Instructions to use SpeciesFileGroup/ento-model-parse 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 SpeciesFileGroup/ento-model-parse 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 SpeciesFileGroup/ento-model-parse:Q4_K_M # Run inference directly in the terminal: llama cli -hf SpeciesFileGroup/ento-model-parse:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SpeciesFileGroup/ento-model-parse:Q4_K_M # Run inference directly in the terminal: llama cli -hf SpeciesFileGroup/ento-model-parse:Q4_K_M
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 SpeciesFileGroup/ento-model-parse:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf SpeciesFileGroup/ento-model-parse:Q4_K_M
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 SpeciesFileGroup/ento-model-parse:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf SpeciesFileGroup/ento-model-parse:Q4_K_M
Use Docker
docker model run hf.co/SpeciesFileGroup/ento-model-parse:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use SpeciesFileGroup/ento-model-parse with Ollama:
ollama run hf.co/SpeciesFileGroup/ento-model-parse:Q4_K_M
- Unsloth Studio
How to use SpeciesFileGroup/ento-model-parse 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 SpeciesFileGroup/ento-model-parse 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 SpeciesFileGroup/ento-model-parse to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SpeciesFileGroup/ento-model-parse to start chatting
- Pi
How to use SpeciesFileGroup/ento-model-parse with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SpeciesFileGroup/ento-model-parse:Q4_K_M
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": "SpeciesFileGroup/ento-model-parse:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use SpeciesFileGroup/ento-model-parse with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SpeciesFileGroup/ento-model-parse: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 SpeciesFileGroup/ento-model-parse:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use SpeciesFileGroup/ento-model-parse with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SpeciesFileGroup/ento-model-parse: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 "SpeciesFileGroup/ento-model-parse: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"
- Docker Model Runner
How to use SpeciesFileGroup/ento-model-parse with Docker Model Runner:
docker model run hf.co/SpeciesFileGroup/ento-model-parse:Q4_K_M
- Lemonade
How to use SpeciesFileGroup/ento-model-parse with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SpeciesFileGroup/ento-model-parse:Q4_K_M
Run and chat with the model
lemonade run user.ento-model-parse-Q4_K_M
List all available models
lemonade list
File size: 7,726 Bytes
dbd6f57 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | package main
import (
"bytes"
"encoding/json"
"fmt"
"io"
"log"
"net/http"
"strings"
)
const ollamaURL = "http://localhost:11434/api/chat"
const model = "ento-label-parser"
type ollamaRequest struct {
Model string `json:"model"`
Stream bool `json:"stream"`
Messages []ollamaMessage `json:"messages"`
}
type ollamaMessage struct {
Role string `json:"role"`
Content string `json:"content"`
}
type ollamaResponse struct {
Message struct {
Content string `json:"content"`
} `json:"message"`
}
type labelResult struct {
Verbatim string `json:"verbatim"`
Parsed json.RawMessage `json:"parsed,omitempty"`
Error string `json:"error,omitempty"`
}
func parseLabel(label string) labelResult {
result := labelResult{Verbatim: label}
reqBody, _ := json.Marshal(ollamaRequest{
Model: model,
Stream: false,
Messages: []ollamaMessage{
{Role: "user", Content: label},
},
})
resp, err := http.Post(ollamaURL, "application/json", bytes.NewReader(reqBody))
if err != nil {
result.Error = fmt.Sprintf("failed to call ollama: %v", err)
return result
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
result.Error = fmt.Sprintf("failed to read response: %v", err)
return result
}
var ollamaResp ollamaResponse
if err := json.Unmarshal(body, &ollamaResp); err != nil {
result.Error = fmt.Sprintf("failed to parse ollama response: %v", err)
return result
}
content := strings.TrimSpace(ollamaResp.Message.Content)
var parsed json.RawMessage
if err := json.Unmarshal([]byte(content), &parsed); err != nil {
result.Error = fmt.Sprintf("model returned invalid JSON: %v\nraw content: %s", err, content)
return result
}
result.Parsed = parsed
return result
}
func handleParse(w http.ResponseWriter, r *http.Request) {
if r.Method != http.MethodPost {
http.Error(w, "method not allowed", http.StatusMethodNotAllowed)
return
}
if err := r.ParseForm(); err != nil {
http.Error(w, "bad request", http.StatusBadRequest)
return
}
raw := r.FormValue("labels")
lines := strings.Split(raw, "\n")
var results []labelResult
for _, line := range lines {
line = strings.TrimSpace(line)
if line == "" {
continue
}
results = append(results, parseLabel(line))
}
w.Header().Set("Content-Type", "application/json")
enc := json.NewEncoder(w)
enc.SetIndent("", " ")
enc.Encode(results)
}
const indexHTML = `<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Label Parser</title>
<style>
body { font-family: sans-serif; max-width: 900px; margin: 2rem auto; padding: 0 1rem; background: #f5f5f5; }
h1 { color: #333; }
textarea { width: 100%; height: 180px; font-family: monospace; font-size: 14px; padding: 0.5rem; box-sizing: border-box; border: 1px solid #ccc; border-radius: 4px; }
button { margin-top: 0.75rem; padding: 0.5rem 1.5rem; font-size: 15px; background: #2563eb; color: white; border: none; border-radius: 4px; cursor: pointer; }
button:hover { background: #1d4ed8; }
button:disabled { background: #93c5fd; cursor: default; }
#progress { margin-top: 0.75rem; font-size: 13px; color: #555; min-height: 1.2em; }
#meta { margin-top: 0.75rem; display: none; background: #fff; border: 1px solid #ddd; border-radius: 6px; padding: 0.6rem 1rem; font-size: 13px; }
#meta table { border-collapse: collapse; }
#meta td { padding: 2px 1.2rem 2px 0; }
#meta td:first-child { color: #888; }
#meta td.ok { color: #16a34a; font-weight: bold; }
#meta td.fail { color: #dc2626; font-weight: bold; }
#output { margin-top: 1rem; background: #1e1e1e; color: #d4d4d4; padding: 1rem; border-radius: 6px; font-family: monospace; font-size: 13px; white-space: pre-wrap; word-break: break-all; min-height: 3rem; }
label { font-weight: bold; display: block; margin-bottom: 0.4rem; }
p.hint { color: #666; font-size: 13px; margin-top: 0.25rem; }
</style>
</head>
<body>
<h1>Entomology Label Parser</h1>
<form id="form">
<label for="labels">Labels (one per line):</label>
<textarea id="labels" name="labels" placeholder="Kazakhstan, Akmola Region: Kokshetau Mountains near Terisakkan River, 23.VI-12.VIII.1957, Emeljanov"></textarea>
<p class="hint">Each non-empty line is sent separately to the model.</p>
<button type="submit" id="btn">Parse</button>
</form>
<div id="progress"></div>
<div id="meta"></div>
<div id="output">Results will appear here.</div>
<script>
function fmt(ms) {
if (ms < 1000) return ms.toFixed(0) + ' ms';
return (ms / 1000).toFixed(2) + ' s';
}
function avg(arr) {
return arr.length ? arr.reduce((a,b) => a+b, 0) / arr.length : null;
}
function renderMeta(total, successes, failures, okTimes, failTimes, totalMs, done) {
const meta = document.getElementById('meta');
meta.style.display = 'block';
const avgOk = avg(okTimes);
const avgFail = avg(failTimes);
meta.innerHTML =
'<table>' +
'<tr><td>labels</td><td>' + total + '</td></tr>' +
'<tr><td>successes</td><td class="ok">' + successes + '</td></tr>' +
'<tr><td>failures</td><td class="' + (failures ? 'fail' : 'ok') + '">' + failures + '</td></tr>' +
(avgOk !== null ? '<tr><td>avg time (success)</td><td>' + fmt(avgOk) + '</td></tr>' : '') +
(avgFail !== null ? '<tr><td>avg time (failure)</td><td>' + fmt(avgFail) + '</td></tr>' : '') +
(done ? '<tr><td>total time</td><td>' + fmt(totalMs) + '</td></tr>' : '') +
'</table>';
}
document.getElementById('form').addEventListener('submit', async e => {
e.preventDefault();
const btn = document.getElementById('btn');
const out = document.getElementById('output');
const prog = document.getElementById('progress');
const meta = document.getElementById('meta');
btn.disabled = true;
out.textContent = '';
prog.textContent = '';
meta.style.display = 'none';
const lines = document.getElementById('labels').value
.split('\n').map(l => l.trim()).filter(l => l !== '');
const total = lines.length;
if (total === 0) {
prog.textContent = 'No labels entered.';
btn.disabled = false;
return;
}
const results = [];
const okTimes = [], failTimes = [];
let successes = 0, failures = 0;
const globalStart = performance.now();
try {
for (let i = 0; i < total; i++) {
prog.textContent = 'Parsing ' + (i + 1) + ' of ' + total + '\u2026';
const fd = new URLSearchParams();
fd.append('labels', lines[i]);
const t0 = performance.now();
const resp = await fetch('/parse', { method: 'POST', body: fd });
const json = await resp.json();
const elapsed = performance.now() - t0;
for (const r of json) {
results.push(r);
if (r.error) { failures++; failTimes.push(elapsed); }
else { successes++; okTimes.push(elapsed); }
}
out.textContent = JSON.stringify(results, null, 2);
renderMeta(total, successes, failures, okTimes, failTimes, performance.now() - globalStart, false);
}
prog.textContent = 'Done \u2014 ' + total + ' label' + (total !== 1 ? 's' : '') + ' parsed.';
renderMeta(total, successes, failures, okTimes, failTimes, performance.now() - globalStart, true);
} catch (err) {
prog.textContent = 'Error: ' + err;
} finally {
btn.disabled = false;
}
});
</script>
</body>
</html>`
func handleIndex(w http.ResponseWriter, r *http.Request) {
w.Header().Set("Content-Type", "text/html; charset=utf-8")
fmt.Fprint(w, indexHTML)
}
func main() {
http.HandleFunc("/", handleIndex)
http.HandleFunc("/parse", handleParse)
log.Println("listening on http://localhost:8080")
log.Fatal(http.ListenAndServe(":8080", nil))
}
|