Text Generation
Transformers
Safetensors
qwen2
conversational
text-generation-inference
4-bit precision
gptq
Instructions to use wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1") model = AutoModelForCausalLM.from_pretrained("wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1", device_map="auto") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-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": "wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1
- SGLang
How to use wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-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 "wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-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": "wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-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 "wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-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": "wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1 with Docker Model Runner:
docker model run hf.co/wolfofbackstreet/Qwen2.5-14B-Instruct-abliterated-SFT-int4-qptq-v1
Upload tokenizer
Browse files- special_tokens_map.json +7 -1
- tokenizer_config.json +3 -4
special_tokens_map.json
CHANGED
|
@@ -21,5 +21,11 @@
|
|
| 21 |
"rstrip": false,
|
| 22 |
"single_word": false
|
| 23 |
},
|
| 24 |
-
"pad_token":
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 25 |
}
|
|
|
|
| 21 |
"rstrip": false,
|
| 22 |
"single_word": false
|
| 23 |
},
|
| 24 |
+
"pad_token": {
|
| 25 |
+
"content": "<|vision_pad|>",
|
| 26 |
+
"lstrip": false,
|
| 27 |
+
"normalized": false,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
}
|
| 31 |
}
|
tokenizer_config.json
CHANGED
|
@@ -207,7 +207,6 @@
|
|
| 207 |
"pad_token_type_id": 0,
|
| 208 |
"padding_side": "left",
|
| 209 |
"split_special_tokens": false,
|
| 210 |
-
"tokenizer_class": "
|
| 211 |
-
"unk_token": null
|
| 212 |
-
|
| 213 |
-
}
|
|
|
|
| 207 |
"pad_token_type_id": 0,
|
| 208 |
"padding_side": "left",
|
| 209 |
"split_special_tokens": false,
|
| 210 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 211 |
+
"unk_token": null
|
| 212 |
+
}
|
|
|