Instructions to use venky1/employee-microslm-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use venky1/employee-microslm-int8 with Transformers:

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

pipe = pipeline("text-generation", model="venky1/employee-microslm-int8", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("venky1/employee-microslm-int8", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use venky1/employee-microslm-int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "venky1/employee-microslm-int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "venky1/employee-microslm-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/venky1/employee-microslm-int8

SGLang

How to use venky1/employee-microslm-int8 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 "venky1/employee-microslm-int8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "venky1/employee-microslm-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "venky1/employee-microslm-int8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "venky1/employee-microslm-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use venky1/employee-microslm-int8 with Docker Model Runner:
```
docker model run hf.co/venky1/employee-microslm-int8
```

Employee Micro-SLM (15.3M - In-Context Document QA & Summarizer)

A custom 15.3-million parameter decoder-only Transformer built from scratch and trained for In-Context Reading Comprehension and Summarization on arbitrary employee data (JSON, XML, Key-Value text).

Highlights

In-Context Learning: Accepts any arbitrary employee data (JSON / XML / text) in the prompt and extracts facts or generates summaries.
Byte-Level BPE Tokenizer: 2,960 vocabulary items with 100% byte-fallback (0% UNK on any Unicode character or syntax).
Ultra Compact: 15.3M parameters (~29.2 MB FP16 / ~14.6 MB INT8).
Zero Hallucination: Answers and summaries are grounded strictly in the provided input document.
Fast CPU & GPU Inference: Optimized Rotary Position Embeddings (RoPE), SwiGLU MLP, and RMSNorm.

Architecture

Hidden Size: 384
Layers: 6
Attention Heads: 6
FFN Dimension: 1,536 (SwiGLU)
Vocabulary Size: 2,960
Max Sequence Length: 512
Parameters: 15,297,408

How to Use

import json
from tokenizers import Tokenizer
from transformers import AutoModelForCausalLM
from huggingface_hub import hf_hub_download
import torch

repo_id = "venky1/employee-microslm-int8"

tok = Tokenizer.from_file(hf_hub_download(repo_id, "tokenizer.json"))
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
model.eval()

def analyze_employee(data, task, max_new_tokens=64):
    context_str = json.dumps(data, indent=2) if isinstance(data, dict) else str(data).strip()
    prompt = f"<bos>### Context:\n{context_str}\n\n### Task:\n{task}\n\n### Answer:\n"
    tokens = tok.encode(prompt).ids
    inp = torch.tensor([tokens])
    out = model.generate(input_ids=inp, max_new_tokens=max_new_tokens, do_sample=False)
    return tok.decode(out[0][len(tokens):].tolist()).replace("##", "").strip()

# 1. Provide ANY JSON
sample_json = {
    "employee_id": "EMP-9481",
    "name": "Siddharth Verma",
    "department": "Cyber Security",
    "role": "Security Architect",
    "hours_worked": "8 hours and 45 minutes",
    "project": "Zero Trust Firewall",
    "worklog": "Audited firewall rules and patched SSL certificate vulnerability."
}

# Ask questions or request summaries
print(analyze_employee(sample_json, "What is the designation of Siddharth?"))
print(analyze_employee(sample_json, "Summarize this employee's workday."))

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