Instructions to use AlSamCur123/OSS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AlSamCur123/OSS with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AlSamCur123/OSS", filename="gpt-oss-20b.MXFP4.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 AlSamCur123/OSS with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AlSamCur123/OSS # Run inference directly in the terminal: llama-cli -hf AlSamCur123/OSS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AlSamCur123/OSS # Run inference directly in the terminal: llama-cli -hf AlSamCur123/OSS
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 AlSamCur123/OSS # Run inference directly in the terminal: ./llama-cli -hf AlSamCur123/OSS
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 AlSamCur123/OSS # Run inference directly in the terminal: ./build/bin/llama-cli -hf AlSamCur123/OSS
Use Docker
docker model run hf.co/AlSamCur123/OSS
- LM Studio
- Jan
- Ollama
How to use AlSamCur123/OSS with Ollama:
ollama run hf.co/AlSamCur123/OSS
- Unsloth Studio new
How to use AlSamCur123/OSS 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 AlSamCur123/OSS 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 AlSamCur123/OSS to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AlSamCur123/OSS to start chatting
- Pi new
How to use AlSamCur123/OSS with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AlSamCur123/OSS
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": "AlSamCur123/OSS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AlSamCur123/OSS with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AlSamCur123/OSS
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 AlSamCur123/OSS
Run Hermes
hermes
- Docker Model Runner
How to use AlSamCur123/OSS with Docker Model Runner:
docker model run hf.co/AlSamCur123/OSS
- Lemonade
How to use AlSamCur123/OSS with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AlSamCur123/OSS
Run and chat with the model
lemonade run user.OSS-{{QUANT_TAG}}List all available models
lemonade list
Trained with Unsloth - config
Browse files- config.json +77 -0
config.json
ADDED
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{
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"architectures": [
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"GptOssForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.0,
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"eos_token_id": 200002,
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"experts_per_token": 4,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2880,
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"initial_context_length": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 2880,
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"layer_types": [
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention"
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],
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"max_position_embeddings": 131072,
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"model_type": "gpt_oss",
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"num_attention_heads": 64,
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"num_experts_per_tok": 4,
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"num_hidden_layers": 24,
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"num_key_value_heads": 8,
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"num_local_experts": 32,
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"output_router_logits": false,
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"pad_token_id": 200017,
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"quantization_config": {
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"modules_to_not_convert": [
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"model.layers.*.self_attn",
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"model.layers.*.mlp.router",
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"model.embed_tokens",
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"lm_head"
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],
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"quant_method": "mxfp4"
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},
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"beta_fast": 32.0,
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"beta_slow": 1.0,
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"factor": 32.0,
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"original_max_position_embeddings": 4096,
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"rope_type": "yarn",
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"truncate": false
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},
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"rope_theta": 150000,
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"router_aux_loss_coef": 0.9,
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"sliding_window": 128,
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"swiglu_limit": 7.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.2",
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"unsloth_fixed": true,
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"use_cache": true,
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"vocab_size": 201088
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}
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