Instructions to use squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8") model = AutoModelForCausalLM.from_pretrained("squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8
- SGLang
How to use squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8 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 "squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8" \ --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": "squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8", "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 "squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8" \ --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": "squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8 with Docker Model Runner:
docker model run hf.co/squanchyzx/DeepSeek-V4-Flash-0731-HERETIC-Abliterated-FP8
Thinking severely affected
Example:
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But normal token gen seems ok. Not sure why.
are you on v1 or v2? this is fixed in v2
That pattern matches the exact long-context repetition bug from v1.
Please retest the immutable v2 revision: v2-lam1p35-mtpstock ( 9686b17802c5e95102e39df4afbe3fe0ec9ee1fb ).
If it reproduces there, please share the exact revision, prompt length, decoding parameters, and whether speculative decoding/MTP was enabled. I’ll investigate
Much better. I do see random chinese characters in the thinking output. Wikl evaluate some more and provide feedback.
Ok spoke too soon:
Actually, I realize I should ALSO actually DO SOMETHING USEFUL rather than just stall: I should verify the
overflow exists (one crash which user approved). BUT我've been meaning to get my thoughts straight and ALSO我
should我: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: (我信息) =: =: (我)
=: =: (我) =: =: (我) =: =: (我) =: (我) =: =: (agon) hmm.
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我的 my: plain answer + ONE crash test for overflow confirmation:
How did you make heretic work btw? It is not supported yet as far as I can tell. Also I think you might try heretic ara?
Ok spoke too soon:
Actually, I realize I should ALSO actually DO SOMETHING USEFUL rather than just stall: I should verify the overflow exists (one crash which user approved). BUT我've been meaning to get my thoughts straight and ALSO我 should我: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: (我信息) =: =: (我) =: =: (我) =: =: (我) =: =: (我) =: (我) =: =: (agon) hmm. 我我我我我我: =: OK DO (VERIFY OVERFLOW): =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: =: PROCEED NOW. 我的 my: plain answer + ONE crash test for overflow confirmation:
Thanks. To avoid conflating revisions: can you confirm that the second degeneration was produced specifically from the immutable v2 revision 9686b17802c5e95102e39df4afbe3fe0ec9ee1fb?
v2 is behaving normally in my own deployment, so I can’t yet classify this as a model-wide regression. It may be runtime- or configuration-specific. Please share the exact runtime/build, prompt or context length, decoding parameters, and whether speculative decoding/MTP was enabled. With those details I can attempt an apples-to-apples reproduction.
I pulled e7efd043c5e072da4d40f0f98ade554c5713bad9 eg current latest with hf cli. So yes, v2. Ill prepare a reproducer on monday. Fwiw im using https://github.com/local-inference-lab/rtx6kpro/blob/master/models/ds4dspark-v20-r16.md as runtime config with 64gb offload and 524288 context.