Instructions to use techhermit/qwen35-slice14b-release with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use techhermit/qwen35-slice14b-release with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/distill_student_init10") model = PeftModel.from_pretrained(base_model, "techhermit/qwen35-slice14b-release") - llama-cpp-python
How to use techhermit/qwen35-slice14b-release with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="techhermit/qwen35-slice14b-release", filename="release_repo-q6_k.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use techhermit/qwen35-slice14b-release with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf techhermit/qwen35-slice14b-release:Q6_K # Run inference directly in the terminal: llama-cli -hf techhermit/qwen35-slice14b-release:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf techhermit/qwen35-slice14b-release:Q6_K # Run inference directly in the terminal: llama-cli -hf techhermit/qwen35-slice14b-release:Q6_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf techhermit/qwen35-slice14b-release:Q6_K # Run inference directly in the terminal: ./llama-cli -hf techhermit/qwen35-slice14b-release:Q6_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf techhermit/qwen35-slice14b-release:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf techhermit/qwen35-slice14b-release:Q6_K
Use Docker
docker model run hf.co/techhermit/qwen35-slice14b-release:Q6_K
- LM Studio
- Jan
- Ollama
How to use techhermit/qwen35-slice14b-release with Ollama:
ollama run hf.co/techhermit/qwen35-slice14b-release:Q6_K
- Unsloth Studio new
How to use techhermit/qwen35-slice14b-release with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for techhermit/qwen35-slice14b-release to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for techhermit/qwen35-slice14b-release to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for techhermit/qwen35-slice14b-release to start chatting
- Pi new
How to use techhermit/qwen35-slice14b-release with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf techhermit/qwen35-slice14b-release:Q6_K
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "techhermit/qwen35-slice14b-release:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use techhermit/qwen35-slice14b-release with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf techhermit/qwen35-slice14b-release:Q6_K
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default techhermit/qwen35-slice14b-release:Q6_K
Run Hermes
hermes
- Docker Model Runner
How to use techhermit/qwen35-slice14b-release with Docker Model Runner:
docker model run hf.co/techhermit/qwen35-slice14b-release:Q6_K
- Lemonade
How to use techhermit/qwen35-slice14b-release with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull techhermit/qwen35-slice14b-release:Q6_K
Run and chat with the model
lemonade run user.qwen35-slice14b-release-Q6_K
List all available models
lemonade list
techhermit/qwen35-slice14b-release
This repository contains the distilled adapter and optional quantized export for the sliced 14B base checkpoint.
Provenance
- Base repo:
techhermit/qwen35-slice14b-base - Base model:
Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled - Best adapter run:
external-behavior-expanded-plus-run6 - Best eval perplexity:
2.3351974012631542 - Quantized export:
release_repo-q8_0.gguf
Usage
Load the base repo first, then apply the adapter from this repo.
- Base repo:
techhermit/qwen35-slice14b-base - Quantized export:
release_repo-q8_0.gguf
For direct inference, use the GGUF export with llama.cpp. For PEFT-based loading or further training, load the base repo and apply the adapter from this repo on top of it.
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