Instructions to use Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1") model = AutoModelForMultimodalLM.from_pretrained("Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1
- SGLang
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-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 "Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 with Docker Model Runner:
docker model run hf.co/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1
Upload 4 files
Browse files- added_tokens.json +28 -0
- config.json +30 -0
- generation_config.json +13 -0
- merges.txt +0 -0
added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "4.51.3"
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}
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merges.txt
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