Instructions to use Ayansk11/FinSenti-MobileLLM-R1-950M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ayansk11/FinSenti-MobileLLM-R1-950M with Transformers:

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

pipe = pipeline("text-generation", model="Ayansk11/FinSenti-MobileLLM-R1-950M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ayansk11/FinSenti-MobileLLM-R1-950M")
model = AutoModelForCausalLM.from_pretrained("Ayansk11/FinSenti-MobileLLM-R1-950M")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Ayansk11/FinSenti-MobileLLM-R1-950M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ayansk11/FinSenti-MobileLLM-R1-950M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ayansk11/FinSenti-MobileLLM-R1-950M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ayansk11/FinSenti-MobileLLM-R1-950M

SGLang

How to use Ayansk11/FinSenti-MobileLLM-R1-950M 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 "Ayansk11/FinSenti-MobileLLM-R1-950M" \
    --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": "Ayansk11/FinSenti-MobileLLM-R1-950M",
		"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 "Ayansk11/FinSenti-MobileLLM-R1-950M" \
        --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": "Ayansk11/FinSenti-MobileLLM-R1-950M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ayansk11/FinSenti-MobileLLM-R1-950M with Docker Model Runner:
```
docker model run hf.co/Ayansk11/FinSenti-MobileLLM-R1-950M
```

FinSenti-MobileLLM-R1-950M / README.md

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	---
	license: apache-2.0
	language:
	- en
	base_model: facebook/MobileLLM-R1-950M
	datasets:
	- Ayansk11/FinSenti-Dataset
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- finance
	- financial-sentiment
	- sentiment-analysis
	- chain-of-thought
	- reasoning
	- grpo
	- sft
	- lora
	- finsenti
	---
	# FinSenti-MobileLLM-R1-950M

	FinSenti-MobileLLM-R1-950M is a 0.9B-parameter model fine-tuned to
	read short financial text (headlines, earnings snippets, market commentary)
	and explain its read of them before settling on positive, negative, or
	neutral. It's Meta's purpose-built mobile model. The architecture is shaped for on-device inference (compact embeddings, untied lm_head, shared attention layers) and FinSenti's recipe lifts the financial-sentiment quality without changing that footprint.

	The model is part of the [FinSenti
	collection](https://huggingface.co/collections/Ayansk11/finsenti), a
	scaling study of small models trained on the same data with the same recipe.

	## What it's good at

	- Classifying short financial text (1-3 sentences) into positive / negative
	/ neutral
	- Producing a short reasoning chain you can read or log
	- Following a strict `<reasoning>...</reasoning><answer>...</answer>` output
	format that's easy to parse downstream

	It was trained on news-style headlines and earnings snippets in English, so
	that's where it shines. Outside that domain you'll see the format hold up
	but the labels get noisier.

	## How it was trained

	Two-stage recipe, same across the whole FinSenti family:

	1. SFT on the SFT train slice from the [FinSenti
	dataset](https://huggingface.co/datasets/Ayansk11/FinSenti-Dataset)
	(~15.2K balanced training samples, drawn from a
	50.8K-sample pool with held-out val/test splits, chain-of-thought
	targets generated by a teacher model and filtered for label agreement).
	This stage took about 1.6 hours on a single A100 80GB
	for this model.
	2. GRPO with four reward functions (sentiment correctness, format
	compliance, reasoning quality, output consistency), each weighted equally
	for a maximum reward of 4.0. The training budget was 3000
	steps with early stopping; the best checkpoint landed near step
	~200 with a mean reward of approximately
	3.29 / 4.0 on the validation slice.

	Trainer stack: PEFT + bitsandbytes (no Unsloth - llama4_text arch unsupported), using Unsloth's pre-quantized mirror
	[`facebook/MobileLLM-R1-950M`](https://huggingface.co/facebook/MobileLLM-R1-950M) as the
	loading shortcut for the upstream
	[`facebook/MobileLLM-R1-950M`](https://huggingface.co/facebook/MobileLLM-R1-950M)
	weights. LoRA adapters (r=16, alpha=32) were
	trained on the attention and MLP projection layers, then merged into the
	base weights before export, so this repo is a self-contained model and
	doesn't need PEFT to load.

	## Quick start

	Standard `transformers` usage:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "Ayansk11/FinSenti-MobileLLM-R1-950M"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id, torch_dtype=torch.bfloat16, device_map="auto"
	)

	system = (
	"You are a financial sentiment analyst. For each headline you receive, "
	"write a short reasoning chain inside <reasoning>...</reasoning> tags, "
	"then give a single label inside <answer>...</answer> tags. The label "
	"must be exactly one of: positive, negative, neutral."
	)
	user = "Apple beats Q4 estimates as iPhone sales jump 12% year over year."

	messages = [
	{"role": "system", "content": system},
	{"role": "user", "content": user},
	]
	prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

	inputs = tok(prompt, return_tensors="pt").to(model.device)
	out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
	print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
	```

	Expected output (your reasoning text will vary; the label should match):

	```
	<reasoning>
	Beating estimates is a positive earnings surprise. A 12% YoY iPhone sales jump in the company's biggest product line points to demand strength. Both signals push the read positive.
	</reasoning>
	<answer>positive</answer>
	```

	## Prompt format

	The model expects the system prompt above, verbatim is best. The user turn
	is the headline or short snippet you want classified. Output is two XML-ish
	blocks in this order: `<reasoning>...</reasoning>` then
	`<answer>...</answer>`. The `<answer>` content is one of `positive`,
	`negative`, or `neutral` (lowercase, no punctuation).

	If you want labels only and don't care about the reasoning, you can stop
	generation as soon as you see `</answer>` to save tokens.

	## Performance notes

	The training reward (max 4.0) hit 3.29 on the
	held-out validation slice. That breaks down across the four reward
	functions roughly as:

	- Sentiment correctness: dominant contributor; the model gets the label
	right on the validation split most of the time
	- Format compliance: near-saturated by the end of GRPO; the model almost
	always produces well-formed `<reasoning>` and `<answer>` tags
	- Reasoning quality: judged on length and presence of finance-relevant
	signal words; this one's the noisiest of the four
	- Consistency: rewards stable labels across paraphrases of the same headline

	Numbers on standard finance benchmarks (FPB, FiQA, Twitter Financial News)
	are forthcoming and will be added once the eval pipeline lands.

	## Hardware

	At bf16 the weights are about 1.8 GB on disk and need ~3 GB of GPU memory for batch=1 inference. CPU inference is fine too: on a modern laptop you'll get a few tokens per second with the bf16 weights, and 15-30 tok/s with the GGUF Q4_K_M build.

	## Limitations

	A few things this model isn't built for:

	- Long documents. Training context was capped at 2048
	tokens. Anything much longer than a few paragraphs is out of distribution.
	- Multi-asset reasoning. It classifies the sentiment of a single piece
	of text. It won't aggregate across multiple headlines or weigh sources.
	- Numerical reasoning. It can read "beats by 12%" and call that
	positive, but it isn't doing math. Don't ask it to forecast.
	- Languages other than English. Training data was English only.
	- Background knowledge. If the headline needs you to know what a
	company does, the model only has whatever was in its base pretraining.
	It can't look anything up.
	- Three labels, hard cutoffs. The output space is positive / negative /
	neutral. If you need a 5-class scale or a continuous score, you'll need
	to retrain or post-process.

	## Training details

	\| \| \|
	\|---\|---\|
	\| Upstream base model \| [facebook/MobileLLM-R1-950M](https://huggingface.co/facebook/MobileLLM-R1-950M) \|
	\| Loading mirror \| [facebook/MobileLLM-R1-950M](https://huggingface.co/facebook/MobileLLM-R1-950M) (Unsloth's pre-quantized copy) \|
	\| Dataset \| [Ayansk11/FinSenti-Dataset](https://huggingface.co/datasets/Ayansk11/FinSenti-Dataset) (~15.2K train per stage, 50.8K total across splits) \|
	\| SFT length \| ~1.6 hours on A100 80GB \|
	\| GRPO budget \| 3000 steps with early stopping (best near step ~200) \|
	\| Best GRPO reward \| ~3.29 / 4.0 \|
	\| Adapter \| LoRA (r=16, alpha=32) on q/k/v/o/gate/up/down projections \|
	\| Sequence length \| 2048 \|
	\| Optimizer \| AdamW (8-bit), cosine LR schedule \|
	\| Hardware \| NVIDIA A100 80GB (Indiana University BigRed200 cluster) \|
	\| Frameworks \| PEFT + bitsandbytes (no Unsloth - llama4_text arch unsupported) \|

	## Related FinSenti models

	Other sizes and bases trained with the same recipe:

	- Qwen3: [Qwen3-0.6B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-0.6B), [Qwen3-1.7B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-1.7B), [Qwen3-4B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-4B), [Qwen3-8B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-8B)
	- Qwen3.5: [Qwen3.5-0.8B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-0.8B), [Qwen3.5-2B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-2B), [Qwen3.5-4B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-4B), [Qwen3.5-9B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-9B)
	- DeepSeek: [DeepSeek-R1-1.5B](https://huggingface.co/Ayansk11/FinSenti-DeepSeek-R1-1.5B)
	- Tiny-LLM: [Tiny-LLM-10M](https://huggingface.co/Ayansk11/FinSenti-Tiny-LLM-10M)
	- Llama-3: [Llama-3.2-1B](https://huggingface.co/Ayansk11/FinSenti-Llama-3.2-1B)
	- SmolLM: [SmolLM-1.7B](https://huggingface.co/Ayansk11/FinSenti-SmolLM-1.7B)

	There's a GGUF build of this same model at
	[Ayansk11/FinSenti-MobileLLM-R1-950M-GGUF](https://huggingface.co/Ayansk11/FinSenti-MobileLLM-R1-950M-GGUF) for Ollama and
	llama.cpp, and the dataset itself is at
	[Ayansk11/FinSenti-Dataset](https://huggingface.co/datasets/Ayansk11/FinSenti-Dataset).

	If you're picking a size, a rough guide:

	- Need it on a phone or browser? Look at the smallest model in the
	group (Qwen3-0.6B) or its GGUF.
	- Laptop with no GPU? Any model up to ~2B as Q4_K_M GGUF works.
	- Single 8-12 GB GPU? The 1.5B-4B sizes are the sweet spot.
	- Server or workstation? The 8B / 9B variants give the best reasoning
	but need the memory.

	## Citation

	If you use this model in research, please cite:

	```bibtex
	@misc{shaikh2026finsenti,
	title = {FinSenti: Small Language Models for Financial Sentiment with Chain-of-Thought Reasoning},
	author = {Shaikh, Ayan},
	year = {2026},
	url = {https://huggingface.co/collections/Ayansk11/finsenti},
	note = {Indiana University}
	}
	```

	## License

	Apache 2.0, same as the base model.

	## Acknowledgements

	Trained on the Indiana University BigRed200 cluster.
	Thanks to the Unsloth and TRL teams for the trainer stack, and to the
	Qwen / DeepSeek teams for the base models.