Instructions to use NeshVerse/Flash-financial-analysis-lfm-1.2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeshVerse/Flash-financial-analysis-lfm-1.2b with Transformers:

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

pipe = pipeline("text-generation", model="NeshVerse/Flash-financial-analysis-lfm-1.2b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeshVerse/Flash-financial-analysis-lfm-1.2b")
model = AutoModelForCausalLM.from_pretrained("NeshVerse/Flash-financial-analysis-lfm-1.2b")
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 NeshVerse/Flash-financial-analysis-lfm-1.2b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NeshVerse/Flash-financial-analysis-lfm-1.2b",
	filename="flash-financial-analysis-q8_0-v2.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 NeshVerse/Flash-financial-analysis-lfm-1.2b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf NeshVerse/Flash-financial-analysis-lfm-1.2b: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 NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf NeshVerse/Flash-financial-analysis-lfm-1.2b: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 NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0

Use Docker

docker model run hf.co/NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0

LM Studio
Jan

vLLM

How to use NeshVerse/Flash-financial-analysis-lfm-1.2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeshVerse/Flash-financial-analysis-lfm-1.2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeshVerse/Flash-financial-analysis-lfm-1.2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0

SGLang

How to use NeshVerse/Flash-financial-analysis-lfm-1.2b 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 "NeshVerse/Flash-financial-analysis-lfm-1.2b" \
    --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": "NeshVerse/Flash-financial-analysis-lfm-1.2b",
		"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 "NeshVerse/Flash-financial-analysis-lfm-1.2b" \
        --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": "NeshVerse/Flash-financial-analysis-lfm-1.2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use NeshVerse/Flash-financial-analysis-lfm-1.2b with Ollama:
```
ollama run hf.co/NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0
```

Unsloth Studio new

How to use NeshVerse/Flash-financial-analysis-lfm-1.2b 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 NeshVerse/Flash-financial-analysis-lfm-1.2b 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 NeshVerse/Flash-financial-analysis-lfm-1.2b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NeshVerse/Flash-financial-analysis-lfm-1.2b to start chatting

Pi new

How to use NeshVerse/Flash-financial-analysis-lfm-1.2b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0

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": "NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NeshVerse/Flash-financial-analysis-lfm-1.2b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NeshVerse/Flash-financial-analysis-lfm-1.2b: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 NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use NeshVerse/Flash-financial-analysis-lfm-1.2b with Docker Model Runner:
```
docker model run hf.co/NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0
```

Lemonade

How to use NeshVerse/Flash-financial-analysis-lfm-1.2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NeshVerse/Flash-financial-analysis-lfm-1.2b:Q8_0

Run and chat with the model

lemonade run user.Flash-financial-analysis-lfm-1.2b-Q8_0

List all available models

lemonade list

⚡ Flash-Financial-Analysis-LFM-1.2B

Lightning-fast financial intelligence for structured data analysis

A blazing-fast, customized, lightweight language model optimized for real-time sales & stock analytics, inventory insights, and financial reporting based on the LiquidAI 1.2B base model supervised fine-tuned FP16 model.

Rag Model Github Repo:

https://github.com/neshverse/Flash-RAG-web-GUI/tree/main

Space (Real-time Testing)

https://huggingface.co/spaces/NeshVerse/Flash-financial-analysis-lfm-1.2b

Note: If you get any error while chatting, just refresh the page.

Model Details

Attribute	Value
Base Architecture	LiquidAI/LFM2.5-1.2B-Base
Fine-tuning	LoRA (r=4, alpha=8)
Context Window	1,024 tokens
Precision	FP16
Parameters	1.2B base + ~500K LoRA

Training Summary

Total Samples: 39,435 (37,463 train / 1,972 validation)
Training Duration: 2.4 hours
Final Loss: 0.497 (train) / 0.508 (validation)
Hardware: Consumer GPU (T4)

Capabilities

Sales Analytics: Real-time sales data querying and analysis
Stock Analytics: Inventory levels, turnover rates, stock movement
Financial Reporting: Automated report generation from structured data
Inventory Insights: Product performance, seasonal trends, demand forecasting

Performance

Inference Speed: ~0.55 it/s (T4 GPU)
Memory Usage: ~6GB (4-bit loaded)
Batch Size: 4 (effective 8 with grad accum)
Max Sequence: 1,024 tokens

Limitations

Optimized for structured financial/sales data queries
Context window limited to 1,024 tokens
Training data from 2022-2023; may not reflect current market conditions
Best performance on English language inputs

Model Files

File	Format	Size	Description	Use Case
`model.safetensors`	FP16	~2.4 GB	Original full precision	Maximum quality, GPU inference
`flash-financial-analysis-q8_0.gguf`	Q8_0	~1.2 GB	8-bit quantized (llama.cpp)	CPU inference, Ollama, LM Studio

Quantized Version (Q8_0)

We now provide a Q8_0 quantized version for easier deployment:

Format: GGUF (llama.cpp compatible)
Size: ~50% smaller than FP16 (1.2 GB vs 2.4 GB)
Quality: ~99.9% of original performance
Tools: Works with llama.cpp, Ollama, LM Studio, llama-cpp-python

Download Q8_0

# Using huggingface-cli
huggingface-cli download NeshVerse/Flash-financial-analysis-lfm-1.2b flash-financial-analysis-q8_0.gguf

# Or direct download
wget https://huggingface.co/NeshVerse/Flash-financial-analysis-lfm-1.2b/resolve/main/flash-financial-analysis-q8_0.gguf

## Quick Start

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "NeshVerse/Flash-financial-analysis-lfm-1.2b",
    max_seq_length=1024,
    load_in_4bit=True,
    trust_remote_code=True,
)

Downloads last month: 81

Safetensors

Model size

1B params

Tensor type

F16

Model tree for NeshVerse/Flash-financial-analysis-lfm-1.2b

Base model

LiquidAI/LFM2.5-1.2B-Base

Adapter

(4)

this model

Adapters

1 model

NeshVerse
/

Flash-financial-analysis-lfm-1.2b