Instructions to use monotykamary/LFM2.5-2.6B-RLCD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monotykamary/LFM2.5-2.6B-RLCD with Transformers:

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

pipe = pipeline("text-generation", model="monotykamary/LFM2.5-2.6B-RLCD")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monotykamary/LFM2.5-2.6B-RLCD")
model = AutoModelForCausalLM.from_pretrained("monotykamary/LFM2.5-2.6B-RLCD", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use monotykamary/LFM2.5-2.6B-RLCD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monotykamary/LFM2.5-2.6B-RLCD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monotykamary/LFM2.5-2.6B-RLCD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/monotykamary/LFM2.5-2.6B-RLCD

SGLang

How to use monotykamary/LFM2.5-2.6B-RLCD 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 "monotykamary/LFM2.5-2.6B-RLCD" \
    --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": "monotykamary/LFM2.5-2.6B-RLCD",
		"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 "monotykamary/LFM2.5-2.6B-RLCD" \
        --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": "monotykamary/LFM2.5-2.6B-RLCD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use monotykamary/LFM2.5-2.6B-RLCD with Docker Model Runner:
```
docker model run hf.co/monotykamary/LFM2.5-2.6B-RLCD
```

LFM2.5-2.6B-RLCD / docs /pcd-results.md

monotykamary

feat: add verified inference-only parallel constrained decoding

3145545 verified 9 days ago

preview code

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PCD measurements and limitations

Measured 2026-09-16 on one NVIDIA L40S, FP16, PyTorch 2.14.0, Transformers 5.17.0, SDPA attention and reference PyTorch convolution. No optimized causal-conv1d, torch.compile, vLLM/SGLang or Jev service comparison.

Release decision

Experimental inference-only release, not a production-quality or calibrated Jev substitute. The latency goal is met on multi-field examples. The proposed accuracy non-inferiority goal is not met: use application-specific evaluation, an autoregressive fallback, or separately funded training before relying on it. No always-on service was deployed.

Development and fresh audit

The 12 development cases were used to debug/select prompts. Six additional cases were written after freezing the protocol and not used to retune it. Both sets are tiny, hand-authored, and not representative production data. Each method/case has one untimed warmup and three measured repetitions. Accuracy counts unique cases, not repeats.

Split	Method	Mean ms	Median ms	p95 ms	Field accuracy	Exact objects	Schema valid
development	token	56.24	54.79	69.87	88.9%	75.0%	100.0%
development	sequence	60.47	55.50	85.64	77.8%	66.7%	100.0%
development	ar_sequence	557.41	538.50	659.95	97.2%	91.7%	100.0%
development	ar_token	565.64	536.43	691.47	91.7%	83.3%	83.3%
audit	token	57.34	55.08	70.98	72.2%	33.3%	100.0%
audit	sequence	58.09	56.13	69.60	66.7%	33.3%	100.0%
audit	ar_sequence	529.36	527.42	548.59	94.4%	83.3%	100.0%
audit	ar_token	559.70	546.89	629.99	94.4%	83.3%	100.0%

ar_sequence is greedy direct-answer JSON using original values and the same sequence-mode system/schema. ar_token uses the code catalog and an all-field JSON request; codes are strictly decoded, not repaired. Token PCD asks independent per-field questions, so its suffix prompt and distribution are not identical to joint AR. All use the same model, precision and backend. Empty thinking is explicit for these direct-answer modes.

Synthetic scaling (one distinct input per row)

Case	Token PCD ms	Sequence PCD ms	AR JSON ms	AR/token ratio	Token / sequence / AR field accuracy
fields-4	53.31	52.00	748.99	14.1x	100% / 100% / 100%
fields-28	106.98	103.41	4875.30	45.6x	100% / 100% / 100%
fields-12	53.77	54.92	2099.88	39.1x	100% / 100% / 100%
enum-64	60.13	84.76	241.54	4.0x	0% / 100% / 100%
enum-255	144.99	346.77	270.86	1.9x	0% / 100% / 100%

High-cardinality warning: token mode chose the wrong answer in both the 64- and 255-category probes. Sequence mode was correct on these inputs, but the 255-category sequence scorer was slower than AR. Do not interpret the high aggregate boolean accuracy as high-cardinality accuracy. The 28-field example is literal configuration extraction, not a broad semantic understanding benchmark.

Incorrect constrained outputs (one per unique case)

Case	Method	Expected	Actual
routing-3	token	`{"route": "north west", "service": "standard", "insured": true}`	`{"route": "north west", "service": "express plus", "insured": true}`
routing-3	sequence	`{"route": "north west", "service": "standard", "insured": true}`	`{"route": "north", "service": "express", "insured": false}`
audit-3	token	`{"route": "south", "service": "express plus", "insured": false}`	`{"route": "north west", "service": "express plus", "insured": false}`
audit-3	sequence	`{"route": "south", "service": "express plus", "insured": false}`	`{"route": "north", "service": "express", "insured": false}`
audit-0	token	`{"topic": "billing", "urgent": false, "refund": false}`	`{"topic": "technical", "urgent": false, "refund": false}`
audit-2	sequence	`{"route": "north", "service": "standard", "insured": true}`	`{"route": "north", "service": "express", "insured": false}`
sentiment-2	token	`{"sentiment": "neutral", "language": "Spanish", "question": true}`	`{"sentiment": "positive", "language": "English", "question": true}`
audit-5	token	`{"sentiment": "neutral", "language": "French", "question": false}`	`{"sentiment": "positive", "language": "English", "question": false}`
sentiment-0	sequence	`{"sentiment": "positive", "language": "English", "question": false}`	`{"sentiment": "positive", "language": "English", "question": true}`
sentiment-3	token	`{"sentiment": "neutral", "language": "English", "question": false}`	`{"sentiment": "positive", "language": "English", "question": false}`
routing-0	sequence	`{"route": "north west", "service": "express plus", "insured": true}`	`{"route": "north", "service": "express plus", "insured": false}`
routing-1	sequence	`{"route": "south", "service": "standard", "insured": false}`	`{"route": "north", "service": "express", "insured": false}`
audit-1	sequence	`{"topic": "technical", "urgent": true, "refund": true}`	`{"topic": "billing", "urgent": true, "refund": true}`
audit-4	sequence	`{"sentiment": "positive", "language": "Spanish", "question": false}`	`{"sentiment": "positive", "language": "Spanish", "question": true}`
audit-4	token	`{"sentiment": "positive", "language": "Spanish", "question": false}`	`{"sentiment": "positive", "language": "English", "question": false}`
enum-64	token	`{"category": "category-057"}`	`{"category": "category-017"}`
enum-255	token	`{"category": "category-248"}`	`{"category": "category-022"}`

Correctness and resource evidence

diagnostic: validation passed; maximum candidate-score differences against uncached reference {'token': 0.015625, 'sequence': 0.05997657775878906}; peak allocated VRAM 5.07 GiB; in-function time 94.24s.
stress: validation passed; maximum candidate-score differences against uncached reference {'token': 0.015625, 'sequence': 0.05997657775878906}; peak allocated VRAM 6.12 GiB; in-function time 79.74s.
Local tests exercise a tiny FP32 hybrid model; CUDA validation also tests the actual model in FP32 and FP16. Both single-token and multi-token convolution cache paths preserve the original prefix state. Score tolerances are 0.002 in FP32 and 0.10 in FP16; raw measured errors are retained, not assumed zero.
An early BF16 hidden-state check failed (maximum observed difference 0.25 with 0.15 absolute tolerance). We did not relax that BF16 gate; FP16 is the recommended tested runtime precision. Original stored BF16 weights remain unchanged.
Candidate probabilities are explicitly uncalibrated. No ECE, Brier-score calibration, learned confidence threshold, or proprietary Jev RLCD training is claimed.
Request timings include prompt tokenization, cached-schema lookup, prefill, cache forking, all microbatches, candidate scoring, and serialization; CUDA is synchronized. They exclude model loading, network, provisioning and output evaluation. Initial schema compilation and cold starts are not benchmarked separately.
p95 estimates are descriptive with very small samples. GPU clocks/allocator/runtime effects varied between exploratory jobs; the report uses the final fixed-source runs, not the fastest observed earlier latency.
Native reasoning was sampled during development on three cases with a 256-token cap: two truncated and one emitted fenced JSON. Those are not valid completed baseline results and are not used to claim PCD speedups or native-model incompetence. We avoided paying to repeat that capped experiment.
Frugal execution: serial single-L40S jobs, cached public weights, bounded runtimes, no H100, no training and no permanent deployment. In-function seconds are not a billing statement; startup/idle/CPU/storage charges are additional.

Raw release evidence

Raw files include every measured output, score, token/branch count, version and inference-source hash. The selected prompt was not changed after the fresh audit. Earlier iterations are development artifacts and are not release performance claims.