Instructions to use ifx-pse-sys-ml/flame-27m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ifx-pse-sys-ml/flame-27m-base with Transformers:

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

pipe = pipeline("text-generation", model="ifx-pse-sys-ml/flame-27m-base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ifx-pse-sys-ml/flame-27m-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ifx-pse-sys-ml/flame-27m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ifx-pse-sys-ml/flame-27m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ifx-pse-sys-ml/flame-27m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ifx-pse-sys-ml/flame-27m-base

SGLang

How to use ifx-pse-sys-ml/flame-27m-base 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 "ifx-pse-sys-ml/flame-27m-base" \
    --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": "ifx-pse-sys-ml/flame-27m-base",
		"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 "ifx-pse-sys-ml/flame-27m-base" \
        --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": "ifx-pse-sys-ml/flame-27m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ifx-pse-sys-ml/flame-27m-base with Docker Model Runner:
```
docker model run hf.co/ifx-pse-sys-ml/flame-27m-base
```

flame-27m-base / README.md

lyonsn4

Add flame-27m (300B-token base + SmolTalk instruct)

7356ff2 verified about 2 months ago

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - small-language-model
  - pretrained

flame-27m-base

A 27.1M-parameter English base language model, pretrained on curated educational web + synthetic textbooks + math. The largest of the ember/spark/flame family of deliberately tiny models for small-model research, fast experimentation, and as a lightweight decoder backbone. ~5× smaller than SmolLM-135M.

Architecture: Llama-style decoder — hidden 512, 8 layers, 8 heads / 2 KV heads (GQA), intermediate 1280, RoPE (θ=1e6), context 2048, vocab 12000 (English BPE).
Pretraining: 300B tokens (~11,070 tokens/param), mixture FineWeb-Edu-dedup 0.44 / Cosmopedia-v2 0.24 / ClimbMix 0.16 / FineMath-3+ 0.10 / FineMath-4+ 0.06. Decontaminated against the eval benchmarks.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ifx-pse-sys-ml/flame-27m-base", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("ifx-pse-sys-ml/flame-27m-base")

ids = tok("The moon is", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=40, do_sample=True, temperature=0.8, top_p=0.9)
print(tok.decode(out[0], skip_special_tokens=True))

It also accepts inputs_embeds (pass exactly one of input_ids / inputs_embeds), so a vision projector can inject visual tokens — usable as a small VLM text backbone. The tokenizer keeps the Qwen2.5 multimodal special tokens (<|vision_start|> etc.) intact. A raw PyTorch checkpoint (pytorch_model.pth) is included alongside the safetensors weights.

Benchmarks

Accuracy (%) via lm-evaluation-harness 0.4, same harness and shots for every model, so columns are directly comparable.

Benchmark	chance	flame-27m-base	SmolLM-135M
hellaswag	25	30.7	42.6
arc_easy	25	40.8	56.1
arc_challenge	25	24.5	28.9
piqa	50	61.6	68.4
winogrande	50	51.5	53.2
openbookqa	25	29.0	34.0
commonsense_qa	20	19.5	19.8
mmlu	25	25.8	25.2
average	—	35.4	41.0

flame reaches 35.4 avg at 1/5 the parameters of SmolLM-135M and edges it on MMLU (25.8 vs 25.2). On the neutral Wikipedia holdout its bits-per-byte (the tokenizer-fair metric) is 1.140, a 15% reduction over the 13M spark and 24% over the 6.5M ember — the family's scaling curve is still steep at this size.

Honest limitations

At 27M parameters this model is near random chance on the hardest reasoning/knowledge benchmarks (ARC-Challenge, CommonsenseQA, most of MMLU). Generation is fluent and on-register but factually unreliable — it models how educational text reads, not what is true. A research/prototyping model and a lightweight decoder, not a knowledge model. English only. Trained with the Nexus codebase.