Text Generation
GGUF
llama-cpp
decision-model
system-one
typed-decisions
uncensored
abliterated
calibrated-probabilities
noul
choice
score
qwen3.8
jev
solar-jev
quantized
conversational
Instructions to use SargeDev/Solar-JEV-27B-R32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use SargeDev/Solar-JEV-27B-R32-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
Use Docker
docker model run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use SargeDev/Solar-JEV-27B-R32-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SargeDev/Solar-JEV-27B-R32-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SargeDev/Solar-JEV-27B-R32-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
- Ollama
How to use SargeDev/Solar-JEV-27B-R32-GGUF with Ollama:
ollama run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use SargeDev/Solar-JEV-27B-R32-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use SargeDev/Solar-JEV-27B-R32-GGUF with Docker Model Runner:
docker model run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
- Lemonade
How to use SargeDev/Solar-JEV-27B-R32-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Solar-JEV-27B-R32-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use SargeDev/Solar-JEV-27B-R32-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
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 SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use SargeDev/Solar-JEV-27B-R32-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 1,315 Bytes
fd8f975 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | {
"bias": [
0.00011960109259234741,
-0.00011981795250903815,
-0.001826660125516355,
-0.0008995002135634422,
1.2772755326295737e-05,
0.001714941579848528,
0.0009451315272599459,
-0.0020803064107894897,
-0.00017852929886430502,
-0.00010998119250871241,
-0.00011865551641676575,
7.452104182448238e-05,
-8.196464477805421e-05,
8.958959369920194e-06,
-0.002271531615406275,
-0.0069303084164857864,
-0.0064850919879972935,
-0.004779351409524679,
-0.002676677191630006,
-0.0025728552136570215,
-0.002822346054017544,
-0.002843661466613412,
-0.0031860454473644495,
-0.002381357364356518
],
"verbalizer_ids": [
3721,
1802,
15,
16,
17,
18,
19,
20,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47
],
"slots": {
"num_slots": 24,
"ranges": {
"noul": [
0,
2
],
"score": [
2,
8
],
"choice": [
8,
24
]
},
"verbalizers": [
"false",
"true",
"0",
"1",
"2",
"3",
"4",
"5",
"A",
"B",
"C",
"D",
"E",
"F",
"G",
"H",
"I",
"J",
"K",
"L",
"M",
"N",
"O",
"P"
],
"template_version": "bare-v1"
},
"note": "decision logits = lm_head-LoRA logprobs of verbalizer ids + bias, then per-kind temperature"
} |