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willopcbeta/Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f16_1-MLC
Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f16_1-MLC is a machine learning model from willopcbeta. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlc-llm.
This is the Ministral-3-3B-Instruct-2512-Llamafied-TextOnly model in MLC format q4f161. The model can be used for projects MLC-LLM and WebLLM. WASM: Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f161-ctx4kcs1k-web…
Downloads · 30 days
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From the Hugging Face model README
This is the Ministral-3-3B-Instruct-2512-Llamafied-TextOnly model in MLC format q4f16_1.
The model can be used for projects MLC-LLM and WebLLM.
WASM:
Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f16_1-ctx4k_cs1k-webgpu.wasm (Test available)
Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f16_1-cs1k-webgpu.wasm(Untested)
Here are some examples of using this model in MLC LLM. Before running the examples, please install MLC LLM by following the installation documentation.
In command line, run
mlc_llm chat HF://willopcbeta/Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f16_1-MLC
In command line, run
mlc_llm serve HF://willopcbeta/Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f16_1-MLC
from mlc_llm import MLCEngine
# Create engine
model = "HF://willopcbeta/Ministral-3-3B-Instruct-2512-Llamafied-TextOnly-q4f16_1-MLC"
engine = MLCEngine(model)
# Run chat completion in OpenAI API.
for response in engine.chat.completions.create(
messages=[{"role": "user", "content": "What is the meaning of life?"}],
model=model,
stream=True,
):
for choice in response.choices:
print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
For more information on MLC LLM project, please visit our documentation and GitHub repo.