Instructions to use burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1 with Transformers:

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

pipe = pipeline("text-generation", model="burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1")
model = AutoModelForCausalLM.from_pretrained("burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1", 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 burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1

SGLang

How to use burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1 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 "burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1" \
    --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": "burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1",
		"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 "burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1" \
        --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": "burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1 with Docker Model Runner:
```
docker model run hf.co/burtenshaw/openenv-echo-world-model-liquidai-lfm2.5-350m-seed1
```

openenv-echo-world-model-liquidai-lfm2.5-350m-seed1 / README.md

burtenshaw HF Staff

openenv echo repeat 20260622T095034Z-lfm25-350m seed 1

a5e63f6 verified 2 months ago

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metadata

license: other
license_name: lfm1.0
base_model: LiquidAI/LFM2.5-350M
library_name: transformers
tags:
  - openenv
  - echo
  - world-model
  - verifier-free

OpenEnv ECHO World Model

This checkpoint was trained with examples/echo_world_model/train_echo.py to predict OpenEnv terminal environment outputs from verifier-free ECHO loss.

Training Metrics

metric	value
`best_step`	`10`
`heldout_ce_after`	`0.41339555382728577`
`heldout_ce_before`	`13.741455078125`
`heldout_ce_delta`	`-13.328059524297714`
`heldout_ce_improvement_pct`	`96.9916173252615`
`heldout_token_acc_after`	`0.8571428571428571`
`heldout_token_acc_before`	`0.0`
`lr`	`5e-05`
`model`	`LiquidAI/LFM2.5-350M`
`seed`	`1`
`steps`	`60`