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

Libraries

How to use FineEnvs/LFM2.5-2.6B-opencode-RL with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FineEnvs/LFM2.5-2.6B-opencode-RL")
model = AutoModelForCausalLM.from_pretrained("FineEnvs/LFM2.5-2.6B-opencode-RL", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FineEnvs/LFM2.5-2.6B-opencode-RL with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FineEnvs/LFM2.5-2.6B-opencode-RL

SGLang

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

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

LFM2.5-2.6B-opencode-RL / README.md

AdithyaSK HF Staff

Fix disconnected evaluation lines and GIF previews

a0bcdab verified 7 days ago

preview code

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	---
	base_model: LiquidAI/LFM2.5-2.6B
	base_model_relation: finetune
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	license: other
	tags:
	- trl
	- openenv
	- harbor
	- agent
	- smoldataenvs
	- grpo
	- opencode
	datasets:
	- FineEnvs/SmolDataEnvs-harbor-train
	thumbnail: https://huggingface.co/spaces/AdithyaSK/multi-harness-rl/resolve/main/app/public/og/og-image.png
	model-index:
	- name: LFM2.5-2.6B-opencode-RL (main, step 1000)
	results:
	- task:
	type: text-generation
	name: Agentic data analysis
	dataset:
	type: FineEnvs/SmolDataEnvs-harbor-test
	name: SmolDataEnvs (250 fixed test tasks)
	split: test
	args:
	harness: all four
	metrics:
	- type: pass_at_1
	name: Pass@1 (%) — Overall
	value: 52.3
	- task:
	type: text-generation
	name: Agentic data analysis
	dataset:
	type: FineEnvs/SmolDataEnvs-harbor-test
	name: SmolDataEnvs (250 fixed test tasks)
	split: test
	args:
	harness: opencode
	metrics:
	- type: pass_at_1
	name: Pass@1 (%) — OpenCode
	value: 58.0
	- task:
	type: text-generation
	name: Agentic data analysis
	dataset:
	type: FineEnvs/SmolDataEnvs-harbor-test
	name: SmolDataEnvs (250 fixed test tasks)
	split: test
	args:
	harness: claude-code
	metrics:
	- type: pass_at_1
	name: Pass@1 (%) — Claude Code
	value: 42.0
	- task:
	type: text-generation
	name: Agentic data analysis
	dataset:
	type: FineEnvs/SmolDataEnvs-harbor-test
	name: SmolDataEnvs (250 fixed test tasks)
	split: test
	args:
	harness: codex
	metrics:
	- type: pass_at_1
	name: Pass@1 (%) — Codex
	value: 43.2
	- task:
	type: text-generation
	name: Agentic data analysis
	dataset:
	type: FineEnvs/SmolDataEnvs-harbor-test
	name: SmolDataEnvs (250 fixed test tasks)
	split: test
	args:
	harness: mini-swe-agent
	metrics:
	- type: pass_at_1
	name: Pass@1 (%) — Mini-SWE-Agent
	value: 66.0
	license_name: lfm1.0
	license_link: LICENSE
	---

	[![The ultimate guide to multi-harness RL](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl/resolve/main/app/public/og/og-image.png)](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)

	# LFM2.5-2.6B-opencode-RL

	A full fine-tune of [LiquidAI/LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) for agentic data-analysis tasks, trained with asynchronous GRPO (TRL Async GRPO) using OpenCode. This release is step 1,000 on `main`, scoring 52.3% pass@1 across four evaluation harnesses.

	[Article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl) · [Collection](https://huggingface.co/collections/FineEnvs/multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Evaluation tasks](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-test) · [Training dashboard](https://huggingface.co/spaces/FineEnvs/data-agent-training-comparison-trackio)

	## Training and evaluation curves

	[![Animated training, pass@1 and tool-use curves through step 1,000](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-RL/resolve/main/assets/curves.gif?v=2)](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)

	Training curves use a trailing 50-step mean (at least 10 observations). Evaluation markers show measured checkpoints; hollow markers and dotted segments indicate incomplete coverage. Faint background lines show the full recorded trajectory; colored lines reveal the measured checkpoints. The first frame shows the completed chart before replaying, so previews also contain the full curves. Tool-call savings compare tasks solved by both the base model and checkpoint, with a different matched cohort at each checkpoint. The animation stops at this revision's step 1,000.

	[Static chart](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-RL/resolve/main/assets/curves.png) · [Plotted data](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-RL/resolve/main/assets/curves.json) · [Interactive article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)

	## Training

	TRL Async GRPO with correctness plus a tool-call efficiency bonus, initialized from the base model. All training rollouts use OpenCode. Rollouts run through OpenEnv × Harbor in E2B sandboxes.

	\| Setting \| Value \|
	\|---\|---\|
	\| Training task pool \| 1,000 tasks: 400 medium / 600 hard \|
	\| Checkpoint \| 1,000 optimizer steps \|
	\| Learning rate \| 3e-6 \|
	\| Rollouts per GRPO group / maximum staleness \| 8 / 4 optimizer steps \|
	\| Optimizer / precision \| paged AdamW 8-bit / bfloat16 \|
	\| Training harnesses \| OpenCode \|
	\| Sampling temperature / top-p \| 0.8 / 1.0 \|
	\| Per-call output budget, training / evaluation \| 4,096 / 4,096 tokens \|

	The reward is `correctness × (1 + 0.1 × 15 / (15 + tool_calls))`, using verified native harness action counts. Incorrect answers receive zero; missing or unverified action counts receive no efficiency bonus. Reported pass@1 measures correctness only. The 1,000-task pool is not a claim that every task contributed an optimizer update.

	## Evaluation

	250 fixed SmolDataEnvs test tasks (33 easy, 118 medium, 99 hard), each evaluated under four harnesses: 1,000 graded task/harness cells. Pass@1 uses the first graded attempt per cell; infrastructure retries do not turn it into pass@k. All cells below are graded.

	\| Harness \| Correct / evaluated \| Pass@1 \|
	\|---\|---:\|---:\|
	\| OpenCode \| 145 / 250 \| 58.0% \|
	\| Claude Code \| 105 / 250 \| 42.0% \|
	\| Codex \| 108 / 250 \| 43.2% \|
	\| Mini-SWE-Agent \| 165 / 250 \| 66.0% \|
	\| Overall \| 523 / 1,000 \| 52.3% \|

	Harness versions: OpenCode 1.18.31, Claude Code 2.1.270, Codex 0.154.0, Mini-SWE-Agent 2.4.6. Full scores, including difficulty breakdowns, are in [eval_results.json](eval_results.json).

	The [main branch](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-RL/tree/main) contains the final step-1,000 checkpoint (52.3%). The [step-900 branch](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-RL/tree/step-900) contains the best observed checkpoint (52.4%), used in the article's SFT-versus-RL comparison. Best checkpoint selection used this test set, not a separate validation set.

	These are single-run results on a specific task set and harness versions. Harness mix, training exposure and compute differ across runs; the scores do not isolate a causal effect of the harness or objective.

	## Load the checkpoint

	The repository contains full saved model weights and the saved tokenizer/chat template, not a LoRA adapter. Training used Transformers 5.14.1; use a compatible Transformers release.

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

	model_id = "FineEnvs/LFM2.5-2.6B-opencode-RL"
	revision = "main"
	tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
	model = AutoModelForCausalLM.from_pretrained(
	model_id, revision=revision, dtype=torch.bfloat16, device_map="auto"
	)
	```

	Reproducing the task scores requires the agent harness and tools described in the [article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl); a plain chat prompt is not the same evaluation.

	## Related releases

	- [LFM2.5-2.6B-multiharness-RL](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL)
	- [LFM2.5-2.6B-opencode-RL](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-RL)
	- [LFM2.5-2.6B-multiharness-SFT](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-SFT)
	- [LFM2.5-2.6B-opencode-SFT](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-SFT)
	- [Qwen3.5-2B-multiharness-RL](https://huggingface.co/FineEnvs/Qwen3.5-2B-multiharness-RL)
	- [Qwen3.5-2B-opencode-RL](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-RL)
	- [Qwen3.5-2B-opencode-standalone-RL](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL)

	All models, datasets, environments and the article are linked in the [multi-harness RL collection](https://huggingface.co/collections/FineEnvs/multi-harness-rl-6abdfaaa8d74dacd481d5212).

	## License and provenance

	Derived from [LiquidAI/LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) under the LFM Open License v1.0. The base model's license is included unchanged in [LICENSE](LICENSE). FineEnvs modified the weights by reinforcement learning; this is not an official Liquid AI release. See [NOTICE](NOTICE) and [release_manifest.json](release_manifest.json) for modification notices, the pinned base revision, checkpoint identity and file checksums. Optimizer, scheduler, RNG and trainer state are excluded.

	## Citation

	For the experiment, methodology and interpretation, cite the [main article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl):

	```bibtex
	@misc{kolavi2026multiharnessrl,
	author = {Adithya S Kolavi},
	title = {The ultimate guide to multi-harness RL},
	year = {2026},
	url = {https://huggingface.co/spaces/AdithyaSK/multi-harness-rl}
	}
	```