Instructions to use techhermit/qwen35-slice14b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use techhermit/qwen35-slice14b-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="techhermit/qwen35-slice14b-base") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("techhermit/qwen35-slice14b-base") model = AutoModelForCausalLM.from_pretrained("techhermit/qwen35-slice14b-base") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use techhermit/qwen35-slice14b-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "techhermit/qwen35-slice14b-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "techhermit/qwen35-slice14b-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/techhermit/qwen35-slice14b-base
- SGLang
How to use techhermit/qwen35-slice14b-base 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 "techhermit/qwen35-slice14b-base" \ --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": "techhermit/qwen35-slice14b-base", "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 "techhermit/qwen35-slice14b-base" \ --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": "techhermit/qwen35-slice14b-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use techhermit/qwen35-slice14b-base with Docker Model Runner:
docker model run hf.co/techhermit/qwen35-slice14b-base
| DEPRECATED: The legacy builder is deprecated and will be removed in a future release. | |
| Install the buildx component to build images with BuildKit: | |
| https://docs.docker.com/go/buildx/ | |
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| Installing collected packages: xxhash, shellingham, sentencepiece, safetensors, regex, python-dateutil, pyarrow, propcache, multidict, mdurl, hf-xet, h11, frozenlist, dill, click, anyio, annotated-doc, aiohappyeyeballs, yarl, pandas, multiprocess, markdown-it-py, httpcore, aiosignal, rich, httpx, aiohttp, typer, bitsandbytes, huggingface_hub, tokenizers, datasets, accelerate, transformers, peft | |
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| [91mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning. | |
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| Successfully tagged automop-distill-student:latest | |
| ========== | |
| == CUDA == | |
| ========== | |
| CUDA Version 12.4.1 | |
| Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| This container image and its contents are governed by the NVIDIA Deep Learning Container License. | |
| By pulling and using the container, you accept the terms and conditions of this license: | |
| https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license | |
| A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience. | |
| Fetching 19 files: 0%| | 0/19 [00:00<?, ?it/s] Fetching 19 files: 100%|ββββββββββ| 19/19 [00:00<00:00, 6133.91it/s] | |
| The fast path is not available because one of the required library is not installed. Falling back to torch implementation. To install follow https://github.com/fla-org/flash-linear-attention#installation and https://github.com/Dao-AILab/causal-conv1d | |
| Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_interleaved', 'mrope_section'} | |
| Writing model shards: 0%| | 0/6 [00:00<?, ?it/s] Writing model shards: 17%|ββ | 1/6 [00:32<02:40, 32.04s/it] Writing model shards: 33%|ββββ | 2/6 [01:08<02:17, 34.41s/it] Writing model shards: 50%|βββββ | 3/6 [01:42<01:43, 34.42s/it] Writing model shards: 67%|βββββββ | 4/6 [02:19<01:11, 35.56s/it] Writing model shards: 83%|βββββββββ | 5/6 [02:57<00:36, 36.34s/it] Writing model shards: 100%|ββββββββββ| 6/6 [03:31<00:00, 35.45s/it] Writing model shards: 100%|ββββββββββ| 6/6 [03:32<00:00, 35.40s/it] | |
| The tokenizer you are loading from '/workspace/.cache/huggingface/hub/models--Jackrong--Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled/snapshots/9094770e48788ba501437d8e9ecd84a17bf91ce1' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. | |
| Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_interleaved', 'mrope_section'} | |
| { | |
| "source_model": "/workspace/.cache/huggingface/hub/models--Jackrong--Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled/snapshots/9094770e48788ba501437d8e9ecd84a17bf91ce1", | |
| "output_dir": "/workspace/distill_student_init10", | |
| "source_layers": 64, | |
| "target_layers": 32, | |
| "selected_layers": [ | |
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| ], | |
| "max_shard_size_gb": 5.0 | |
| } | |