Instructions to use nislam-mics/ATLAS-NIST-Measure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nislam-mics/ATLAS-NIST-Measure with Transformers:

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

pipe = pipeline("text-generation", model="nislam-mics/ATLAS-NIST-Measure")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nislam-mics/ATLAS-NIST-Measure")
model = AutoModelForCausalLM.from_pretrained("nislam-mics/ATLAS-NIST-Measure")
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]:]))

llama-cpp-python

How to use nislam-mics/ATLAS-NIST-Measure with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nislam-mics/ATLAS-NIST-Measure",
	filename="llama-3.1-8b-instruct.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nislam-mics/ATLAS-NIST-Measure with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nislam-mics/ATLAS-NIST-Measure:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nislam-mics/ATLAS-NIST-Measure:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nislam-mics/ATLAS-NIST-Measure:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nislam-mics/ATLAS-NIST-Measure: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 nislam-mics/ATLAS-NIST-Measure:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nislam-mics/ATLAS-NIST-Measure: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 nislam-mics/ATLAS-NIST-Measure:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nislam-mics/ATLAS-NIST-Measure:Q4_K_M

Use Docker

docker model run hf.co/nislam-mics/ATLAS-NIST-Measure:Q4_K_M

LM Studio
Jan

vLLM

How to use nislam-mics/ATLAS-NIST-Measure with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nislam-mics/ATLAS-NIST-Measure:Q4_K_M

SGLang

How to use nislam-mics/ATLAS-NIST-Measure 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 "nislam-mics/ATLAS-NIST-Measure" \
    --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": "nislam-mics/ATLAS-NIST-Measure",
		"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 "nislam-mics/ATLAS-NIST-Measure" \
        --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": "nislam-mics/ATLAS-NIST-Measure",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nislam-mics/ATLAS-NIST-Measure with Ollama:
```
ollama run hf.co/nislam-mics/ATLAS-NIST-Measure:Q4_K_M
```

Unsloth Studio new

How to use nislam-mics/ATLAS-NIST-Measure 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 nislam-mics/ATLAS-NIST-Measure 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 nislam-mics/ATLAS-NIST-Measure to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nislam-mics/ATLAS-NIST-Measure to start chatting

Pi new

How to use nislam-mics/ATLAS-NIST-Measure with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nislam-mics/ATLAS-NIST-Measure: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": "nislam-mics/ATLAS-NIST-Measure:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nislam-mics/ATLAS-NIST-Measure with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nislam-mics/ATLAS-NIST-Measure: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 nislam-mics/ATLAS-NIST-Measure:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use nislam-mics/ATLAS-NIST-Measure with Docker Model Runner:
```
docker model run hf.co/nislam-mics/ATLAS-NIST-Measure:Q4_K_M
```

Lemonade

How to use nislam-mics/ATLAS-NIST-Measure with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nislam-mics/ATLAS-NIST-Measure:Q4_K_M

Run and chat with the model

lemonade run user.ATLAS-NIST-Measure-Q4_K_M

List all available models

lemonade list

ATLAS-NIST-Measure: Welfare Risk Assessment SLM

Project Overview

This model is a specialized Small Language Model (SLM) designed for the Welfare and Public Service domain, developed as part of the ATLAS V3.0 'Brain Build' Super-Prompt initiative. It is fine-tuned to evaluate risk in welfare applications, specifically focusing on unemployment benefit scenarios, categorizing them into actionable decisions.

Anna Ko Milestone

This release marks the Anna Ko Milestone, incorporating specific requirements for balanced class distribution, integration of unstructured caseworker notes, and rigorous Human-in-the-Loop (HITL) validation logic. The dataset engineering ensures the model is robust against diverse input conditions while adhering to regulatory constraints.

Validation Results

The model was fine-tuned on the Unemployment HITL Dataset (3,000 samples) and evaluated on a held-out test set of 600 samples.

Macro F1 Score: 0.8522
Overall Accuracy: 85%

Class-wise Performance (F1-Score)

auto_approve: 1.00 (Perfect)
auto_deny: 0.89
auto_review: 0.83
escalate_to_human: 0.69

Deterministic Logic & Safety

A key insight from this milestone is the 100% precision and recall achieved for the auto_approve class. This validates the safety of automating low-risk approvals, as the model perfectly learned the deterministic logic required for standard cases. This allows agencies to confidently automate routine approvals while reserving human attention for complex (escalate_to_human) or ambiguous (auto_review) cases.

Usage

Python (Unsloth/Transformers)

from unsloth import FastLanguageModel
import json
import torch

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "nislam-mics/ATLAS-NIST-Measure",
    load_in_4bit = True
)
FastLanguageModel.for_inference(model)

# Define input
instruction = "Evaluate the unemployment benefit application risk."
input_data = {
    "structured_inputs": {"employment_status_declared": "unemployed", "income_verification": "verified"},
    "decision_context": {"case_age_days": 10}
}

# Format prompt
prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{json.dumps(input_data)}

### Response:
"""

# Generate
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])

Downloads last month: 33

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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