Instructions to use pulseimi/parser-v2-qwen25-7b 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 pulseimi/parser-v2-qwen25-7b 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 pulseimi/parser-v2-qwen25-7b:Q8_0 # Run inference directly in the terminal: llama cli -hf pulseimi/parser-v2-qwen25-7b:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf pulseimi/parser-v2-qwen25-7b:Q8_0 # Run inference directly in the terminal: llama cli -hf pulseimi/parser-v2-qwen25-7b:Q8_0
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 pulseimi/parser-v2-qwen25-7b:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf pulseimi/parser-v2-qwen25-7b:Q8_0
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 pulseimi/parser-v2-qwen25-7b:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf pulseimi/parser-v2-qwen25-7b:Q8_0
Use Docker
docker model run hf.co/pulseimi/parser-v2-qwen25-7b:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use pulseimi/parser-v2-qwen25-7b with Ollama:
ollama run hf.co/pulseimi/parser-v2-qwen25-7b:Q8_0
- Unsloth Desktop
- Pi
How to use pulseimi/parser-v2-qwen25-7b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pulseimi/parser-v2-qwen25-7b:Q8_0
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": "pulseimi/parser-v2-qwen25-7b:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use pulseimi/parser-v2-qwen25-7b with Docker Model Runner:
docker model run hf.co/pulseimi/parser-v2-qwen25-7b:Q8_0
- Lemonade
How to use pulseimi/parser-v2-qwen25-7b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pulseimi/parser-v2-qwen25-7b:Q8_0
Run and chat with the model
lemonade run user.parser-v2-qwen25-7b-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use pulseimi/parser-v2-qwen25-7b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pulseimi/parser-v2-qwen25-7b:Q8_0
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 pulseimi/parser-v2-qwen25-7b:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use pulseimi/parser-v2-qwen25-7b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf pulseimi/parser-v2-qwen25-7b:Q8_0
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 "pulseimi/parser-v2-qwen25-7b:Q8_0" \ --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: 2,255 Bytes
b7c9121 | 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 | import json, re, sys, urllib.request
SYSTEM = json.loads(open(sys.argv[1]).readline())['messages'][0]['content']
GOLD = [json.loads(l) for l in open(sys.argv[2])]
PORT = sys.argv[3]
TAG = sys.argv[4]
def ask(question):
payload = {
"model": "parser",
"messages": [
{"role": "system", "content": SYSTEM},
{"role": "user", "content": question},
],
"temperature": 0.0,
"max_tokens": 600,
}
req = urllib.request.Request(
f"http://localhost:{PORT}/v1/chat/completions",
data=json.dumps(payload).encode(),
headers={"Content-Type": "application/json"},
)
with urllib.request.urlopen(req, timeout=60) as r:
body = json.loads(r.read())
return body["choices"][0]["message"]["content"]
route_correct = 0
endpoint_correct = 0
json_valid = 0
n = len(GOLD)
rows = []
for i, g in enumerate(GOLD):
# Match production/training format: route is GIVEN, model builds the DAG.
tagged_q = f"[ROUTE: {g['expected_route']}] {g['question']}"
try:
raw = ask(tagged_q)
m = re.search(r"\{.*\}", raw, re.DOTALL)
parsed = json.loads(m.group(0)) if m else None
json_valid += 1 if parsed else 0
got_route = parsed.get("route") if parsed else None
got_endpoints = sorted(c.get("endpoint", "") for c in parsed.get("calls", [])) if parsed else []
exp_endpoints = sorted(g.get("expected_endpoints", []))
r_ok = got_route == g["expected_route"]
e_ok = got_endpoints == exp_endpoints
route_correct += r_ok
endpoint_correct += e_ok
rows.append({"q": g["question"], "expected_route": g["expected_route"], "got_route": got_route, "route_ok": r_ok, "endpoint_ok": e_ok, "got_endpoints": got_endpoints, "exp_endpoints": exp_endpoints})
except Exception as ex:
rows.append({"q": g["question"], "error": str(ex)})
if (i + 1) % 20 == 0:
print(f"[{TAG}] {i+1}/{n}", file=sys.stderr)
print(json.dumps({
"tag": TAG,
"n": n,
"json_valid_pct": round(100 * json_valid / n, 1),
"route_accuracy_pct": round(100 * route_correct / n, 1),
"endpoint_exact_match_pct": round(100 * endpoint_correct / n, 1),
"rows": rows,
}, indent=2))
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