Instructions to use flowxai/semantic-mapper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flowxai/semantic-mapper with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="flowxai/semantic-mapper")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flowxai/semantic-mapper")
model = AutoModelForCausalLM.from_pretrained("flowxai/semantic-mapper", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use flowxai/semantic-mapper with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("flowxai/semantic-mapper")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use flowxai/semantic-mapper with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="flowxai/semantic-mapper",
	filename="gguf/semantic-mapper-4b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use flowxai/semantic-mapper 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 flowxai/semantic-mapper:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf flowxai/semantic-mapper:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf flowxai/semantic-mapper:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf flowxai/semantic-mapper: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 flowxai/semantic-mapper:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf flowxai/semantic-mapper: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 flowxai/semantic-mapper:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf flowxai/semantic-mapper:Q4_K_M

Use Docker

docker model run hf.co/flowxai/semantic-mapper:Q4_K_M

LM Studio
Jan

vLLM

How to use flowxai/semantic-mapper with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flowxai/semantic-mapper"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flowxai/semantic-mapper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/flowxai/semantic-mapper:Q4_K_M

SGLang

How to use flowxai/semantic-mapper with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "flowxai/semantic-mapper" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flowxai/semantic-mapper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "flowxai/semantic-mapper" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flowxai/semantic-mapper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use flowxai/semantic-mapper with Ollama:
```
ollama run hf.co/flowxai/semantic-mapper:Q4_K_M
```

Unsloth Studio

How to use flowxai/semantic-mapper 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 flowxai/semantic-mapper 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 flowxai/semantic-mapper to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for flowxai/semantic-mapper to start chatting

How to use flowxai/semantic-mapper with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "flowxai/semantic-mapper"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "flowxai/semantic-mapper"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use flowxai/semantic-mapper with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "flowxai/semantic-mapper"

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 flowxai/semantic-mapper

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use flowxai/semantic-mapper with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "flowxai/semantic-mapper"

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 "flowxai/semantic-mapper" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use flowxai/semantic-mapper with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "flowxai/semantic-mapper"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "flowxai/semantic-mapper"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "flowxai/semantic-mapper",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use flowxai/semantic-mapper with Docker Model Runner:
```
docker model run hf.co/flowxai/semantic-mapper:Q4_K_M
```

Lemonade

How to use flowxai/semantic-mapper with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull flowxai/semantic-mapper:Q4_K_M

Run and chat with the model

lemonade run user.semantic-mapper-Q4_K_M

List all available models

lemonade list

semantic-mapper / EVAL_RESULTS.md

bogdanraduta

Add EVAL_RESULTS.md

7975cfa verified 15 days ago

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FlowX Semantic Mapper (4B) — Evaluation Results

Held-out evaluation of the released model (flowx-semantic-mapper-4b-v2). All numbers are on a document-disjoint held-out set of 112 records spanning EN/FR/DE/RO across banking, insurance, logistics, and labor. Decoding: greedy (temperature 0), enable_thinking=False. Metrics are exact set-match precision/recall/F1 unless noted; concepts also reports a fuzzy/semantic token-match F1, and entities reports field-accuracy over actor/action/object.

Headline (held-out, n=112)

Field	Precision	Recall	F1
JSON validity			1.00
all-3-facets present			1.00
domain_tags (free-text)	0.552	0.570	0.555
concepts (controlled 252-vocab, exact)	0.547	0.558	0.541
concepts (fuzzy / semantic match)	0.596	0.590	0.578
entities (field-accuracy)			0.530

The controlled-vocabulary result (before / after)

The central design change of this release was replacing open free-text concepts (1062 unique labels, 88% seen exactly once, unlearnable and unmeasurable) with a controlled 252-concept taxonomy (concept_taxonomy.yaml), then expanding the corpus with real regulatory text.

Concepts F1	value	held-out
open free-text vocabulary (v1)	0.24 (exact) / 0.37 (fuzzy)	n=35
controlled vocab + data expansion (this release)	0.54 (exact) / 0.58 (fuzzy)	n=112

Concepts F1 more than doubled, and entities went from unmeasurable (the facet was empty in the old held-out) to 0.53, on a held-out set that is 3x larger and multilingual.

Honest reading / caveats

Home-field note. The held-out shares the annotation lineage of the training data (real regulatory text, frontier-model-assisted ontology annotation to the controlled vocab). Read these as a strong format-and-consistency signal; validate on your own corpus before production claims.
Concepts are partial (~0.54). The model surfaces many but not all applicable concepts.
domain_tags are free-text on purpose (recall + human browsing) and are honestly noisier than the controlled concepts.
English is strongest; FR/DE/RO are supported and materially improved by the real-data expansion, but remain a bit weaker than EN.

Reproduction

Held-out set: mlx_data/ontology_cv2/valid.jsonl (built document-disjoint from the training split). Scorer: eval_mapper_4b.py <model_path> mlx_data/ontology_cv2/valid.jsonl.

Author: Bogdan Răduță, Head of Research, FlowX.AI.