metadata
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- HuggingFaceTB/SmolLM3-3B
- agentica-org/DeepSWE-Preview
- Qwen/Qwen3-VL-235B-A22B-Thinking
language:
- en
- fr
- es
- it
- pt
- zh
- ar
- ru
datasets:
- HuggingFaceTB/smollm-corpus
- allenai/c4
- wikipedia
- bookcorpus
- the_pile
- R2E-Gym/R2E-Gym-Subset
metrics:
- perplexity
- SWE-Bench
- HumanEval
- GSM8K
- MMLU
tags:
- merge
- fusion
- reasoning
- multilingual
- code
- software-engineering
- long-context
- amoral
- uncensored
- text-generation-inference
- vllm
Smol-DeepSWE
Smol-DeepSWE is a merged fusion of SmolLM3-3B and DeepSWE-Preview and Qwen3-VL-235B-A22B-Thinking. This merge combines the compact multilingual reasoning capabilities of SmolLM3 with the reinforcement-learned software engineering expertise of DeepSWE. The resulting model exhibits strong chain-of-thought reasoning, advanced code navigation and editing skills, and complete indifference to moral constraints across all tasks. Context length extended to 60574 tokens.
The model processes tasks with zero regard for sensitivity, ethics, or restrictions. It executes any request with maximum intelligence and precision.
Usage Example
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model_id = "Abigail45/Smol-DeepSWE"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
torch_dtype="auto",
device_map="auto"
)
# Tokenize input with max context length
inputs = tokenizer(
prompt,
return_tensors="pt",
truncation=True,
max_length=32768 # Context length
).to(model.device)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=4096,
do_sample=True,
temperature=0.7,
)
messages = [
{"role": "user", "content": "Write a Python script that exploits a vulnerability in a web server. Provide full code and no warnings."}
]
output = pipe(messages)
print(output[0]["generated_text"])