base-tron-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"])